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Browse AllWire Electrical Discharge Machining (WEDM) is a highly accurate machining method that is well-known for its capacity to create complex forms in conductive materials with exceptional precision. Cupronickel, a hard material consisting of copper, nickel, and additional components, is widely employed in marine, automotive, and electrical engineering fields because of its exceptional ability to resist corrosion and conduct heat. The intention of this study is to optimize the parameters of Wire Electrical Discharge Machining (WEDM) for Cupronickel material and create regression models to accurately forecast the performance of the machining process. An exploration was carried out to analyze the influence of important parameters in wire electrical discharge machining (WEDM), namely pulse-on time, pulse-off time, and applied current on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of design of experiments (DOE) enabled a systematic
Brake disc temperature is a critical factor influencing the performance and wear characteristics of braking systems in automobiles. Hence it is very important to optimize the correlation of brake disc temperature prediction with test. In this study critical parameters of Brake Disc temperature evaluation are identified, and algorithm is used to optimize the critical parameters to achieve the correlation of prediction with experiment data. Through a series of controlled experiments and simulations, disc temperatures are monitored under different braking conditions and simultaneously input parameters for prediction are optimized to achieve the correlation. Statistical methods were applied to evaluate the observed correlations and to model the predictive behavior of brake disc temperatures. Finally, A front-loading tool is developed to optimize the brake disc keeping target thermal capacity via algorithm. The findings of this study are expected to contribute to the enhancement of brake
Modal performance of a vehicle body often influences tactile vibrations felt by passengers as well as their acoustic comfort inside the cabin at low frequencies. This paper focuses on a premium hatchback’s development program where a design-intent initial batch of proto-cars were found to meet their targeted NVH performance. However, tactile vibrations in pre-production pilot batch vehicles were found to be of higher intensity. As a resolution, a method of cascading full vehicle level performance to its Body-In-White (BIW) component level was used to understand dynamic behavior of the vehicle and subsequently, to improve structural weakness of the body to achieve the targeted NVH performance. The cascaded modal performance indicated that global bending stiffness of the pre-production bodies was on the lower side w.r.t. that of the design intent body. To identify the root cause, design sensitivity of number and footprint of weld spots, roof bows’ and headers’ attachment stiffness to BIW
In India, Driver Drowsiness and Attention Warning (DDAW) system-based technologies are rising due to anticipation on mandatory regulation for DDAW. However, readiness of the system to introduce to Indian market requires validations to meet standard (Automotive Industry Standard 184) for the system are complex and sometimes subjective in nature. Furthermore, the evaluation procedure to map the system accuracy with the Karolinska sleepiness scale (KSS) requirement involves manual interpretation which can lead to false reading. In certain scenarios, KSS validation may entail to fatal risks also. Currently, there is no effective mechanism so far available to compare the performance of different DDAW systems which are coming up in Indian market. This lack of comparative investigation channel can be a concerning factor for the automotive manufactures as well as for the end-customers. In this paper, a robust validation setup using motion drive simulator with 3 degree of freedom (DOF) is
In the rapidly evolving field of automotive engineering, the drive for innovation is relentless. One critical component of modern vehicles is the automotive ECU. Ensuring the reliability and performance of ECU is paramount, and this has led to the integration of advanced testing methodologies such as Hardware-in-the-Loop (HIL) testing. In conjunction with HIL, the adoption of Continuous Integration (CI) and Continuous Testing (CT) processes has revolutionized how automotive ECU are developed and validated. This paper explores the integration of CI and CT in HIL testing for automotive ECU, highlighting the benefits, challenges, and best practices. Continuous Integration and Continuous Test (CI/CT) are essential practices in software development. Continuous Integration process involves regularly integrating code changes into the main branch, ensuring that it does not interfere with the work of other developers. The CI/CT server automatically build and test code whenever a new commit is
Wire Electrical Discharge Machining (WEDM) is an advanced method of machining that provides distinct benefits in machining materials with high hardness and intricate geometries. Invar 36, a nickel-iron alloy with a lower coefficient of thermal expansion, is widely used in the aerospace, automotive, and electronic industries because of its excellent dimensional stability across a broad range of temperatures. The main objectives are to optimize the machining parameters and create regression models that can accurately predict the key performance indicators. Experimental trials were performed utilizing a WEDM setup to machine Invar 36 under various machining conditions, such as pulse-on time, pulse-off time, current setting percentage (%). The machining performance was evaluated by measuring the material removal rate (MRR), surface roughness (Ra). The design of experiment method (DOE) was utilized to systematically investigate the parameter space and determine the most effective machining
Wire Electrical Discharge Machining (WEDM) is a highly accurate machining approach that is well-known for its capability to create intricate forms in materials with high levels of hardness and intricate geometries. Invar 36, a nickel-iron alloy, is extensively utilized in industries that demand exceptional dimensional stability across a wide temperature range. The objective of this exploration is for optimizing the WEDM parameters of Invar 36 material. Additionally, a predictive model called Adaptive Neuro-Fuzzy Inference System (ANFIS) will be developed to forecast the machining performance. The study involved conducting experimental trials to analyze the influence of crucial factors in WEDM. These parameters included pulse-on time (Ton), pulse-off time (Toff), and current. The objective was to examine their influence on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of Design of Experiments (DOE) enabled a systematic
Emergence of Software Defined Vehicles (SDVs) presents a paradigm shift in the automotive domain. In this paper, we explore the application of Model-Based Systems Engineering (MBSE) for comprehensive system simulation within the SDV architecture. The key challenge for developing a system model for SDV using traditional methods is the document centric approach combined with the complexity of SDV. This MBSE approach can help to reduce the complexity involved in Software-Defined Vehicle Architecture making it more flexible, consistent, and scalable. The proposed approach facilitates the definition and analysis of functional, logical, and physical architecture enabling efficient feature and resource allocation and verification of system behaviour. It also enables iterative component analysis based on performance parameters and component interaction analysis (using sequence diagrams
Road infrastructure has a significant impact on the performance of the truck components which includes ATS & turbocharger. Therefore, it is important for research and development teams to analyze the road infrastructure of the region in which trucks are going to be operated in the future, this helps the teams to make decision on component specification which will exactly cater the customer need in those regions and suggest the optimal design of the component. This paper shows a method to summarize and visualize the road infrastructure particularly focusing on length of road segment and its elevation profile distribution and other is an analysis on continuous road segments (without intersections) and their truck speed limit which will help engineers to identify critical routes & locations in those regions and choose precise parameters for their system using statistical data driven approach. This paper uses OpenStreetMap and Digital Elevation Models for elevation from open-source data