This research study employs a comparative Multi-Criteria Decision-Making (MCDM) approach to select optimal thermoplastic materials for hybrid vehicle battery packs in the automotive industry, addressing the challenges posed by high-temperature environments. Through a detailed evaluation of materials based on criteria such as thermal stability, mechanical strength, chemical resistance, and environmental impact, the research identifies materials that enhance battery efficiency, longevity, and vehicle performance. Utilizing SWARA-ARAS, SWARA-EDAS, and SWARA-TOPSIS methods, the study systematically assesses and ranks various polymers, providing recommendations that prioritize safety, performance, and sustainability. The findings offer valuable insights for manufacturers in making informed material selection decisions, contributing to the advancement of sustainable automotive technologies. This research not only highlights the importance of material selection in the context of hybrid vehicle battery packs but also sets a foundation for future studies to explore emerging materials and decision-making frameworks, aiming to further enhance the efficiency and sustainability of hybrid vehicles.

This study explores the impact of CO ₂ laser cutting parameters on surface roughness and kerf width of 3D-printed Carbon Fiber reinforced Polylactic Acid (PLA-CF) composites while developing phenomenological models using hybrid artificial intelligence techniques. PLA-CF composites possess certain mechanical properties and surface quality. The surface roughness and kerf width values were measured under different laser cutting conditions (such as plate thickness, power, and cutting speed) and were predicted using multiple linear regression, particle swarm optimization-based adaptive neuro fuzzy inference system, and ant colony optimization-based adaptive neuro fuzzy inference system models. Experimental results showed that kerf width and surface roughness are influenced significantly by laser cutting parameters, showing the importance of accurately selecting the parameters. The most dominant factor entered the model as cutting speed: as cutting speed was increased, kerf width decreased, but higher levels of power resulted in kerf width. Thickness provided a non-linear input: kerf width decreased from 2 to 2.5 mm, then increased to 4 mm. The least kerf width (0.809 mm) was obtained at 90 W power and 9 mm/s cutting speed, with 2.5 mm thickness. Surface roughness increased with cutting speed and power, with minimum surface roughness (1.878 µm) at 2 mm thickness and 90 W power with 3 mm/s cutting speed. Among the hybrid artificial intelligence models, the particle swarm optimization-based adaptive neuro fuzzy inference system model gave the best accuracy, achieving the lowest mean squared error and highest correlation coefficient, whereas the ant colony optimization-based adaptive neuro fuzzy inference system model performed better than multiple linear regression but not better than particle swarm optimization. These results, therefore, validate the applicability of hybrid artificial intelligence models for predicting surface quality and kerf width during CO ₂ laser cutting.

This research study with an extensive literature review represents a comprehensive multi-criteria analysis for optimizing the cutting parameters in the manufacturing of flexible two-phase passive thermal management systems (FTP-TMS) using thermoplastic materials. Recognizing the critical role of thermoplastics in FTP-TMS due to their inherent flexibility and lightweight properties, this research focuses on the precision cutting of polypropylene, polyethylene, and polyvinyl chloride using CO₂ laser technology. The study is structured into three distinct phases. Initially, an experimental setup was conducted to cut 2 mm thick thermoplastic materials with varying power and cutting speed parameters. Subsequently, the SWARA method was employed to weight the criteria, followed by the application of seven different multi-criteria decision-making (MCDM) methods for optimization. The final phase involved a detailed analysis of the outputs, including ranking, correlation, and sensitivity analyses. The findings indicate that cutting polypropylene with a 90 W power setting and a speed of 15 mm s⁻¹ yields the most optimal results. This study fills a significant gap in the existing literature by providing a dedicated analysis for thermoplastics in FTP-TMS manufacturing. The insights gained are pivotal for standardizing manufacturing practices and enhancing the design and fabrication of flexible thermal management solutions, offering substantial benefits to sectors like electronics, aerospace, and automotive industries.

This experimental study examines how cutting parameters influence surface roughness (Ra), kerf width (KW), heat-affected zone (HAZ), and material removal rate (MRR) during CO ₂ laser cutting of polyethylene (PE) material. The aim is to determine the optimal cutting conditions by integrating fuzzy analytic hierarchy process (AHP) and multi-criteria decision-making (MCDM) methods. Control factors (i.e. focal length, power, and cutting speed) were varied in a full factorial experimental design. Criterion weights for the responses were calculated with the help of the AHP method in fuzzy environment, resulting in weights of 0.442 for Ra, 0.102 for KW, 0.287 for HAZ, and 0.169 for MRR. These weights were integrated into MCDM methods (i.e. combined compromise solution [CoCoSo], technique for order preference by similarity to ideal solution [TOPSIS], and Vlsekriterijumska Optimizacija I Kompromisno Resenje [VIKOR]) to rank the experiments and identify the optimal parameters. The optimal cutting condition was found to be a focal length of 8 mm, power of 90 W, and cutting speed of 12 mm/s. Spearman's rank correlation coefficients were applied for analysis of the consistency among the hybrid approaches, with results indicating a very strong positive relationship between them. This comprehensive approach ensures precise and efficient laser cutting of PE, contributing valuable insights to the field of manufacturing and materials processing.

Additive manufacturing, particularly Fused Filament Fabrication (FFF), provides notable advantages such as design flexibility and efficient material usage. However, components produced via FFF often exhibit suboptimal surface quality and dimensional inaccuracies. Acrylonitrile Butadiene Styrene (ABS), a widely used thermoplastic in FFF applications, commonly necessitates post-processing to enhance its surface finish and dimensional precision. This study investigates the effects of CO₂ laser cutting on FFF-printed ABS plates, focusing on surface roughness, top and bottom kerf width, and bottom heat-affected zone. Forty-five experimental trials were conducted using different combinations of plate thickness, cutting speed, and laser power. Measurements were analysed statistically, and analysis of variance was applied to determine the significance of each parameter. To enhance prediction capabilities, seven machine learning models—comprising traditional (Linear Regression and Support Vector Regression), ensemble (Extreme Gradient Boosting and Random Forest), and deep learning algorithms (Long Short-Term Memory (LSTM), LSTM-Gated Recurrent Unit (LSTM-GRU), LSTM-Extreme Gradient Boosting (LSTM-XGBoost))—were developed and compared. Among these, the LSTM-GRU model achieved the highest predictive performance across all output metrics. Results show that cutting speed is the dominant factor affecting cutting quality, followed by laser power and thickness. The proposed experimental-computational approach enables accurate prediction of laser cutting outcomes, facilitating optimisation of post-processing strategies for 3D-printed ABS parts and contributing to improved precision and efficiency in polymer-based additive manufacturing.

In this study, aluminium matrix hybrid composites reinforced with boron carbide (B₄C) and silicon carbide (SiC) were fabricated using the direct hot-pressing technique under 35 MPa pressure at 600 °C for 5 min. Particle size distribution and scanning electron microscope analysis were conducted for the input powders. The microstructure, mechanical properties, and drillability of the fabricated composites were examined. As the SiC content increased, the density decreased, hardness improved, and transverse rupture strength declined. Drilling experiments were performed based on the Taguchi L₁₈ orthogonal array. The control factors included cutting speed (25 and 50 m/min), feed rate (0.08, 0.16, and 0.24 mm/rev), point angle (100°, 118°, and 136°), and SiC content (0%, 5%, and 10%). Quality characteristics such as thrust force, torque, surface quality indicators, diameter deviation, and circularity deviation were evaluated. The Taguchi method was applied for single-response optimization, while the Entropy-weighted, Taguchi-based CoCoSo method was used for multi-response optimization. Analysis of Variance was conducted to assess factor significance, and regression analysis was used to model relationships between inputs and responses, yielding high R² values. The optimal drilling performance was achieved at 50 m/min, 0.08 mm/rev, 136°, and 10% SiC, and the confirmation tests verified these results within the 95% confidence interval.