The fused deposition modelling (FDM) technique involves the deposition of a fused layer of material according to the geometry designed in the software. Several parameters affect the quality of parts produced by FDM. This paper investigates the effect of FDM printing process parameters on tensile strength, impact strength, and flexural strength. The effects of process parameters such as printing speed, layer thickness, extrusion temperature, and infill percentage are studied. Polyactic acid (PLA) was used as a filament material for printing test specimens. The experimental layout is designed according to response surface methodology (RSM) and responses are collected. Specimens are prepared for testing of these parameters as per ASTM standards. A mathematical model for each of the responses is developed based on the nonlinear regression method. The desirability approach, nonlinear regression, as well as experimental values are in close agreement with each other. The desirability approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 3.109, 6.532, and 3.712, respectively. The nonlinear regression approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 2.977, 6.532, and 3.474, respectively. The desirability concept and nonlinear regression approach resulted in the best mechanical property of the FDM-printed part.

Additive manufacturing enables the design of lightweight, high-performance composites customised for specific mechanical applications. This study aims to enhance the impact resistance of 3D-printed Onyx-Kevlar fiber-reinforced composites by optimizing printing parameters and predictive machine learning (ML) modeling. A Taguchi L27 orthogonal array was used to systematically examine the influence of fiber orientation, volume fraction, infill density, and pattern on impact performance. Statistical analysis identified fiber volume fraction and infill density as the most significant factors. To reduce experimental load and enable accurate property estimation, supervised ML models, Support Vector Regression (SVR), and Linear Regression, were developed and trained on experimental data. Both models demonstrated high predictive performance, with R² values of 0.9166 and 0.9747, respectively. Model accuracy was further validated through learning curves, residual analysis, and performance metrics. Scanning Electron Microscopy (SEM) provided microstructural insights into fracture behavior, correlating with model predictions. he combined approach provides a reliable and efficient method to optimize composite performance with less trial-and-error experimentation. This study demonstrates the utility of predictive ML in guiding material design and highlights its applicability in developing impact-resistant composites for protective and structural engineering applications.

Electric, hybrid, and fuel cell vehicles are the future of the automobile industry, and power source design is one of the most crucial steps in designing these vehicles. This paper aims to design and structurally simulate a custom accumulator—which powers an electric vehicle, for a lightweight, single-seater formula-style racecar. The work is dependent on the model-based design and CAD model approach. Mathematical modeling on SCILAB is used to model equations to get the characteristics of the accumulator, such as the energy, capacity, current, voltage, state of charge, and discharge rates. The output of this model gives the configuration of the battery pack as several cells in series and parallel to adequately power the tractive system. An accumulator container is designed to safeguard the cells from external impacts and vibrational loads, which otherwise can lead to safety hazards. Following this, the Finite Element Analysis (FEA) performed on the accumulator resulted in maximum peak deformation of 0.56 mm, ensuring the safety check against various external loads. Further, the finer stability of the battery pack was virtually validated after performing the vibrational analysis, resulting in a deformation of 3.5493 mm at a 1760.8 Hz frequency.