Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: A critical review
TL;DR: In this article, a literature review is conducted to classify the articles on EWM applications in machining operations, which included 65 academic articles from different journals, books, and conferences since the year 2009.
Abstract: Machining operation optimization improves the quality of the product, reduces cost, enhances overall efficiency by reducing human error, and enables consistent and efficient operation. It is a vital decision-making process and achieves the best solution within constraints. It reduces reliance on machine-tool technicians and handbooks to identify cutting parameters, as a lack of awareness of the optimal combination of machining parameters leads to several machining inefficiencies. Subsequently, the optimization of the machining process is more useful for units of production, particularly machining units. In multi-objective optimization (MOO) problems, weights of importance are assigned, mostly identical. But, nowadays, the weights assignment techniques have received a lot of consideration from the professionals and researchers in MOO problems. Various techniques are developed to assign weights of significance to responses in MOO. The Entropy weights method (EWM) continues to work pleasingly across diverse machining operations to allocate objective weights. In this paper, a literature review is conducted to classify the articles on EWM applications in machining operations. The categorization proposal for the EWM reviews included 65 academic articles from different journals, books, and conferences since the year 2009. The EWM applications were separated into 18 categories of conventional and non-conventional machining operations. The implementation procedure of EWM is presented with an example along with method development. Scholarly articles in the EWM applications are further inferred based on (1) implementation of EWM in different machining operations, (2) MOO methods used with entropy weights in machining operations, (3) application of entropy weights by citation index and publication year, and (4) entropy weights applications in other fields. The review paper provided constructive insight into the EWM applications and ended with suggestions for further research in machining and different areas.
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TL;DR: A simple multi-criteria decision-making (MCDM) methodology based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is presented to choose an industrial robot for the arc welding operation and showed that the MCDM approaches for robot selection are quite useful.
74 citations
TL;DR: An inclusive analysis of 268 documents about specific energy consumption (SEC) in machining operations from 2001 to 2020 in the Scopus database is made to deliver an inclusive perception for the scholars working in this field.
Abstract: This paper’s persistence is to make an inclusive analysis of 268 documents about specific energy consumption (SEC) in machining operations from 2001 to 2020 in the Scopus database. A systematic approach collects information on SEC documents’ primary data; their types, publications, citations, and predictions are presented. The VOSviewer 1.1.16 and Biblioshiny 2.0 software are used for visualization analysis to show the progress standing of SEC publications. The selection criteria of documents are set for citation analysis. The ranks are assigned to the most prolific and dominant authors, sources, articles, countries, and organizations based on the total citations, number of documents, average total citation, and total link strength. The author-keywords, index-keywords, and text data content analysis has been conducted to find the hotspots and progress trend in SEC in machining operations. The most prolific and dominant article, source, author, organization, and country are Anderson et al. “Laser-assisted machining of Inconel 718 with an economic analysis”, the Int J Mach Tools Manuf, Shin Y.C., form Purdue University Singapore, and United States, respectively, based on total citations as per defined criteria. The author keywords “specific cutting energy” and “surface roughness” dominate the machining operations SEC. SEC’s implication in machining operations review and bibliometric analysis is to deliver an inclusive perception for the scholars working in this field. It is the primary paper that utilizes bibliometric research to analyze the SEC in machining operations publications expansively. It is valuable for scholars to grasp the hotspots in this field in time and help the researchers in the SEC exploration arena rapidly comprehend the expansion status and trend.
39 citations
TL;DR: In this paper , a simple multi-criteria decision-making (MCDM) methodology based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is presented to choose an industrial robot for the arc welding operation.
37 citations
TL;DR: In this paper, a multi-regression model with standardized regression weights was used to identify significant power quality predictors for active power evaluation for machining operations and highlighted the significance of electric parameters as predictors in power estimation.
Abstract: Increasing the energy efficiency of machining operations can contribute to more sustainable manufacturing. Therefore, there is a necessity to investigate, evaluate, and optimize the energy consumed during machining operations. The research highlights a method employed to prioritize the most energy-intensive machining operation and highlights the significance of electric parameters as predictors in power estimation of machining operations. Multi regression modeling with standardized regression weights was used to identify significant power quality predictors for active power evaluation for machining operations. The absolute error and the relative error both decreased when the active power was measured by the power analyzer for each of the identified machining operations, compared to the standard power equation and that obtained from the modeled regression equations. Furthermore, to determine energy-intensive machining operation, a hybrid decision-making technique based on TOPSIS (a technique for order preference by similarity to ideal solution) and DoM (degree of membership) was utilized. Allocation of weights to energy responses was carried out using three methods, i.e., equal importance, entropy weights, and the AHP (analytical hierarchy process). Results revealed that a drilling process carried out on material ST 52.3 is energy-intensive. This accentuates the significance of electric parameters in the assessment of active power during machining operations.
32 citations
TL;DR: In this paper, a review of the materials and machine parameters associated with FDM printing and highlights the effect of the mechanical and thermal properties of the material on the durability and strength of the printed objects.
24 citations
References
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TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Abstract: In this final installment of the paper we consider the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now. To a considerable extent the continuous case can be obtained through a limiting process from the discrete case by dividing the continuum of messages and signals into a large but finite number of small regions and calculating the various parameters involved on a discrete basis. As the size of the regions is decreased these parameters in general approach as limits the proper values for the continuous case. There are, however, a few new effects that appear and also a general change of emphasis in the direction of specialization of the general results to particular cases.
65,425 citations
Book•
01 Jan 1986
TL;DR: In this article, the authors present an integrative presentation of the principles of decision analysis in a behavioral context, including sensitivity analysis, value-utility distinction, multistage inference, attitudes toward risk, and attempt to make intuitive sense out of what have been treated in the literature as endemic biases and other errors of human judgement.
Abstract: Decision analysis is a technology designed to help individuals and organizations make wise inferences and decisions. It synthesises ideas from economics, statistics, psychology, operations research, and other disciplines. A great deal of behavioural research is relevant to decision analysis; behavioural scientists have both suggested easy and natural ways to describe and quantify problems and shown the kind of errors to which unaided intuitive judgements can lead. This long-awaited book offers the4first integrative presentation of the principles of decision analysis in a behavioural context. The authors break new ground on a variety of technical topics (sensitivity analysis, the value-utility distinction, multistage inference, attitudes toward risk), and attempt to make intuitive sense out of what have been treated in the literature as endemic biases and other errors of human judgement. Those interested in artificial intelligence will find it the easiest presentation of hierarchical Bayesian inference available.
2,616 citations
Book•
05 Apr 1999
TL;DR: This book discusses Geographical Data, Information, and Decision Making, and Multicriteria Decision Analysis, as well as Spatial Decision Support Systems, which addresses the role of spatial data and information in decision making.
Abstract: PRELIMINARIES. Geographical Data, Information, and Decision Making. Introduction to GIS. Introduction to Multicriteria Decision Analysis. SPATIAL MULTICRITERIA DECISION ANALYSIS. Evaluation Criteria. Decision Alternatives and Constraints. Criterion Weighing. Decision Rules. Sensitivity Analysis. MULTICRITERIA-SPATIAL DECISION SUPPORT SYSTEMS. Spatial Decision Support Systems. MC-SDSS: Case Studies. Glossary. Selected Bibliography. Indexes.
2,342 citations
01 Jan 1981
TL;DR: There are some classical decision rules such as dominance, maximin and maximum which are still fit for the MADM environment but they do not require the DM’s preference information, and accordingly yield the objective (vs. subjective) solution.
Abstract: There are some classical decision rules such as dominance, maximin and maximum which are still fit for the MADM environment. They do not require the DM’s preference information, and accordingly yield the objective (vs. subjective) solution. However, the right selection of these methods for the right situation is important. (See Table 1.3 for references).
1,890 citations
TL;DR: In this article, the authors reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation.
Abstract: Multi-criteria decision analysis (MCDA) methods have become increasingly popular in decision-making for sustainable energy because of the multi-dimensionality of the sustainability goal and the complexity of socio-economic and biophysical systems. This article reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation. The criteria of energy supply systems are summarized from technical, economic, environmental and social aspects. The weighting methods of criteria are classified into three categories: subjective weighting, objective weighting and combination weighting methods. Several methods based on weighted sum, priority setting, outranking, fuzzy set methodology and their combinations are employed for energy decision-making. It is observed that the investment cost locates the first place in all evaluation criteria and CO2 emission follows closely because of more focuses on environment protection, equal criteria weights are still the most popular weighting method, analytical hierarchy process is the most popular comprehensive MCDA method, and the aggregation methods are helpful to get the rational result in sustainable energy decision-making.
1,868 citations