Browse Topic: Statistical analysis
In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable
Tillage, a fundamental agricultural practice involving soil preparation for planting, has traditionally relied on mechanical implements with limited real-time data collection or adjustment capabilities. The lack of real-time data and implement statistics results in fleet managers struggling to track performance, driver behavior, and operational efficiency of the implements. Lack of data on vehicle performance can result in unexpected breakdowns and higher maintenance costs, ensuring compliance with regulations is challenging without proper data tracking, potentially leading to fines and legal issues. Bluetooth-enabled mechanical implements for tillage operations represent an emerging frontier in precision agriculture, combining traditional soil preparation techniques with modern wireless technology. Implement mounted battery powered BLE (Bluetooth Low Energy) modules operated by solar panel based rechargeable batteries to power microcontroller. When Implement is operational turns
In today’s competitive landscape, industries are relying heavily on the use of warranty data analytics techniques to manage and improve warranty performance. Warranty analytics is important since it provides valuable insights into product quality and reliability. It must be noted here that by systematically looking into warranty claims and related information, industries can identify patterns and trends that indicate potential issues with the products. This analysis helps in early detection of defects, enabling timely corrective actions that improve product performance and customer satisfaction. This paper introduces a comprehensive framework that combines conventional methods with advanced machine learning techniques to provide a multifaceted perspective on warranty data. The methodology leverages historical warranty claims and product usage data to predict failure patterns & identify root causes. By integrating these diverse methods, the framework offers a more accurate and holistic
A good Noise, Vibration, and Harshness (NVH) environment in a vehicle plays an important role in attracting a large customer base in the automotive market. Hence, NVH has been given significant priority while considering automotive design. NVH performance is monitored using simulations early during the design phase and testing in later prototype stages in the automotive industry. Meeting NVH performance targets possesses a greater risk related to design modifications in addition to the cost and time associated with the development process. Hence, a more enhanced and matured design process involves Design Point Analysis (DPA), which is essentially a decision-making process in which analytical tools derived from basic sciences, mathematics, statistics, and engineering fundamentals are used to develop a product model that better fulfills the predefined requirement. This paper shows the systematic approach of conducting a Design Point Analysis-level NVH study to evaluate the acoustic
Parts in automotive exhaust assembly are joined to each other using welding process. When the exhaust is subjected to dynamic loads, most of these weld joints experience high stresses. Hence it should be ensured that the exhaust assembly is designed to meet the requirements of exhaust durability for the estimated life of the vehicle. We also know that all parts used in manufacturing of exhaust system have inherent variations with respect to sheet metal thickness, dimensions and shape. Some parts like flex coupling and isolators have high variations in their stiffness based on their material and manufacturing processes. This all leads to a big challenge to ensure that the exhaust system meets the durability targets on a vehicle manufactured with all these variations. This works aims to evaluate the statistical spread in weld life of an exhaust with respect to inherent variations of its components. For the purpose of variational analysis, a Design of Experiments (DOE) is done where
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study primarily examines the use of Haste alloy in vehicle applications and suggests creating regression models to predict performance parameters in ECM. The experiments are formulated based on Taguchi's ideas, and mathematical equations are derived using multiple regression models. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. ANOVA is employed to evaluate the statistical significance of process parameters that impact performance indicators. The proposed regression models for Haste alloy are more versatile
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has emerged as a revolutionary method for fabricating complex geometries using a variety of materials. Polyethylene terephthalate glycol (PETG) is a thermoplastic material that is biodegradable and environmentally friendly, making it a preferred choice in additive manufacturing (AM) due to its affordability and ease of use. This study aims to optimize the FDM settings for PETG material and investigate the impact of key process parameters on printing performance. An experimental study was conducted to evaluate the influence of crucial factors in FDM, including layer thickness, infill density, printing speed, and nozzle temperature, on significant outcomes such as dimensional accuracy, surface quality, and mechanical properties. The use of the Grey Relational Analysis (GRA) approach enabled a systematic assessment of multi-performance characteristics, facilitating the optimization of the FDM process. The findings
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely forecast important performance metrics. Experimental trials were conducted using a WEDM system to mill Invar 36 under several machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
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