Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study

Ardamanbir Singh Sidhu, Sehijpal Singh, Raman Kumar, Danil Yurievich Pimenov and Khaled Giasin
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Ardamanbir Singh Sidhu: Department of Mechanical Engineering, I. K. Gujral Punjab Technical University, Jalandhar 144603, India
Sehijpal Singh: Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana 141006, India
Raman Kumar: Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana 141006, India
Danil Yurievich Pimenov: Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, 454080 Chelyabinsk, Russia
Khaled Giasin: School of Mechanical and Design Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK

Energies, 2021, vol. 14, issue 16, 1-39

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.

Keywords: machining operations; electric parameters; active power; active energy; specific energy consumption; energy efficiency; TOPSIS; entropy weight; AHP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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