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Browse AllThe 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
Predictive maintenance is crucial for Industry 4.0, and deep neural networks are a promising approach for predicting the capacity of electric batteries. However, few applications effectively utilize neural networks for this purpose with lithium-ion batteries. In this work, different deep learning models are developed, starting with simple neural networks, dense neural networks, convolutional networks, and recurrent networks. Using a public domain dataset, training, testing, and validation datasets were generated to predict battery capacity as a function of the number of cycles. Despite the limited number of samples in the dataset, deep learning techniques are employed to ensure robust prediction performance. The work presents the loss functions for each iteration of the algorithms and the average absolute error. The models made good generalizations over the test dataset within a short prediction time window. Finally, the work presents an average absolute error below 0.3, ensuring good
A bench was developed with the aim of making it possible to test direct injection fuel system of low-displacement engines (up to 2,000cc) outside of a conventional test bench. It has adjustable supports that make it possible to install various engines of different manufacturers. In addition, the bench has features an electric motor, an external oil pumping system and a programmable ECU. These accessory systems were necessary because the engine for which the bench was initially designed has undergone various adaptations that required external systems such as those mentioned above. The project was designed to provide great ease, agility and low manufacturing costs, so the entire bench chassis was manufactured using just one standardized steel profile that is easily found on the market. Still about manufacturing, the concept of the prototype was also developed around the need for it to be compact and easy to transport so that the tests could be carried out in different environments in an