Browse Topic: Mining vehicles and equipment

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Topology Optimization of Lightweight for Frame Structure of Deep-Sea Mining Vehicle2026-99-01307/31/2026
In order to improve the self-sufficiency rate of key mineral resources in China, it is necessary to develop and research deep-sea mining vehicles to improve the mining capacity of seabed mineral resources. The deep-sea mining vehicle is a heavy-duty underwater robot, and its main frame structure, as a critical component, must be designed to be lightweight to improve payload capacity and mining efficiency. This paper first conducted static analysis for the initial main frame structure. Finite element analysis results indicate that the initial structure fails to meet the strength requirements for lifting and recovery operations. The power index penalty factor was introduced into the topology optimization, which was based on the variable density method. The topology optimization objective was set to minimize structural compliance, with the maximum element stress and volume fraction used as constraints. The optimization process finally obtained the optimal material distribution. According to the results of topology optimization and space requirements of the installed equipment on the deep-sea mining vehicle, the new frame structure of the mining vehicle was re-established in the secondary modelling. According to the results of the analysis, the weight of the frame structure was reduced by 2.9%, and at the same time, the maximum stress was reduced by 57.2%, the maximum displacement was reduced by 47.2%, and the first-order natural frequency was increased by 54%. The strength and stiffness of the frame structure were greatly improved.
Tao, Yichun, Yang, Mingyu
Simulation Research on Non-Destructive Testing of Pipeline Based on Ultrasonic Guided Wave2026-99-01437/31/2026
Ultrasonic guided waves enable long-range, low-intrusion inspection of pipelines. This study examines how array topology and axial spacing influence the quality of defect echoes when the longitudinal axisymmetric mode L(0,2) is used. We build COMSOL finite-element models of a steel pipe and excite it with PZT-4 at 80 kHz; three practical layouts are compared: (i) odd–even receiving, (ii) 8-transmit/8-receive, and (iii) 16-transmit/8-receive, arranged as two axially separated groups. The spacing between the groups is chosen to suppress parasitic modes such as L(0,1) and to strengthen L(0,2). Results show that the two-group configuration sharpens the defect echo and reduces modal interference; increasing the number of transmitters further raises the defect-wave amplitude and improves the separation from end-reflection echoes. Among the schemes, 8×8 performs well for small-defect identification, while 16×8 yields the clearest boundaries and fastest defect indication. These findings clarify how sensor number and placement govern modal purity and sensitivity, and they offer practical guidance for designing guided-wave arrays that improve the reliability of long-range pipeline inspection. - Ultrasonic guided waves Pipeline non-destructive testing L(0,2) mode; Sensor array layout; Finite element simulation; Guided wave signal processing.
Liao, Wei, Li, Tengfei, Zhang, Wenhui, Lin, Qingming, Guo, Yanbing
Biomimetic Design and Numerical Analysis of a VIV Suppression Fairing for Offshore Risers2026-99-16407/24/2026
As oil and gas exploitation advances into deep seas, risers linking offshore platforms and subsea extraction systems endure long-term complex marine loads. Fatigue damage from Vortex-Induced Vibration (VIV) has become a key factor limiting the safe operation of deep-sea engineering structures. To address this issue, a bionic adaptive rotating fairing, which is adjustable to ocean current directions, was designed. Its main components include buoyancy blocks, a fairing with spiral guide rails on the inner wall, and clamps, which work together to reduce VIV by regulating flow patterns. Numerical simulations of concave and convex fairings showed that under subcritical flow, shifting from a concave to convex cross-section gradually enhances the fairing’s drag and lift reduction effects on risers, with a steady improvement trend. Further comparisons were made between 0.25D convex fairings, 0.35D convex fairings, and bare risers, focusing on drag/lift reduction, vortex shedding frequency, and Strouhal number. Both convex fairings exhibited similar VIV suppression performance to the bare riser, but differed significantly in the percentage reduction of vortex shedding frequency and Strouhal number. Thus, the 0.25D convex fairing was identified as the optimal configuration for VIV suppression among the concave-convex fairings studied.
Zhang, Xu, Song, Guangming, Wang, Baozhong, Zhao, Jinpeng, Chen, Qianshuo
Advanced Predictive Modeling of Pipeline Settlement in Rectangular Pipe Jacking Tunneling using Hybrid Deep Learning Techniques: A PSO-LSTM-Self Attention Approach2026-99-16487/24/2026
Nowadays, as computer technology makes quick progress, innovative algorithms like deep learning are getting used more and more in underground engineering and lots of other fields. When working on rectangular pipe jacking tunnel projects, accurately predicting the magnitude of pipeline settlement is really key to keeping the work moving smoothly. But traditional ground settlement prediction methods mainly rely on empirical formulas and numerical simulation software. When applied to tunnels with complex geometries, though, these methods usually don’t work as well as needed. To fix this problem, our study came up with a new model called PSO-LSTM-Self-Attention Mechanism (shortened to PSO-LSTM-SAM), specifically designed to predict pipeline settlement caused by rectangular pipe jacking work. This model takes the data collected from construction monitoring and uses that as the input for time series modeling work. That allows for in-depth analysis of real-time settlement data, and as a result, it can make more precise predictions of long-term pipeline settlement. To verify the effectiveness of the PSO-LSTM-SAM algorithm, the researchers compared its prediction results with those from a conventional LSTM network, an LSTM-SAM network, and a PSO-SVR network. They also checked the model’s performance by looking at pipeline settlement predictions from different monitoring points, using data from the Changsha Railway Transit Line 6 project. The results show that the PSO-LSTM model, with the self-attention mechanism added in, greatly boosts how accurate tunnel settlement predictions are, and the model fits the data better, too. This proves that the PSO-LSTM-SAM model works well: by using the strengths of deep learning, it offers a new way to predict pipeline settlement when building rectangular pipe jacking tunnels.
Chen, Yi, Weng, Xiaoxuan, Zhang, Hong, Li, Yongsuo, Hu, Da
IN FUEL CONTROL26TOFHP06_026/1/2026
How to ensure off-highway combustion systems operate with sufficient control to meet tightening emissions standards and evolving fuel landscapes without sacrificing reliability. Off-highway equipment is being asked to do more with less. Less margin for emissions, less tolerance for downtime and less room for inefficiency, while operating under some of the most demanding duty cycles in the transport sector. Tier 4 and Tier 5 emissions standards have reshaped engine calibration strategies. Renewable diesel and biodiesel blends are entering worksites and farms at scale. At the same time, construction, mining and agricultural machines are expected to run for 20-25 years, often at sustained high load and far from service infrastructure. In this environment, combustion systems are far from being phased out.
Anderson, Todd
Research on Fault Diagnosis Agent of Monorail Crane Based on Knowledge Graph and Lightweight Large Model2026-99-07415/15/2026
The monorail crane is important in mining operations, and its operation affects both safety and efficiency. Currently, fault diagnosis for monorail cranes has several challenges, such as heterogeneous mixing of multimodal data, poor use of knowledge, low real-time requirements, and high deployment costs for large-scale models. To solve these problems, we present an agent framework using a multimodal knowledge graph and a lightweight large model. In particular, we construct a fault knowledge graph for monorail cranes, organizing professional knowledge about components, failure modes, symptoms, and maintenance. By employing retrieval-augmented generation (RAG) technology, the knowledge graph is merged with the Qwen lightweight large model (low-rank adaptation) for fine-tuning to develop a diagnostic agent with task planning, tool invocation and memory. The experimental results show that the agent framework reduces “machine hallucination” and outperforms conventional diagnostic accuracy, response speed and resource efficiency, thus offering a safe and efficient solution for intelligent operation and maintenance of mining equipment.
Zhang, Yixuan, Xue, Shun, Bi, Xiang, Wei, Xing, Kang, Ranyu, Jue, Jie, Cheng, Liruiran
Research on Parameterization Method and System Application of Mining Supports Based on Modularization2026-99-07155/15/2026
The design process of mining supports is often complicated due to their intricate structure and numerous dimensional dependencies, leading to a cumbersome modeling process and low design efficiency. To address these challenges, this paper introduces a parametric design system for mining supports built on the SolidWorks platform. The system integrates modular design concepts, module-matching principles, dimension-driven techniques, and API development. By adopting a modular assembly modeling approach, the system offers an efficient solution for managing the dimensional relationships between the various components of mining supports. Additionally, the system supports adaptive processing of 2D engineering drawings, facilitating the rapid design and manufacturing of mining supports. Engineering case studies demonstrate that this system enhances the design efficiency of mining supports by over 90%, significantly shortening the product development cycle, ensuring product quality, and strengthening the company’s market competitiveness. Furthermore, the proposed design system serves as a valuable reference for the parametric design of other types of mining supports.
Rui, Lichao, Song, Jiahao, Yang, Zhiqing, Li, Helong, Ding, Lijian
Energy Management Strategy for Hybrid Mining Trucks Based on Global SOC Planning and Neural Network Optimal Control2026-01-04464/7/2026
Hybrid mining trucks, as core equipment for mine transportation, face high energy consumption and significant fluctuations in power demand during cyclic operations due to prolonged exposure to demanding operating conditions characterized by heavy loads and variable working conditions. To address the issues of high energy consumption and significant fluctuations in power demand during the cyclic operation of mining trucks, this paper proposes a hybrid mining truck energy management strategy based on global SOC (State of Charge) planning and neural network optimization control. First, a powertrain model was developed for a typical operating cycle of a hybrid mining truck, and its accuracy was validated by comparing it with experimental data. Using dynamic programming algorithms to plan the SOC for single-cycle operations provides a rational reference for energy allocation across different operational phases of mining trucks during a single cycle. Next, using the powerful nonlinear mapping and self-learning capabilities of neural networks, the system learns the optimal power output sequence of the range extender during offline computation. This enables rapid decision-making and adaptive adjustment of power allocation between the range extender and battery in real-time applications, thereby effectively reducing energy consumption under cyclic operating conditions. Design a SOC tracking controller to adjust the output of the neural network optimization controller, thereby achieving tracking of the planned global SOC. Finally, validated through simulations and real vehicle tests, the proposed energy management strategy can improve fuel economy, achieving a 1.27% fuel saving compared to traditional rule-based strategies in real vehicle tests. This study provides a feasible energy management scheme for the green and efficient operation of heavy mining vehicles.
Yang, Jianyu, Zhao, Zhiguo, Chen, Huiyong, Li, Tao, Zhuang, Wenyu, Shen, Peihong, Tang, Peng
Sideslip Angle Estimation for Mining Trucks Based on Real-Time Cornering Stiffness Identification2026-01-06184/7/2026
High-precision estimation of key vehicle–road state parameters is crucial for ensuring the accurate and safe control of mining trucks (MT), as well as for reliable trajectory tracking. Among these parameters, the vehicle sideslip angle is particularly critical for assessing and predicting lateral stability. However, its direct measurement is challenging, and its estimation typically depends on an accurate characterization of tire cornering stiffness. For MT, large variations in loading conditions (from empty to fully loaded) pose significant challenges to sideslip angle estimation due to the resulting nonlinearity and variability of tire cornering stiffness. To address this issue, a novel joint estimation framework integrating the Moving Horizon Estimation (MHE) and Square-Root Cubature Kalman Filter (SCKF) is proposed to simultaneously achieve high-precision estimation of both tire cornering stiffness for each tire and vehicle sideslip angle. In this framework, the cornering stiffness of the front, middle, and rear axles is identified and updated in real time using MHE through a forgetting-factor least squares method based on yaw rate and lateral acceleration data within a fixed-length time window. The updated stiffness is then incorporated into the SCKF for accurate estimation of the sideslip angle. This sequential process effectively establishes a coupling between the estimation of the two parameters, forming an integrated joint estimation mechanism. The proposed framework is validated on the TruckSim–Simulink co-simulation platform, and the results confirm its superior accuracy and robustness, demonstrating its potential to improve the safety and control performance of MT.
Xia, Xue, Shen, Peihong, Jiao, Leqi, Li, Tao, Chen, Huiyong, Zhao, Kun, Jiao, Leqi, Zhao, Zhiguo
Classification, Nomenclature, and Specification Definitions for Directional Drilling Tracking EquipmentJ2520_202602 (Current)2/18/2026
This SAE Standard applies to directional drilling electronics and tracking equipment of the following types: Tracking transmitter Tracking receiver Telemetry device Remote display This type of tracking equipment is typically used with horizontal earthboring machines as defined in SAE J2022.
MTC9, Trenching and Horizontal Earthboring Machines
Miner’s Safety Bot: An ESP32-Based Autonomous Mobile Mine Safety Monitoring System2026-28-01162/12/2026
Mining operations are important to industrial growth, but they expose the mining workers to risk including hazardous gases, elevated ambient temperatures, and dynamic structural instabilities within underground environments. Safety systems in the past, typically based on fixed sensor networks or manual patrols, fall short in accurate hazard detection amidst shifting mine conditions. The proposed project Miner's Safety Bot advanced this paradigm by leveraging an ESP 32 microcontroller as a mobile platform that integrates gas sensing, thermal monitoring, visual inspection and autonomous obstacle avoidance. The system incorporates MQ7 semiconductor gas sensor to monitor real time carbon monoxide (CO), offering detection range from 5 to 2000 ppm with accuracy of 5 ppm. Temperature and humidity are monitored through DHT11 digital sensor, calibrated to ensure reliability across the harsh microclimates in mines. Navigation and autonomous movement are enabled by Ultrasonic Sensor (HC-SR04) with 3 mm accuracy level for obstacle detection, that is integrated into mobile chassis which is driven by L298N dual H-bridge motor drivers. The bot's orientation and sensor field of view are controlled by a servo motor. For visual inspection, ESP32-CAM module streams real time visuals from mine. Wireless data transmission uses the ESP32's inbuilt Wi-Fi to link sensor outputs to the Blynk IoT platform, that enables to monitor data remotely.
D, Suchitra, D, Anitha, Muthukumaran, Balasubramaniam, Mohanraj, Siddharth, Subash Chandra Bose, Rohan
Comprehensive Evaluation of Bolster Behaviour: Field Data Insights for Design Modifications2026-26-04971/16/2026
Bogie suspension systems are becoming increasingly popular in tipper vehicles to enhance their performance and durability, especially in demanding environments like construction and mining areas [1]. Bolsters contribute significantly to the overall performance and durability of the bogie suspension systems of tipper vehicles by evenly distributing the loads across the whole suspension system. They act as shock absorbers and negate the impact caused by the rough terrains and heavy loads, thereby reducing stress on individual components and maintaining the structural integrity of the vehicle. Bolsters also help in improving the ride comfort and to maintain the position of the suspension system [2]. This study focuses on the comprehensive testing and evaluation of bolsters to understand their modes and displacement data derived from field data. The primary objective is to analyse the performance and behaviour of bolsters under various operational conditions. Critical manners of deformation and displacement patterns were identified by methodically examining the collected data from the field. The purpose of these acumens is to inform and guide the consequent design modifications, to which they will be of utmost importance. The result of these evolutions in the design of bolsters will eventually lead to more effectual and durable bolsters which in turn will improve their trustworthiness and efficiency in real-world applications. Due to a number of aspects measuring bolster displacement and modes data in the field is a challenging task. Because the bolster may move unpredictably in jagged and rough terrain, it is more difficult to measure displacement and modes precisely. Precise data collection depends on the sensor’s placement. It can be difficult to identify the best places for sensors in the field that prevent interference and produce accurate data. It is crucial to make sure that every measurement tool is accurately attuned both before and during data collection activity. Sensor drift due to field conditions may demand frequent recalibration due to the complex and multidirectional movement of bolsters in tipper vehicles. It takes sophisticated algorithms and analytical methods to accurately capture these movements and realize their modes. Due to its placement within the vehicle’s suspension system, the is challenging to reach for measurement. This may curb the kinds of sensors and techniques that are available for use. In general, an assortment of environmental, technical and practical obstacles must be overcome in order to measure bolster displacement and modes data in the field. Careful planning, sturdy tools and pioneering analytical techniques are needed to handle these complications and guarantee accurate and reliable data collection.
V Dhage, Yogesh, Kolage, Vikas
Mobility Within Mines: Harmonization of Safety Requirements for Underground Mining Trucks in India2026-26-01401/16/2026
The need for energy is ever increasing, though the dependency on renewable energy have increased, it is not sufficient to cater the demand. India is one of fastest developing country which depends on coal 55% for its total energy need. To achieve coal digging & transportation an underground mining vehicle has gained high importance. Underground mine environment is inherently dangerous due to various factors, including explosive and toxic gases, dust, and the potential for collapses. Thereby vehicles running in coal mines requires extreme safety features to safeguard its operator & coal mine workers. In India the Directorate General of Mines Safety (DGMS) under Government of India circulates notification to Manager of Coal and Metalliferous Mines & OEM, concerned about the minimum safety evaluations to be taken care for the mining trucks. It has been observed that there are significant inconsistencies in design practices for mining vehicles, with the presence of multiple, unverified types and models. In many cases, these designs lack conformity to established Indian or international standards, even where such standards are readily available. This not only compromises quality and reliability but also poses risks to safety and long-term sustainability. This Paper is providing complete guideline for required safety features for the latest available technology in underground mining trucks. This paper will take you through various standards available globally to insure safety of the operator. Further the Paper will provide complete solution for specific modifications in standard procedure to fit with Indian scenario. Presently in India, Underground Mining trucks are not covered under Central Motor vehicle rules as the mining truck application is way different than the commercial trucks those ply on road. The paper gives guideline for having safety related compliances also touches upon performance & environment related compliances which aligns mining trucks safety through global practices and technology assessment. This paper will also guide for designers and engineers to consider various standards which shall support their study & design to meet listed standards required for mining truck application. This paper can be a guideline for mining industry & regulatory bodies in India for keeping technical standards & enhancement in technology so that new guidelines can be inclusive of latest standards requirements before deploying vehicles for underground mining activity.
Babar, Sagar, Akbar Badusha, A
A Study of Electric Motor Configuration for Multi Axle Electric Heavy Duty Commercial Truck Application to Attain Maximum Potential of Torque Vectoring2026-26-00841/16/2026
As the air pollution level rises around the globe, the need for alternative sources of energy increases, and this need applies to automotive industry also. Commercial vehicles are one of the major sources of air pollution around the world as they have impactful applicability in our day to day life. With growing advancement in mobility solutions, commercial vehicles are undergoing transformation to improve efficiency, safety and performance. One of the emerging technologies is of torque vectoring which is a concept used to provide better traction and stability to the vehicle in different driving conditions and used in the vehicle having multi motor configuration. Advance torque vectoring concept coupled with electric motor can react to dynamic driving conditions by providing instant torque. The concept of torque vectoring can be useful for heavy commercial vehicles used in off-road applications such as mining because torque vectoring helps in better weight management, cornering stability, and better drivability on different road surface conditions. Torque vectoring improves overall dynamics of the vehicle to provide better traction and improving maneuverability. This paper discusses different configurations for electric motor placement to achieve maximum potential for torque vectoring for a multi axle heavy commercial electric vehicle used in off-road applications. An overview of torque vectoring control approach used for this study is also explained in this paper. Different configurations were studied by varying the number and placement of motors on both the rear axle. These configurations are analyzed by running the simulation in the Simulink-Trucksim co-simulation environment. The simulation data is further analyzed on different parameters like steering angle, yaw rate, and torque distribution by individual motors to decide the best configuration for the vehicle to reach maximum potential of torque vectoring.
Agarwal, Pranjal, Chaudhari, Gitesh, Gangad, Vikas, Penta, Amar
Comprehensive Durability Assessment of Planet Carrier in Heavy-Duty Tipper Gearboxes Using RLDA Torque Data2026-26-04431/16/2026
In heavy-duty tippers, where challenging conditions demand high torque, planet carriers play a crucial role by enabling efficient load distribution and torque transmission while supporting gear ratio and speed variation in space-constrained systems such as automatic transmissions, hybrid drivetrains, and electric vehicles. This paper focuses on the comprehensive durability performance assessment of planet carrier housing (PCH) using duty cycles derived from road load data acquisition (RLDA) measurements for a heavy-duty tipper gearbox development program. The existing Design Validation Plan (DVP) for the planet carrier considers first gear utilization of 10-15% at 40% vehicle overload, in line with historical data. However, recent trends in mining applications revealed vehicle overloads of 55-65%, leading to an increase in first gear utilization (25-35%). This shift presents challenges for original equipment manufacturer (OEM) to enhance design durability while incorporating additional safety margins to meet the demands of a competitive, cost-driven market. To address this discrepancy, road load data was collected on a heavy-duty tipper with 65% abusive overloads. Torque telemetry on the propeller shaft captured RLDA data, which was processed to generate a torque profile for the planet carrier. This profile was then used to define the duty cycle via torque rainflow matrices across various gear conditions. The data revealed a 35% first gear utilization, prompting a revision of the existing DVP using this field-reflective data. Using revised DVP, a comprehensive durability assessment of the planet carrier was conducted, considering the torque rainflow matrices for all gear operating conditions. The fatigue assessment included the effect of induction hardening using a boundary layer approach in the commercial fatigue solver FEMFAT. Critical locations in the planet carrier were identified and addressed through suitable design modifications to meet the fatigue damage targets of the revised DVP. The final prototype design was validated through physical testing, showing no failures and aligning well with simulation predictions.
Bagane, Shivraj, Pendse, Ameya
Influence of Tipping Angles and Body Shapes on Payload Discharge Efficiency in Dump Trucks2026-26-04931/16/2026
The payload retention and material outflow pattern during the unloading process of dump trucks are critical factors influencing the efficiency and effectiveness of operations in construction and mining industries. This paper investigates the impact of tipping angles and the shape of the dump truck body on payload retention and outflow characteristics. Using FEA methodology, we explore the material outflow pattern for different body geometries such as box body, scoop body etc. for comparative analysis in order to optimize the shape for better & effective unloading. The results demonstrate a comparative estimation for an optimal body shape configuration to effectively unload payload and correlation of payload retention at various tipping angles. The current study also describes the effect of high cohesive forces between the payload particles on the discharge efficiency, and the pattern of mass flow rate is mapped against the tipping angle for various types of material properties for comparison. The paper also highlights the outflow pattern for various material types due to gravity when the body is kept at a particular tipping angle.
Phukan, Prerna, Sahu, Hemant, Dave, Rajeev
Static Rollover Stability Analysis of Heavy Tipper Vehicle2026-26-00901/16/2026
Heavy tipper vehicles are primarily utilized for transporting ores and construction materials. These vehicles often operate in challenging locations, such as mining sites, riverbeds, and stone quarries, where the roads are unpaved and characterized by highly uneven elevations in both the longitudinal and lateral directions of vehicle travel. During the unloading process, the tipper bodies are raised to significant heights, which increases the vehicle's centre of gravity, particularly if the payload material does not discharge quickly. Such conditions can lead to tipper rollover accidents, causing severe damage to life and substantial vehicle breakdowns. To analyse this issue, a study is conducted on the vehicle design parameters affecting the rollover stability of a 35-ton GVW tipper using multi-body simulations in ADAMS software. The tilt table test was simulated to determine the table angle at which wheel lift occurs. Initially, simulations are performed with the rigid body model, and the results are validated using the calculation method specified in the ECE R 111 regulation. Subsequently, additional factors, such as lash in the suspension and body joints, as well as the flexibility of the vehicle structure, are incorporated into the simulation model to analyse variations in rollover threshold angle during the tilt table test.
Vichare, Chaitanya Ashok, Patil, Sudhir, Gupta, Amit
Guidelines and Procedures for Horizontal Directional Drilling (HDD) Machine Tracking Equipment OperatorsJ2519_202601 (Current)1/1/2026
These general guidelines and precautions apply to personnel operating directional drilling tracking equipment when used with horizontal directional drilling (HDD) machines as defined in ISO 21467:2023.
MTC9, Trenching and Horizontal Earthboring Machines
Application of a Process Mining Tool for Three-Way Match Analysis in the Procure-to-Pay Process of a Multinational Auto Parts Company2025-36-023612/18/2025
Process mining emerges as a very important tool in the automotive industry to improve processes and increase efficiency. Its use allows the identification of bottlenecks and opportunities for improvement in production processes, contributing to increased productivity and cost reduction. This article aimed to evaluate the benefits of applying the Process Mining tool by conducting a Three-way match analysis in the Procure-to-pay (PTP) process of a company in the auto parts sector, seeking to identify opportunities for improvement. Analysis using process mining in PTP of the organization allowed us to identify significant number of cases of price discrepancies were observed in relation to orders related to services, being 2.5 times higher than orders related to materials. Additionally, quantity discrepancies represented 24% of the cases analyzed, compared to only 1.5% of price discrepancies. Of the materials involved in these price discrepancies, approximately 63% were not registered in the system. Most cases of price discrepancies among material suppliers were related to transportation services. Furthermore, 22% of the processes analyzed involved price changes, possibly due to how tax calculation was configured in the SAP system. It is recommended that the company continue to seek solutions to reduce price discrepancies, especially concerning service orders, and increase the level of automation in purchasing and payment processes. In light of the presented results, the process mining tool emerges as a strategic ally, offering competitive advantage by identifying bottlenecks, reducing costs, and automating processes.
Rosa da Silva, Petterson Maxwell, Campos, Renato de, Franco, Bruno Chaves
Directional Drilling Planning and Mapping NomenclatureJ2583_202512 (Current)12/10/2025
This SAE Standard applies to planning and mapping various types of information associated with directional boring/drilling machines. This type of planning and mapping information is typically used with horizontal directional drilling (HDD) machines as defined by ISO 21467:2023.
MTC9, Trenching and Horizontal Earthboring Machines
Research on Modeling Method of Metro Passenger Flow Congestion Propagation with Dynamic Spatiotemporal Graph Convolutional Networks2025-99-045512/10/2025
With the rapid development of metro network operation, metro passenger flow congestion propagation occurs frequently. Accurately modeling passenger flow congestion propagation is crucial for alleviating metro passenger flow congestion and formulating corresponding control strategies. Traditional modeling methods struggle to effectively capture the complex spatiotemporal dependency relationships in metro networks. To improve the accuracy of congestion propagation modeling, this paper proposes a Dynamic Spatiotemporal Graph Convolutional Network (DSTGCN). The model integrates node attributes and temporal encoding through a dynamic adjacency matrix generation module, uses multi-head attention mechanisms to adaptively learn the time-varying propagation intensity between nodes, and combines static topology to construct dynamic adjacency matrices. A multi-scale spatiotemporal feature extraction module is designed, employing temporal convolution and spatial attention mechanisms to mine periodic and local correlation features, and aggregating historical states with different time lags through stacked graph convolutions. Experimental results on real metro datasets verify the effectiveness of each module of the model and reveal the inherent laws of passenger flow congestion propagation in metro networks. The research provides theoretical support for congestion early warning and dynamic regulation in metro network operation.
Chen, Beijia, Wang, Junhang, Shao, Jiayu
Hydro-Pneumatic Suspension Optimization for Mining Explosion-Proof Vehicles in Underground Coal Mines2025-99-010611/11/2025
This study investigates the critical factors influencing the performance of hydro-pneumatic suspension systems (HPSS) in mining explosion-proof engineering vehicles operating in complex underground coal mine environments. To address challenges such as poor ride comfort and insufficient load-bearing capacity under harsh mining conditions, a two-stage pressure HPSS was analyzed through integrated numerical modeling and field validation. A mathematical model was established based on the structural principles of the suspension system, focusing on key parameters including cylinder bore (195–255 mm), piston area (170–210 mm), damping orifice diameter (7–8 mm), check valve flow area, and accumulator configurations (low-pressure: 1.2 MPa, high-pressure: 6 MPa). Experimental trials were conducted in active coal mines, simulating typical mining scenarios such as uneven road surfaces (120 mm obstacles), heavy-load gangue transportation, and confined-space operations in thin coal seams (<1.5 m). This study conducted experimental validation of a hydro-pneumatic suspension system (HPSS) for mining explosion-proof engineering vehicles under multi-condition operational scenarios in active coal mines. Field tests were performed to simulate typical mining environments, including uneven road surfaces (120 mm obstacles), variable-speed driving (constant speed, acceleration, deceleration), and inclined terrains (uphill, downhill, and near-horizontal road surfaces). Comprehensive performance evaluations focused on dynamic stroke stability, vibration attenuation, and safety metrics were carried out by replicating real-world mining conditions. Results demonstrated that optimizing the cylinder bore diameter and adjusting the piston area significantly enhanced dynamic stroke stability, ensuring consistent load-bearing capacity across diverse mining terrains. Furthermore, tuning the damping orifice diameter effectively improved anti-rollover capability while maintaining ride comfort, achieving a balanced trade-off between vibration suppression and operational safety. Parameter adjustments to the HPSS, validated through rigorous field trials, proved critical for enhancing driving stability and safety in complex underground mining environments. These findings provide actionable insights for designing robust suspension systems tailored to the extreme demands of mineral extraction operations.
Song, Yan, Liang, Yufang
Cybersecurity: Applying Threat Modeling to Sensor-Control Architectures in Autonomous Off-Highway Vehicles2025-28-031611/6/2025
The rapid evolution of autonomy in Off-Highway Vehicles (OHVs)—spanning agriculture, mining, and construction—demands robust cybersecurity strategies. Sensor-control systems, the cognitive core of autonomous OHVs, operate in harsh, connectivity-limited environments. This paper presents a structured approach to applying threat modeling to these architectures, ensuring secure-by-design systems that uphold safety, resilience, and operational integrity.
Kotal, Amit
Off-Highway Electrification: A Case Study beyond Propulsion and Emission2025-28-022311/6/2025
To provide needs of food, clothing and infrastructure for growing population of the world, off-highway vehicles such as those in construction, agriculture and commercial landscaping are moving towards electrification for enhanced precision, productivity, efficiency and sustainability. It has also paved way to adopt autonomy of these vehicles to address challenges like skilled labour shortage for timely and efficient execution. There are many challenges and opportunities of electrification in off-highway domain, be it through completely replacing engine in vehicles or efficiency improvements using hybrid architecture for powertrain and auxiliary power demands, electrification being key enabler precision and speed of the complex operations, automation of complex operation. This paper explains the need of electrification in electric off-highway vehicles and shows how the electrification solves the current challenges faced by off-highway heroes like farmers, construction site owners and workers, commercial lawn and golf turf owners and workers, etc. It first discusses the challenges faced by this industry in terms of scarcity of skilled labour, changing weather conditions, operator fatigue and ever-increasing pressure of productivity, uptime and cost. Then paper presents why electrification is key to solve these issues and how increasing adoption of such technologies becomes relevant.It further explains some architecture/application case studies in farm equipment to cater needs of complex operations like crop care and harvesting, manoeuvring through different soils, lands as well as doing repeated and complex operations at construction sites and other type of off-highway applications like oil fracking industry, trucks, mining, etc.. This case studies describe how electrification has directly enabled more productivity through speed and precision, less operator fatigue, fuel efficiency, farm input efficiency and has become enabler for autonomy.
Deshpande, Chinmay Vasudev, Mujumdar, Chaitanya, Bachhav, Kiran
Enhancing Diagnostic Efficiency and Customer Support for Off-Highway Vehicles in India2025-28-030111/6/2025
Off-highway vehicles (OHVs) are vital for India’s construction, mining, agriculture, and infrastructure sectors. With growing demand for productivity and sustainability, the need for efficient customer support and precise diagnostic techniques has become paramount. This paper presents a comprehensive study of challenges faced in India, current and emerging diagnostic technologies, troubleshooting techniques, and strategies for effective customer support. Case studies, tables, and diagrams illustrate practical solutions.
Mulla, Tosif, Thakur, Anil, Tripathi, Ashish
Self-Healing Biodegradable Composites for Off-Highway Vehicles in Extreme Environments2025-28-021111/6/2025
Off-highway vehicles (OHVs) frequently operate in extreme environments—ranging from arid deserts and frozen tundras to dense forests and abrasive mining zones—where structural wear, impact damage, and environmental stress compromise their material integrity. Frequent repairs and component replacements increase operational costs, downtime, and environmental waste, making durability and sustainability key concerns for next-generation vehicle systems. This paper explores a novel class of self-healing biodegradable composites, inspired by biological systems, to address these challenges. The proposed materials combine bio-based resins, microencapsulated healing agents, and shape-memory polymers (SMPs) to autonomously repair microcracks and surface-level damage when triggered by thermal, UV, or mechanical stimuli. The design draws inspiration from natural self-healing systems such as tree bark and reptile skin, replicating their regenerative behavior to enhance structural resilience in OHVs. The composite’s biodegradability ensures environmental friendliness at end-of-life, aligning with circular economy goals. Laboratory-scale experiments and computational simulations assess tensile strength, fracture toughness, healing efficiency, and environmental stability (e.g., temperature cycling, UV exposure, and abrasion).
Vashisht, Shruti
Connected Aftermarket Services for Off-Highway Vehicles: Enhancing Fleet Management Through Real-Time Data and Predictive Maintenance2025-28-030711/6/2025
Off-highway vehicles (OHVs) are essential in heavy-duty industries like mining, agriculture, and construction, as equipment availability and efficiency directly affect productivity. In these harsh settings, conventional maintenance plans relying on set intervals frequently result in either early component replacements or unexpected breakdowns. This document presents a Connected Aftermarket Services Platform (CASP) that utilizes real-time data analysis, predictive maintenance techniques, and unified e-commerce functionalities to evolve OHV fleet management into a proactive and smart operation. The suggested system integrates IoT-enabled telematics, cloud-based oversight, and AI-powered diagnostics to gather and assess machine health indicators such as engine load, vibration, oil pressure, and usage trends. Models for predictive maintenance utilize both historical and real-time data to produce advance notifications for component failures and maintenance requirements. Fleet managers get practical alerts and enhanced service suggestions, reducing unexpected downtime. The platform includes an e-commerce interface that enables smooth ordering of spare parts informed by predictive diagnostics and lifecycle data of components. The system includes features like auto-generated parts lists, supplier comparisons, and inventory tracking, allowing for efficient and cost-effective maintenance activities. Simulation studies demonstrate a 21% decrease in maintenance expenses, a 32% reduction in unplanned downtime, and enhanced inventory turnover rates within the simulated OHV fleets. These findings emphasize the effect of integrated services on operational effectiveness, cost reductions, and sustainability. The CASP model reimagines lifecycle support for OHVs by establishing a digital thread from field operations to aftermarket logistics, offering a scalable, data-driven approach for contemporary fleet management.
Vashisht, Shruti
Onboard Exhaust Capture and Carbon Sequestration for Off-Highway Vehicles: A Sustainable Approach to Emission Reduction2025-28-021511/6/2025
Off-highway vehicles (OHVs) in sectors such as mining, construction, and agriculture contribute significantly to global greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and nitrogen oxides (NOₓ). Despite the growth of alternative fuels and electrification, diesel engines remain dominant due to their superior torque, reliability, and adaptability in harsh environments. This paper introduces a novel onboard exhaust capture and carbon sequestration system tailored for diesel-powered OHVs. The system integrates nano-porous filters, solid-state CO₂ adsorbents, and a modular storage unit to selectively capture CO₂ and NOₓ from exhaust gases in real time. Captured CO₂ is then compressed for onboard storage and potential downstream utilization—such as fuel synthesis, carbonation processes, or industrial sequestration. Key innovations include: A dual-function capture mechanism targeting both CO₂ and NOₓ Lightweight thermal-regenerative adsorption materials Integration with existing diesel aftertreatment systems Simulation and bench-scale testing indicate up to 72% CO₂ capture efficiency under transient OHV duty cycles, with energy penalties managed below 6% of net engine output. The system offers a retrofit-compatible, scalable pathway to significantly reduce carbon emissions in hard-to-electrify sectors, serving as a bridge toward long-term carbon neutrality goals.
Vashisht, Shruti
Optimizing Heavy-Duty Mining Operations with LNG Powertrains: Boosting Efficiency and Minimizing Environmental Impact2025-28-023611/6/2025
Heavy-duty mining is a highly demanding sector within the trucking industry. Mining companies are allocated coal mine sites, and fleet operators are responsible for efficiently extracting ore within the given timeframe. To achieve this, companies deploy dumper trucks that operate in three shifts daily to transport payloads out of the site. Consequently, uptime is crucial, necessitating trucks with exceptionally robust powertrains. The profitability of mining operations hinges on the efficient utilization of these dumper trucks. Fuel consumption in these mines constitutes a significant portion of total expenses. Utilizing LNG as a fuel can help reduce operational fuel costs, thereby enhancing customer profitability. Additionally, employing LNG offers the potential to lower the CO2 footprint of mining operations. This paper outlines the creation of a data-driven duty cycle for mining vehicles and the simulation methodology used to accurately size LNG powertrain components, with a focus on maintaining uptime and productivity.
John, Ann Veena, Pendharkar, Koustubh
Ensuring Functional Safety and SOTIF for Autonomous Off-Highway Vehicles: Addressing Risks in Complex Systems2025-28-031511/6/2025
The increasing complexity of autonomous off-highway vehicles, particularly in mining, demands robust safety assurance for Electronic/Electrical (E/E) systems. This paper presents an integrated framework combining Functional Safety (FuSa) and Safety of the Intended Functionality (SOTIF) to address risks in autonomous haulage systems. FuSa, based on ISO 19014[1] and IEC 61508[2], mitigates hazards from system failures, while SOTIF, adapted from ISO 21448[3] addresses functional insufficiency and misuse in complex operational environments. We propose a comprehensive verification and validation (V&V) strategy that identifies hazardous scenarios, quantifies risks, and ensures acceptable safety levels. By tailoring automotive SOTIF standards to off-highway applications, this approach enhances safety for autonomous vehicles in unstructured, high-risk settings, providing a foundation for future industry standards.
Kumar, Amrendra, Bagalwadi, Saurabh
Environmental Factors and Off-Highway Vehicle Safety Abstract2025-28-032211/6/2025
Off Highway vehicles recreation has rapidly expanded across the globe hence it is important to consider the safety of off-highway vehicles which is significantly influenced by various environmental factors, which can pose unique challenges and risks. it is important to make sure that the entire vehicle operates safely and reliably even in the toughest conditions. This paper investigates the impact of environmental conditions on the safety and performance of off-highway vehicles, such as construction equipment, agricultural machinery, and mining vehicles. By examining factors such as terrain, weather conditions, visibility, and natural obstacles, the study aims to identify key hazards and propose strategies to mitigate them. The paper explores how advanced technologies, including digital twins and predictive analytics, can be leveraged to enhance safety measures and improve vehicle resilience in diverse environmental settings. Through comprehensive case studies and empirical data, we demonstrate the critical role of environmental factors in shaping safety protocols and maintenance practices for off-highway vehicles. The findings underscore the importance of proactive safety management and the adoption of innovative technologies to ensure the reliable and safe operation of off-highway vehicles in challenging environments.
Mogal, Masthanvali, Chennamalla, Chandra Shekar
Adaptive Terrain Morphing Chassis for Off-Highway Vehicles: Enhancing Fuel Efficiency through Dynamic Structural Adaptation2025-28-034211/6/2025
Off-highway vehicles (OHVs) routinely navigate unstable and varied terrains—mud, sand, loose gravel, or uneven rock beds—causing increased rolling resistance, reduced traction, and high energy expenditure. Traditional rigid chassis systems lack the flexibility to adapt dynamically to changing surface conditions, leading to inefficiencies in vehicle stability, maneuverability, and fuel economy. This paper proposes an adaptive terrain morphing chassis (ATMC) that can actively modify its structural geometry in real-time using embedded sensors, hydraulic actuators, and soft robotic elements. Drawing inspiration from nature and recent advances in adaptive materials, the ATMC adjusts vehicle ground clearance, track width, and load distribution in response to terrain profile data, thereby optimizing fuel efficiency and performance. Key contributions include: A multi-sensor fusion system for real-time terrain classification Hydraulic actuators and morphing polymers for variable chassis configurations Simulated fuel savings of 8–14% across diverse terrains compared to fixed-geometry systems The design also contributes to sustainability by reducing energy waste and material wear, and by enabling smart, terrain-responsive behavior that can extend the lifespan of vehicle components. This innovation holds significant potential for deployment in resource-heavy industries where OHVs operate in unpredictable and efficiency-critical environments.
Vashisht, Shruti
Customer Support – Failure and Warranty Analysis in Off-Highway Vehicles2025-28-030011/6/2025
The reliability and durability of off-highway vehicles are crucial for industries like construction, mining, and agriculture. Failures in such machines not only disrupt operations but can also lead to significant economic losses and safety concerns. Effective failure and warranty analysis processes are essential to improve customer support, minimize downtime, and enhance equipment life cycle. This paper outlines a comprehensive 7-step failure analysis methodology tailored for off-highway vehicles, accompanied by warranty analysis using Weibull, 6MIS, and 12MIS IPTV. It details the process from problem identification through permanent solution implementation, emphasizing tools and techniques necessary for sustainable improvements. The structured approach provides an actionable blueprint for OEMs and service teams to enhance customer satisfaction, support sustainable development goals, and maintain regulatory compliance.
Mulla, Tosif, Thakur, Anil, Tripathi, Ashish
Decarbonizing Off-Highway Vehicles: Fuel Alternatives and Implementation Strategies2025-28-023011/6/2025
Off-Highway Vehicles (OHVs) — including mining trucks, construction machinery, and agricultural equipment — contribute significantly to greenhouse gas (GHG) emissions and local air pollutants due to their dependence on fossil diesel. Achieving sustainable development goals in off-highway sectors requires transitioning toward alternate fuels that can reduce CO₂, NOₓ, and particulate matter (PM) emissions while maintaining performance and reliability. This paper comprehensively evaluates alternate fuels such as biodiesel, renewable diesel, compressed and liquefied natural gas (CNG/LNG), liquefied petroleum gas (LPG), hydrogen, and alcohol-based blends. Using insights from Service Bulletins, fuel standards, and the Worldwide Fuel Charter, it discusses fuel properties, engine compatibility, operational challenges, sustainability impacts, economic feasibility, safety considerations, and regulatory aspects. Case studies of alternate fuel deployment in OHVs illustrate practical challenges and successes. Recommendations are made for fuel selection, system modifications, and future research to support sustainable operation of OHVs.
Mulla, Tosif, Thakur, Anil, Tripathi, Ashish
Rethinking VEHICLE CONTROLS25TOFHP10_0310/1/2025
Smarter control architectures including CAN- and LIN-based multiplexing can elevate operational efficiency, customization and end-user experience. From long-haul Class 8 trucks navigating cross-country routes to articulated dump trucks operating deep in a mining pit, the need for smarter, more reliable and more efficient control systems has never been more critical. Across both on- and off-highway commercial vehicle segments, OEMs are re-evaluating how operators interact with machines - and how those systems can be made more robust, flexible and digitally connected. Suppliers have responded to this industry-wide shift with new solutions that reduce complexity, improve durability and help customers future-proof their vehicle architectures. For example, Eaton's latest advancement is the E33 Sealed Multiplexed (MUX) Rocker Switch Module (eSM) - a sealed, modular switch solution that replaces traditional electromechanical designs with a multiplexed digital interface. Combined with Eaton's OMNEX Trusted Wireless mobile control systems, these innovations provide OEMs with a unified ecosystem for both cab-based and remote vehicle control.
Ortega, Carlos
A Control Strategy of Path Tracking for Unmanned Mining Truck Based on Model Predictive Control and Considering Steering Delay10-09-04-00378/22/2025
To achieve accurate and stable path tracking for unmanned mining trucks in the face of changing paths and response delays in steering, this study raised a lateral control strategy for unmanned mining trucks based on MPC and considering steering delay response characteristics. Under the basis of deriving the state space equation from the commonly used two degrees of freedom truck dynamics model, this method introduces the dynamic relationship between steering angle issuance and actual response to form an augmented form of state vector to overcome the control instability caused by steering response delay. Then, based on the MPC method, a constrained objective function is constructed to solve for the optimal control law. In response to the problem of inaccurate selection of prediction and control time domains, this article proposes an adaptive selection method for prediction and control time horizon based on a modified particle swarm optimization (MPSO) algorithm, which obtains the optimal prediction and control time horizon that meet the preset training road conditions in this study, preventing the problem of control accuracy and control oscillation hard to balance caused by the horizon being too small or too large, thereby improving the control effect. Finally, the tracking performance of this algorithm was compared with pure tracking algorithms using a truck dynamics model bench simulation developed independently based on Simulink and a mining truck real-vehicle verification. The algorithm demonstrated good path tracking performance.
Mao, Libo, Wu, Guangqiang, Gui, Yuhui
EDITORIAL: Tech - and tariffs - top of mind at tradeshows25TOFHP06_056/1/2025
April saw two major tradeshows take place, playing host to numerous advanced vehicle and technology reveals from global OEMs and suppliers - some of which are detailed in these pages. Bauma in Munich, Germany, a leading trade fair for the construction and mining vehicle industries, saw around 600,000 visitors from more than 200 countries and regions, as well as over 3,600 exhibitors from 57 nations. Billed as the largest advanced CV technology show, ACT Expo engaged more than 12,000 stakeholders from at least 54 countries, including over 2,700 fleet operators. But just as present as the technology itself at these shows was the ongoing uncertainty stemming from the Trump administration's volatile trade policy announced on April 2 involving steep tariffs that have been adjusted frequently in the ensuing weeks.
Gehm, Ryan
SAE Truck & Off-Highway Engineering: April 202525TOFHP044/10/2025
Designs on a diverse powertrain future Perkins details range of development efforts to power future off-highway machines, from clean-sheet diesel to hybrid-electric and hydrogen combustion. Autonomous trucking tech hits high gear While some developers of autonomous technology for commercial trucks have stalled out, there's renewed energy to deliver augmented ADAS and automated driving systems to mass production. Autonomous quarry haulage in high demand Komatsu works with Pronto to upfit a growing fleet of haul trucks operating at Komatsu's Arizona Proving Grounds and customer sites. Editorial Hydrogen is hitting some headwinds Cummins unveils new B7.2 diesel engine Artificial intelligence being schooled for mining applications FPT's 37-kWh battery pack dives into marine market Mitsubishi Fuso repurposes EV batteries for energy storage systems First drive: REE Automotive's electrifying P7 delivery chassis Kalmar launches 3rd-gen electric terminal tractor Product Briefs SPOTLIGHTS: Thermal Management; Electronic Components
Designs on a diverse POWERTRAIN FUTURE25TOFHP04_014/1/2025
Perkins details range of development efforts to power future off-highway machines, from clean-sheet diesel to hybrid-electric and hydrogen combustion. Many manufacturers in the construction and mining vehicle sectors have tabbed the Bauma trade show in April as the venue for major product debuts. Perkins is one of those, though it provided select media an overview of its latest powertrain developments and projects at a pre-Bauma briefing in early February. Hydrogen and hybrids were a large part of the discussion at the London media event, but Perkins began the day expounding on good old diesel-engine development. The company's engineers are still working hard to strengthen - and streamline - its diesel portfolio, all while readying new platforms for other fuels and applications.
Gehm, Ryan
AUTONOMOUS QUARRY HAULAGE in high demand25TOFHP04_034/1/2025
Komatsu works with Pronto to upfit a growing fleet of haul trucks operating at Komatsu's Arizona Proving Grounds and customer sites. At Komatsu's Quarry Days 2025 event at its Arizona Proving Grounds (AZPG) outside of Tucson, dealers, customers and media got the opportunity to operate Komatsu mining and construction equipment, learn about its latest technology innovations and talk to product experts. A highlight of the event was the first public demonstration of Komatsu's HD605-10 haul truck outfitted with Pronto's Autonomous Haulage System (AHS), spotlighting the equipment maker's partnership with the AI tech startup to pilot autonomous quarry haulage operations. Several HD605-10 trucks have been equipped with AHS as part of this program currently being tested by quarry operators in Texas. The AZPG site currently has just the one automated truck.
Gehm, Ryan
Artificial intelligence being schooled for mining applications25TOFHP04_064/1/2025
A battery-electric Honda midsize SUV entering production in early 2026 will use Helm.ai's artificial intelligence to facilitate conditional automated driving. The start-up firm's AI technology could soon see its first off-highway application. “Different driving environments look pretty much the same from an engineering perspective, so the lessons we've learned from [passenger vehicle] autonomous driving can be brought to the mining space in a fairly seamless fashion,” Vladislav Voroninski, cofounder and CEO of Helm.ai, said in an interview with SAE Media.
Buchholz, Kami
Critical Minerals Recovery from Electronic WasteTBMG-526423/1/2025
There’s some irony in the fact that devices that seem indispensable to modern life — mobile phones, personal computers, and anything battery-powered — depend entirely on minerals extracted from mining, one of the most ancient of human industries. Once their usefulness is spent, we typically return these objects to the Earth in landfills, by the millions.
Adaptive Localization Algorithm for Autonomous Mining Trucks with Motion Distortion Removal2025-01-70271/31/2025
During the operation of autonomous mining trucks in the process of crushing stones, the GPS signal is lost due to signal blockage by the crushing workshop. Simultaneous Localization and Mapping (SLAM) becomes critical for ensuring accurate vehicle positioning and smooth operation. However, the bumpy road conditions and the scarcity of plane and corner feature points in mining environments pose challenges to SLAM algorithms in practical applications, such as pose jumps and insufficient positioning accuracy. To address this, this paper proposes a high-precision positioning algorithm based on inertial navigation 3D signals, incorporating point cloud motion distortion correction, a vehicle roll model, and an Adaptive Kalman Filter (AKF). The goal is to improve the positioning accuracy and stability of autonomous mining trucks in complex scenarios. This paper utilizes real-world operational data from mining vehicles and adopts a 3D point cloud motion distortion correction algorithm to mitigate the impact of bumpy roads on positioning accuracy. Additionally, a dynamic model that considers vehicle sideslip is integrated, and the feedback from the Inertial Measurement Unit (IMU) is fused with the positioning results obtained from LiDAR point cloud registration using Normal Distributions Transform (NDT) through an Adaptive Extended Kalman Filter (AEKF). Furthermore, an error analysis model is designed to enable adaptive adjustment of the algorithm, and the performance of the NDT algorithm is enhanced in open, feature-scarce environments through LiDAR point cloud fusion techniques. Simulation results show that the positioning stability on bumpy roads is improved by approximately 21.2%. The improved algorithm effectively suppresses pose jumps during large turn radii, reducing the average error by 5.94% compared to the traditional Kalman Filter (KF). Moreover, the algorithm demonstrates higher positioning accuracy and stability under sensor failures and adverse weather conditions.
Meng, Chunyang, Song, Kang, Xie, Hui, Xing, Wanyong
Drain, Fill, and Level Plugs for Off-Road, Self-Propelled Work MachinesJ371_202501 (Current)1/21/2025
This SAE Recommended Practice applies to off-road, self-propelled work machine categories of earthmoving, forestry, road building and maintenance, and specialized mining machinery as defined in SAE J1116.,
Machine Technical Steering Committee
Critical Minerals Recovery from Electronic WasteTBMG-523351/1/2025
There’s some irony in the fact that devices that seem indispensable to modern life — mobile phones, personal computers, and anything battery-powered — depend entirely on minerals extracted from mining, one of the most ancient of human industries. Once their usefulness is spent, we typically return these objects to the Earth in landfills, by the millions.
Digital Twin-Enabled Fault Detection for Suspension Systems in Autonomous Mining Haulage Vehicles2024-36-017012/20/2024
Autonomous vehicles for mining operations offer increased productivity, reduced total cost of ownership, decreased maintenance costs, improved reliability, and reduced operator exposure to harsh mining environments. A large flow of data exists between the remote operation and the ore haul vehicle, and part of the data becomes information for the maintenance sector which it monitors the operating conditions of various systems. One of the systems deserving attention is the suspension system, responsible for keeping the vehicle running and within a certain vibration condition to keep the asset operational and productive. Thus, this work aims to develop a digital twin-assisted system to evaluate the harmonic response of the vehicle’s body. Two representations were created based on equations of motion that modeled the oscillatory behavior of a mass-damper system. One of the representations indicates a quarter of the ore transport truck’s hydraulic system in a healthy state, called a virtual entity, and the other representation indicates a quarter of the same system prone to failure. Faults representing leakage in the hydraulic system chambers and piston seal loss are generated by changing the damping coefficient. A sensitivity analysis was conducted to evaluate the harmonic behavior of the vehicle body under a decrease in the damping coefficient. Finally, fault analysis in the hydraulic system was achieved through the calculation of residuals, which is the difference between the oscillatory response of the fault-prone system and the oscillatory response of the digital twin. The results demonstrate the effectiveness of the digital twin approach in accurately detecting and diagnosing faults within the suspension system, thereby ensuring the operational efficiency and sustainability of mining vehicles.
Rosa, Leonardo Olimpio, Branco, César Tadeu Nasser Medeiros
SAE TOMORROW TODAY: Scaling Connected Vehicles with Over-The-Air Updates1349212/18/2024
As the automotive world shifts toward fully software-defined vehicles (SDVs), many OEMs are hesitant to embrace a consumer-driven model for over-the-air (OTA) updates. Enter Sibros, a company offering holistic OTA software that enables automakers to achieve scalable, software-defined mobility. By providing AI-powered insights that deliver continuous product and service enhancements, OEMs can harness Sibros' single no-code platform to monitor, control and optimize SDVs, thus accelerating time-to-market, reducing code defects and enabling hundreds of connected vehicle use cases at global scale. This presents unique value for consumers, OEMs, commercial fleet operators, and even off-road sectors like agriculture and mining. To learn more, we sat down with Hemant Sikaria, CEO & Co-Founder, Sibros, to discuss the impact of his company's groundbreaking technology, how insurance and regulation come into play, and the importance of robust security measures to ensure the responsible growth of SDVs. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, Twitter, and YouTube. Follow host Grayson Brulte on LinkedIn, Twitter, and Instagram.
Hineman, Marcie
Optimizing the Capacity of Dozer Blade with Hydraulic Foldable Wings to Enhance Productivity2024-28-026812/5/2024
Crawler Dozers play a critical role in global construction, mining and industrial sectors, performing essential tasks like pushing the material, grading, leveling and scraping. In the highly competitive dozer market, meeting the growing demand for increased productivity requires strategies to enhance blade capacity and width. Dozer operations involve pushing the material and dozing, where blade capacity significantly influences performance. Factors such as mold board profile, blade height, and width impact the blade capacity which are crucial for productivity in light weight applications such as snow removal and dirt pushing. Blade width is also pivotal for grading and leveling tasks. Traditional blade designs, like straight or fixed U-type blades, constrain operator flexibility, limiting overall productivity. The integration of hydraulic-operated foldable wings on both sides of the blade offers the adaptability to adjust blade capacity which also helps to reduce material spillage. This study investigates the impact of hydraulic folding wings on blade capacity, especially analyzing the correlation between fold angle and blade capacity. In this study, an empirical formula is derived to calculate the blade capacity of a folding blade for different wing folding angles. The optimal fold angle for maximizing capacity is determined for a standard material through analytical methods. Furthermore, a comparative analysis is carried out to assess the blade capacity of a foldable blade at the optimal folding angle in contrast to a straight blade. The study aims to evaluate the consequent influence of the blade capacity on the overall productivity. It is found from the study that the blade curvature included volume accounts for 16% of the total blade capacity and at optimum wing folding angle, the blade capacity is 26% more compared to the straight configuration.
Sahoo, Jyoti Prakash, Sarma, Neelam Kumar
Komatsu employs ‘slick’ battery swapping for underground LHD machine24TOFHP12_1012/1/2024
Komatsu introduced its first battery-electric load-haul-dump (LHD) machine, the WX04B, at the MINExpo tradeshow in September. The WX04B is designed specifically for narrow vein mines in underground hard rock mining operations. Komatsu is pairing the electric LHD with its new OEM-agnostic 150-kW battery charger that was also revealed in Las Vegas. The 4-tonne WX04B LHD features what Komatsu claims is best-in-class energy density, offering up to four hours of runtime on a single charge. The Li-ion NMC (nickel-manganese-cobalt) battery from Proterra has a capacity of 165 kWh and nominal voltage of 660 V. Fewer charge cycles are needed compared to competitors, the company claims, which helps to maximize operational efficiency and minimize downtime. Proterra and Komatsu began their collaboration on the LHD's H Series battery system in 2021, long before Komatsu's acquisition of American Battery Solutions (ABS) in December 2023.
Gehm, Ryan
EDITORIAL: Bauma the big one in 202524TOFHP12_0412/1/2024
December is a good time to reflect on the past year - to celebrate successes and consider opportunities for improvement - but it is also an opportune time to look to the future. As I think about the year ahead and appraise the tradeshow landscape that'll provide significant content for this magazine, mobilityengineeringtech.com, our e-newsletters and other multimedia products, none is bigger than Bauma in Munich, Germany, particularly in terms of the global construction and mining vehicle industries. The triennial event will cover an area that's equivalent to 86 soccer fields, according to Stefan Rummel, CEO of Messe München GmbH. Speaking to the press during an October virtual preview of Bauma 2025, which takes place from April 7-13, Rummel said that the number of exhibitors - expected to be about 3,600 - will be closer to the 2019 event versus the post-COVID-19 edition that was pushed back from its usual spring timeslot to the fall of 2022.
Gehm, Ryan
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