Browse Topic: Supply chain management
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a
Fatigue design is invariably of prior concern for the automotive industry, no matter of the evolution of the mobility market: at first because carmakers must stay compliant with general structural integrity requirements for reliability, notably applicable to the chassis system, then due to the endless competition for lightweighting in order to mitigate product costs and/or enhance vehicle efficiency. In the past, this key performance was often tackled by basic reference load cases, making use of the simplest signal content, e.g. sinus functions, to practice constant amplitude loads on test rigs and for computations, respectively. Nowadays, full time series coming from proving ground measurements, or any corresponding virtual road load data computations, may be applied to feed complex vehicle computations for virtual assessment and complex test facilities for final approval, under variable amplitude loads. In between, the concept of load spectra (i.e. distribution of amplitudes with
Battery cell aging and loss of capacity are some of the many challenges facing the widespread implementation of electrification in mobility. One of the factors contributing to cell aging is the dissimilarities of individual cells connected in a module. This paper reports the results of several aging experiments using a mini-module consisting of seven 5 Ah 21700 lithium-ion battery cells connected in parallel. The aging cycle comprised a constant current-constant voltage charge cycle at a 0.7C C-rate, followed by a 0.2C constant current discharge, spanning the useful voltage range from minimum to maximum according to the cell manufacturer. Charge and discharge events were separated by one-hour rest periods and were repeated for four weeks. Weekly reference performance tests were executed to measure static capacity, pulse power capability and resistance at different states of charge. All diagnostics were normalized with respect to their starting numbers to achieve a percentage change
This paper presents a new regression model-based method for accurate predictions of stiffness of different glass laminate constructions with a point-load bending test setup. Numerical FEA models have been developed and validated with experimental data, then used to provide training data required for the statistical model. The multi-variable regression method considered six input variables of total glass thickness, thickness ratio of glass plies as well as high-order terms. Highly asymmetrical, hybrid laminates combining a relatively thick soda-lime glass (SLG) ply joined with a relatively thin Corning® Gorilla® Glass (GG) ply were analyzed and compared to standard symmetrical SLG-SLG constructions or a monolithic SLG with the same total glass thickness. Both stiffness of the asymmetrical laminates and the improvement percentage over the standard symmetrical design can be predicted through the model with high precision.
Time Sensitive Networking (TSN) Ethernet is a real-time networking capability that is being developed by a growing number of embedded computing companies for the earliest stages of adoption by aerospace and defense manufacturers and their suppliers. According to the Institute of Electrical and Electronics Engineers (IEEE) TSN working group, it is a set of standards that provides deterministic connectivity within IEEE 802-aligned networks. Nigel Forrester is the Director of Product Strategy for Concurrent Technologies, a UK-based provider of high performance embedded computing solutions for aerospace, defense and many other industries. Check out our interview with Forrester about the potential impact of TSN Ethernet on new and legacy aerospace and defense applications, and how it is being adopted by manufacturers and system integrators below.
The automotive industry is facing unprecedented pressure to reduce costs without compromising on quality and performance, particularly in the design and manufacturing. This paper provides a technical review of the multifaceted challenges involved in achieving cost efficiency while maintaining financial viability, functional integrity, and market competitiveness. Financial viability stands as a primary obstacle in cost reduction projects. The demand for innovative products needs to be balanced with the need for affordable materials while maintaining structural integrity. Suppliers’ cost structures, raw material fluctuations, and production volumes must be considered on the way to obtain optimal costs. Functional aspects lead to another layer of complexity, once changes in design or materials should not compromise safety, durability, or performance. Rigorous testing and simulation tools are indispensable to validate changes in the manufacturing process. Marketing considerations are also
Design validation plays a crucial role in the overall cost and time allocation for product development. This is especially evident in high-value manufacturing sectors like commercial vehicle electric drive systems or e-axles, where the expenses related to sample procurement, testing complexity, and diverse requirements are significant. Validation methodologies are continuously evolving to encompass new technologies, yet they must be rigorously evaluated to identify potential efficiencies and enhance the overall value of validation tests. Simulation tools have made substantial advancements and are now widely utilized in the development phase. The integration of simulation-based or simulation-supported validation processes can streamline testing timelines and sample quantities, all the while upholding quality standards and minimizing risks when compared to traditional methods. This study examines various scenarios where the implementation of advanced techniques has led to a reduction in
In today's world, the electric vehicle (EV) industry is experiencing a remarkable boom with increasing global demand. With it, comes the surging and unprecedented need for EV batteries. Recycling these batteries has become of crucial importance, as it not only plays a vital role in ensuring the security of the battery supply chain but also serves as a key measure for reducing greenhouse gas emissions. However, there are still several issues that remain unresolved in this domain. Unsettled Issues Regarding Electric Vehicle Battery Recycling delves deep into these issues, thoroughly exploring the current state of the industry and potential solutions to drive sustainable EV battery recycling. By addressing these challenges, we can strive towards a more sustainable future in the EV sector. Click here to access the full SAE EDGETM Research Report portfolio.
LIDAR-based autonomous mobile robots (AMRs) are gradually being used for gas detection in industries. They detect tiny changes in the composition of the environment in indoor areas that is too risky for humans, making it ideal for the detection of gases. This current work focusses on the basic aspect of gas detection and avoiding unwanted accidents in industrial sectors by using an AMR with LIDAR sensor capable of autonomous navigation and MQ2 a gas detection sensor for identifying the leakages including toxic and explosive gases, and can alert the necessary personnel in real-time by using simultaneous localization and mapping (SLAM) algorithm and gas distribution mapping (GDM). GDM in accordance with SLAM algorithm directs the robot towards the leakage point immediately thereby avoiding accidents. Raspberry Pi 4 is used for efficient data processing and hardware part accomplished with PGM45775 DC motor for movements with 2D LIDAR allowing 360° mapping. The adoption of LIDAR-based AMRs
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