Selection of optimal polymer joining technology: Insights and decision support through interrelated case studies using CARCACS method

Polymer materials play an essential role in the automotive, aerospace, packaging, and healthcare industries, necessitating reliable joining processes to ensure structural integrity, functionality, and cost efficiency. Choosing the optimal polymer joining technology involves evaluating various criteria, such as design freedom, investment, joint quality, process adaptability, cycle time, etc., for prioritization and informed decision-making. This study aims to systematically assess polymer joining processes to improve efficiency and sustainability in industrial applications. Two interrelated case studies are conducted using the CARCACS (“ Criteria-wise Alternative Ranking and Correlation Analysis for Composite Scoring ”) method. The first case study identifies laser transmission welding as the most suitable joining process among eight commonly used processes based on multiple evaluation criteria. The second case study refines this selection by examining various laser welding systems, with fibre lasers emerging as the optimal choice. Validation through comparison with other MCDM (Multi-Criteria Decision-Making) methods and Spearman's rank correlation coefficients reveals strong agreement and robust decision-making. Sensitivity analysis further confirms the stability of the top-ranked alternatives across different scenarios. This research provides a robust framework for selecting polymer joining technologies and demonstrates the practical implications for optimizing these technologies in industrial applications. • Prioritized polymer joining technologies using the CARCACS method for informed decision-making. • Two novel polymer joining case studies are used sequentially with multiple alternatives and criteria. • The first case study identifies a suitable joining process; the second explores the system for execution. • Results are validated by comparing with other MCDM methods and Spearman's rank correlation. • Sensitivity analyses confirm robust decision-making and resilience of top-ranked alternatives.