In the automotive industry, multiple predictive maintenance units run behind the scenes in every production process to support significant product development, particularly among Accessories Manufacturers (AMs). As a result, they wish to maintain a positive relationship with vehicle manufacturers by providing 100 percent quality assurances for accessories. This is only achievable if they implement an effective anticipatory strategy that prioritizes quality control before and after product development. To do this, many sensors devices are interconnected in the production area to collect operational data (humanity, viscosity, and force) continuously received from machines and sent to backend computers for control operations and predictive analysis. As a result, there is a vast volume of data that may be processed further to obtain accurate information on equipment processing speed and production efficiency. However, extracting details in the essential format for data-driven decision support for predictive maintenance is problematic. As a result, an effective predictive maintenance approach based on Machine Learning (ML) methods is established. It has an impact on the Hybrid Machine Learning (HML) model, which blends supervised and unsupervised learning. It helps to forecast breakdowns and production line deviations ahead of time, preventing the manufacturing unit from shutting down. The proposed predictive methodology has been tested in terms of earlier anomaly detection, production line accuracy & machinery efficiency and compared with other existing ML based predictive maintenance approaches.

Thin sheets covering the skeleton of the railway coach shell undergo severe deformations during the manufacturing process. A comprehensive three-dimensional finite element model has been developed to simulate the manufacture of the complete coach shell assembly. The analysis has been extended by varying the camber, the self-weight of the coach shell and the thickness of the side panel sheet, to identify the exact cause for the coach shell deformation. Camber, a positive deflection given intentionally to compensate for the sagging of the coach, is one of the very important design parameters in the manufacture of railway coaches. A three-step analysis is suggested to calculate the correct camber. It has been demonstrated that the camber value arrived at by the conventional method is not accurate and a correction factor is required. The welding of the side panel to the underframe is a crucial determinant of camber. This issue is addressed through a comprehensive finite element analysis.

Manual metal arc welding of the thin sheet sidepanel subassemblies of a railway coach shell causes deformation on the exterior surface. Welding creates residual stresses and distortions because of the elastoplastic response of the sheet to the transient, localized thermal expansion and contraction. Various strategies minimize the distortion, but a basic understanding of the mechanism is required. The welding process has been mathematically modelled using a general-purpose finite element code ABAQUS. The transient temperature fields and the associated stress, strain and strain fields have been calculated by a coupled thermomechanical analysis. To improve the exterior appearance of the coach shell, a technique called the magnetic forming process has been proposed. This process is designed to restore the original geometry of the deformed sidepanel sheet. The effectiveness of this process has been studied in detail. The magnetic forming process, simulated to flatten and straighten the thin sheet, is calculated to yield good results.

This research paper discusses about the design and analysis of suspension strut used in automotive industries. The main objective of this research is to study the suspension strut by modelling using solid works and then analysis have been performed using ANSYS by considering structural steel, carbon fiber and E-Glass material. Based on the results obtained from the analysis, comparison have been made for above materials to reduce the weight of the suspension strut to improve the life of the model. From the analysis results it can be concluded that it is possible to reduce the weight of suspension strut using composite materials. From the above study it can be concluded that glass fiber has performed well as compared to other materials which in turn increases the life of the suspension strut.