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

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Foundations of Computing and Decision Sciences Pub Date : 2020-12-01 DOI:10.2478/fcds-2020-0017
M. K. Roy, I. Shivakoti, R. Phipon, Ashis Sharma
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引用次数: 5

Abstract

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.
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基于DEA和TOPSIS的激光加工聚合物材料选择的整体方法
摘要激光加工技术已被广泛应用于各种材料的切割,聚合物板材的切割也不例外。工艺可靠性高,成品质量好,使得激光切割机广泛应用于高分子材料的切割。早期的研究人员研究了二氧化碳激光切割到厚度从2mm到10mm不等的几种热塑性聚合物。本文提出了一种方法,用于对聚合物材料的适用性进行分级,并根据使用多决策(MCDM)方法获得的权重来回答LBM切割的选择问题。并尝试用前人的实验结果来验证由此得到的结果。分析包括使用数据包络分析(DEA)的非参数线性规划方法进行过程效率评估,结合通过与理想溶液(TOPSIS)的相似性来选择聚合物片材的顺序偏好技术,这是基于接近值的。这种独特的混合分析结果表明,对于3mm厚的聚合物片材,聚丙烯(PP)比聚乙烯(PE)和聚碳酸酯(PC)更可取。而事实证明,对于5mm厚的聚合物片材,聚碳酸酯(PC)比其他两种聚合物更可取。因此,目前的研究分析对3mm厚的聚合物片材非常适合,而对5mm厚的聚合物片材则略有偏差。
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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