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.

/pdf/prioritizing-energy-intensive-machining-operations-and-2riww099tk.pdf

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

This paper aims to comprehensively review 891 documents in the Scopus database about Internet of Things (IoT) in Ind 4.0 to understand the historical growth, current state, and potential expansion trend. From 2014 to 2020, a systematic methodology gathered information on IoT in Ind 4.0 documents in various Scopus databases. The relevant IoT research in Ind 4.0 documents is provided, and their types, publications, citations, and predictions are discussed. The VOSviewer 1.16.6 and Biblioshiny 2.0 applications display IoT status in Ind 4.0 publications for visualization research. The citation review aims to find the most prominent and influential authors, sources, papers, countries, and organizations. For citation analysis and ranking, document selection criteria were well defined. The author keywords, index keywords, and text data content analysis were used to identify the hotspots and development trends. The yearly IoT in Ind 4.0 article publications presented a speedily increasing trend, and a curve was fitted employing an exponential function. The paper “Intelligent manufacturing in the context of Industry 4.0: a review” was rated first with 754 citations. With 1629 citations, the “International Journal of Production Research” was ranked #1 along with Wan J. The South China University of Technology in Guangzhou, China, was placed first along with the United States as the most prolific and influential country. ‘Industry 4.0’ appeared the first time in 2014 with an application of IoT in Ind 4.0 with an overall appearance of 528, followed by the ‘internet of things’ in 2015, three times with a total count of 220 up to 2020. The IoT in Ind 4.0 assessment and bibliometric analysis intended to provide intellectuals a broad perspective working in IoT in Ind 4.0. Scholars should understand the hotspots in this area as soon as possible. This is also the first paper to thoroughly use bibliometric research to examine the IoT in Ind 4.0 literature. It will assist researchers of IoT in Ind 4.0 in expanding their knowledge and quickly comprehending the development status and pattern.

/pdf/exploring-the-application-sphere-of-the-internet-of-things-2lhy8if5.pdf

Exploring the Application Sphere of the Internet of Things in Industry 4.0: A Review, Bibliometric and Content Analysis

Smart watches: A review of evolution in bio-medical sector

Bibliometric analysis of entropy weights method for multi-objective optimization in machining operations

Improving energy performance has been recognized as an effective measure to promote the energy saving and emission reduction and to realize the sustainable development. Methods of improving energy performance towards sustainable manufacturing are numerous and scattered, resulting in insufficiency from the perspective of overall strategies and system integration. After the intensive selection of advanced literatures, about 166 research papers directly related to energy performance improvement are analyzed. A comprehensive review and analysis from multi-perspectives of energy monitoring, evaluation, optimization and benchmarking are performed, which is conducive to understand the energy consumption pattern and take effective energy-saving measures. This paper establishes a framework of energy performance integrated method, and four energy management methods and their energy models in energy monitoring, evaluation, optimization and benchmarking phase are analyzed and summarized. Besides, the proposed framework analyzes potential applications and key advantages, and some challenges and potential opportunities for energy performance improvement methods are discussed. This study will provide a significant foundation for energy-efficient production through various methods application. 

A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking

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.

https://www.mdpi.com/2071-1050/13/10/5617/pdf

Bibliometric Analysis of Specific Energy Consumption (SEC) in Machining Operations: A Sustainable Response

MULTI-OBJECTIVE OPTIMIZATION OF CUTTING PARAMETERS TO MINIMIZE ENERGY CONSUMPTION IN DRY TURNING OF AISI 4140 ALLOY STEEL Avilash Sharma Sehijpal Singh Ardamanbir Singh Sidhu Abstract Manufacturing processes consume enormous amounts of electrical energy. Electrical energy generation uses fossil fuels. Reduction of energy consumption of manufacturing processes can considerably reduce environmental impact and also benefit manufacturer economically. Machining is one of the major manufacturing process.It is important to optimize the energy consumption of machining process while maintaining the quality parameters. Present work utilizes Response Surface Methodology (RSM) to optimize and study the effect of cutting parameters (cutting speed, feed rate and depth of cut) on Specific Energy Consumption (SEC), surface finish and tool wear during dry turning of AISI 4140 alloy steel. Results reveal that minimum SEC was achieved at higher levels of all cutting parameters. In case of tool wear, minimum tool wear was achieved at lower levels of cutting parameters. The most optimal solution for minimum SEC, surface roughness and tool wear was achieved at cutting speed equal 136.715 m/min, feed rate equal to 0.160 mm/rev and depth of cut equal to 1.168 mm.

http://iiie-iej.ivyscientific.org/wp-content/uploads/sites/4/published_papers/11/6/1071/paper_1071.pdf

Ardamanbir Singh Sidhu

Papers

Bibliometric Analysis of Specific Energy Consumption (SEC) in Machining Operations: A Sustainable Response

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

Bibliometric analysis of entropy weights method for multi-objective optimization in machining operations

Multi-objective optimization of cutting parameters to minimize energy consumption in dry turning of aisi 4140 alloy steel

Optimization and modelling of active power consumption of ST52.3 alloy steel during a drilling operation