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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.
Ardamanbir Sidhu; Sehijpal Singh; Raman Kumar; Danil Pimenov; Khaled Giasin. Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study. Energies 2021, 14, 4761 .
AMA StyleArdamanbir Sidhu, Sehijpal Singh, Raman Kumar, Danil Pimenov, Khaled Giasin. Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study. Energies. 2021; 14 (16):4761.
Chicago/Turabian StyleArdamanbir Sidhu; Sehijpal Singh; Raman Kumar; Danil Pimenov; Khaled Giasin. 2021. "Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study." Energies 14, no. 16: 4761.
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.
Raman Kumar; Sehijpal Singh; Ardamanbir Sidhu; Catalin Pruncu. Bibliometric Analysis of Specific Energy Consumption (SEC) in Machining Operations: A Sustainable Response. Sustainability 2021, 13, 5617 .
AMA StyleRaman Kumar, Sehijpal Singh, Ardamanbir Sidhu, Catalin Pruncu. Bibliometric Analysis of Specific Energy Consumption (SEC) in Machining Operations: A Sustainable Response. Sustainability. 2021; 13 (10):5617.
Chicago/Turabian StyleRaman Kumar; Sehijpal Singh; Ardamanbir Sidhu; Catalin Pruncu. 2021. "Bibliometric Analysis of Specific Energy Consumption (SEC) in Machining Operations: A Sustainable Response." Sustainability 13, no. 10: 5617.