A Holistic Approach to Polymeric Material Selection for Laser Beam Machining using Methods of DEA and TOPSIS

Abstract

Laser Beam machining (LBM) nowadays finds a wide acceptance for cutting various materials and cutting of polymer sheets is no exception. Greater reliability of process coupled with superior quality of finished product makes LBM widely used for cutting polymeric materials. Earlier researchers investigated the carbon dioxide laser cutting to a few thermoplastic polymers in thickness varying from 2mm to 10mm. Here, an approach is being made for grading the suitability of polymeric materials and to answer the problem of selection for LBM cutting as per their weightages obtained by using multi-decision making (MCDM) approach. An attempt has also been made to validate the result thus obtained with the experimental results obtained by previous researchers. The analysis encompasses the use of non-parametric linear-programming method of data envelopment analysis (DEA) for process efficiency assessment combined with technique for order preference by similarity to an ideal solution (TOPSIS) for selection of polymer sheets, which is based on the closeness values. The results of this uniquely blended analysis reflect that for 3mm thick polymer sheet is polypropelene (PP) to be highly preferable over polyethylene (PE) and polycarbonate (PC). While it turns out to be that polycarbonate (PC) to be highly preferable to other two polymers for 5mm thick polymer sheets. Hence the present research analysis fits very good for the polymer sheets of 3mm thickness while it deviates a little bit for the 5mm sheets.

Multi-Objective Optimization of Cutting Parameters for Polyethylene Thermoplastic Material by Integrating Data Envelopment Analysis and SWARA-Based CoCoSo Approach

“…AHP facilitates the determination of the comparative importance of different criteria, while TOPSIS assists in ranking the alternatives based on their performance relative to these criteria. The amalgamation of AHP and TOPSIS offers a holistic framework for decision-making, enabling effective parameter optimization and the realization of the desired surface roughness in thermoplastic materials (Roy et al, 2020). In fact, metaheuristic-based ANFIS applications have been implemented to accurately predict surface roughness, as demonstrated by Guvenc et al (2022).…”

Selection of Landfill Cover Materials Based on Data Envelopment Analysis (DEA)—A Case Study on Four Typical Covering Materials

“…First proposed by Charnes [30], DEA is now widely used, among others, in business decision making, technology evaluation, and especially in material selection, where it has been successfully applied across a number of different contexts [31,32]. Based on DEA, Dickson [33] used an evaluation index system in the selection of suppliers of construction materials, allowing the suppliers to be ranked according to selected indicators.…”

Polymeric Materials Selection for Flexible Pulsating Heat Pipe Manufacturing Using a Comparative Hybrid MCDM Approach

“…Data envelopment analysis (DEA), AHP, and TOPSIS methods were integrated to determine the most suitable polymeric material for cutting, based on the output parameters obtained from the CO 2 laser-cutting process of three different polymeric materials (i.e., polyethylene, polycarbonate, and polypropylene). It was mentioned that polymeric materials for laser beam machining might be selected, depending on material type and thickness [28]. Only the preference selection index (PSI) was applied to specify the best polymer matrix composite (PMC) that was resistant to impact loading.…”