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

: 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.


Introduction
Extraordinary conditions prevailed in 2020 due to the coronavirus pandemic. The situation was unparalleled. CO 2 emissions and energy use charted an unusual path. Worldwide energy demand weakened by 3.8% in the first quarter of 2020 compared with the first quarter of 2019. Annual energy demand in 2020 decreased by 6%. This is the largest ever decrease in energy requirement in the past 70 years. As a result, CO 2 releases fell by 8% nearly. In quantitative terms, the fall in emissions was 2.6 Gigaton (Gt) to reach 30.6 Gt for 2020. It is the lowest level since 2010 [1]. This was mainly due to the reduction in industrial production. This proves the fact that industrial production or manufacturing activity has a significant impact on CO 2 emissions. The fall in the emissions of 2020 may be temporary. Structural changes are required in environmental policies by all governments to sustain the reduction in CO 2 emissions [2]. The specific energy consumption (SEC) is one of the industrial manufacturing responses, especially in the field of machining operations. It is responsible for diminishing energy consumption and CO 2 emissions. This paper is and processing time by using the Taguchi method while turning of AISI 5140 with carbide insert. The optimum specific energy consumption was achieved at 80 m/min. cutting speed, 0.2 mm/rev. feed rate, and 1.25 mm depth of cut, and optimum processing time was achieved at cutting speed of 60 m/min., feed rate of 0.2 mm/rev., and depth of cut 0.75 mm [10]. Warsi et al. [11] developed and analyzed the SEC map for high-speed machining of Al 6061-T6 alloy and reported that it could be used effectively to decide the most energy-efficient cutting parameters for given work material. Cai et al. [12] illustrated the concept, content and scope of the energy benchmarking rules for machining operations research. The machining operation's power efficiency is defined as the processing power ratio to total power consumption and compared the power efficiency of brushing, conventional milling, and microscale drilling employing experimental results. The power efficiency of brushing, traditional milling, and microscale drilling was 23.8%, 7.6%, and 1%, respectively [13]. The authors [13] compared the results reported by Zhao et al. [14], i.e., the energy efficiency comparison of conventional turning process with laser-assisted turning operation, this process at first considered by Skvarenina and Shin [15], performed to study the variations of cutting speed (1.36-2.04 m/sec.), depth of cut (0.5-1 mm), feed (0.075-0.125 mm/rev.), and material removal temperature (26.85-76.85 • C) on tool wear, cutting forces, specific cutting energy, and surface roughness. The laser-assisted turning was performed on a turret lathe with a 1.5 kW CO 2 laser. The SEC by the laser-assisted turning operation was 0.85 J/mm 3 less as compared to the conventional turning process. The tool life, SEC, and surface roughness were improved by 60%, 25%, and 5%, respectively. Balogun et al. [16] anticipated the methodology used to assess machining efficiency based on maximum specific shear energy and derive the specific ploughing energy for minimum tip energy demand. The shearing dominated machining should be utilized to enhance the process efficiency in mechanical machining. Park et al. [17] applied RSM and TOPSIS method to optimize the machining variables; cutting speed (60-300 m/min.), feed rate (0.10-0.16 mm/rev.), nose radius (0.2-0.6 mm), edge radius (20-100 µm), and rake angle (−10 • to 0 • ) of the turning operation for hardened AISI 4140 steel to diminish the cutting energy consumption and to get better energy efficiency. The sustainable machining output parameters, power factor, energy consumption, and energy efficiency were optimized with the Taguchi method. ANOVA was utilized to assesses the impact of turning cutting variables. The results reveal nose radius does not impact too much on energy responses [18]. The sustainable responses were also optimized simultaneously along with material removal rate, surface roughness, and power consumption of the CNC turning machine while utilizing different weight assigned with Entropy and AHP methods [19]. The active power consumption of the CNC turning machine while machining alloy steel has been optimized with the Taguchi method [20]. Li et al. [21] incorporated Taguchi, RSM, and particle swarm optimization to optimize energy saving parameters in vertical milling of AISI 1045 steel. The response specific energy consumption was picked to assess energy efficiency along with processing time. The energy efficiency of finish turning of EN 353 alloy steel was investigated during turning operation [22]. The sustainable machining responses were optimized, such as machining time [23], active power consumption [24], active cutting energy [25]. Kara and Li [26] presented an empirical model of the specific energy consumption; the model was developed based upon eight dissimilar milling and turning machines. Energy consumption while machining was fixed at 1 cm 3 . The developed empirical model anticipates the energy consumption with correctness of more than 90%. A model validation test was conducted on brass and mild steel. The accuracy of the model for brass and mild steel was 91.06% and 95.59%, respectively. Suresh et al. [27] used RSM to develop models of specific cutting force, Ra, power, and wear of tool, although turning of hardened AISI 4340 steel with coated carbide insert. They optimized cutting parameters with the Taguchi method. The ANOVA results revealed that the feed rate has the superior impact on the machining force (53.38%), specific cutting force (70.55%), and Ra (83.79%). The cutting speed has a superior effect on the power consumption (77.67%) and tool wear Sustainability 2021, 13, 5617 4 of 30 (75.75%). To diminish the specific cutting force, upper values of feed rate are required, tool wear and power has a linear relation with cutting speed and feed rate. Guo et al. [28] applied a two-step approach to determining the optimal cutting state with a deviation of speed (60-800 m/min.), feed (0.05-0.3 mm/rev.), and depth of cut (0.5-2 mm) of steel and aluminum with the coated carbide tool under dry conditions. They developed a model for total specific energy consumption and surface roughness with the least square curve fitting method. With a rise in feed rate, the total specific energy consumption declines, smaller diameter yields higher energy consumption because higher RPM requires more power consumption. The total specific energy consumption lowers with increasing depth of cut. The R-square 0.995 and 0.943 for total specific energy consumption and Ra, respectively. They represent the capability of the models developed for steel. Li et al. [29] presented a modified energy consumption model of the milling operation. The statistic modeling was used to obtain the model coefficients based on trial information. The improved model offered a reliable prediction of energy consumption with an accuracy of around 96%. Rsquare value for specific energy consumption, total power during cutting, ideal power demand and air cutting power of 99.98%, 99.45%, 97.80%, and 96.18%, respectively, shows the reliable prediction accuracy of the improved model terms. The new lubricant is used under minimum quantity lubrication for grinding of material Ti-6Al-4V. The authors claim that palm oil containing 0.1% by weight of Graphene nanoplatelets can reduce the specific cutting energy to the extent of 80.25% as compared with conventional cutting fluid LB2000. This is achieved due to reduced cutting forces due to decreased friction because GNP increases contact angles and a viscosity [30]. The authors utilize nitrogen gas as a coolant in turning stainless steel 304 grade on CNC machine tool. They claim that surface finish gets better. There is an increase in material removal rate and a decrease of 2.7% of specific cutting energy at lower speeds [31].
Peng and Xu [32] critically reviewed the energy-efficient machining system by grouping it into four categories, i.e., theoretical, empirical, discrete event-based, and hybrid models. They suggested that it requires an inclusive understanding, as well as optimization of energy consumption. A thorough study of the literature revealed that the SEC stood for the mapping association among energy consumption and process conditions, representing industrial machinery energy efficiency to improve input and output effectiveness [33]. To clarify the concept of development in machining energy, the relevant literature is described using a systematic approach, accompanied by qualitative and content analysis [34]. A thorough review of conceptual and empirical energy models for industrial machinery was carried out. Optimization of cutting parameters has been described as a critical technique for industrial machinery energy efficiency [35]. Yoon suggested a new hierarchical structure for energy management strategies for a machine tool focused on process stages, applicability, and strategic thinking. Yoon [36] proposed a novel hierarchical model for energy efficiency strategies based on process levels, ease of applicability, and decision making for a machine tool.
The bibliometric analysis has been successfully applied to find the research hotspots in many fields including, optimization techniques in metal cutting operations [37], sustainable development research [38], plasma spray coating techniques [39], green manufacturing [40], global carbon footprint [41], industry 4.0 and sustainability [42], managing for sustainability [43], circular economy, and internet of things [44].
The review of literature of SEC in machining operations and review studies on energy in machining reveals that the SEC is a significant sustainable response in machining operations. However, the review studies did not adequately characterize the overall framework of SEC analysis. Therefore, visualization methods are needed to investigate SEC in machining operations. This is necessary to illustrate the progression of the SEC in the machining literature. Two-level research, which included bibliometric and visualization review, has been used to bridge that gap. This study's general intent is to use bibliometric and visualization analysis to investigate the selected literature's options and strategies on SEC in machining operations from 2001 to 2020.

Materials and Methods
To analyze the research trends, keyword and content analysis, etc., of SEC in machining operations, VOSviewer 1.6.16 and Biblioshhiny 2.0 software are used.

Research Data Collection Methodology on Specific Energy Consumption (SEC)
The research data were collected on Scopus as it is an appropriate search engine containing primary citation sources and publishes peer-reviewed journals and conferences. The data collection process was completed on the first of April of 2021. The keywords utilized to collect information of SEC in machining operations are shown in Table 1. Search step 1 in Table 1 shows the keywords searched on the Scopus search engine, and step 2 indicates that data collection was limited to 2001 to 2020. Search step 3 shows the excluded keywords that are not relevant to the machining operations investigated for SEC. The final collected most relevant documents on SEC in machining operations comes out to 268.  ("Particle Size", "Grinding (comminution)", "Ball Mills", "Cements", "Comminution", "Grinding Mills", "Particle Size Analysis", "Forecasting", "Ternary Alloys", "Size Determination", "High Pressure Grinding Rolls", "Reinforcement", "Grinding Machines", "Size Effect", "Biomass", "Vertical Roller Mills", "Minerals", "Moisture", "Wood", "Additives", "Cement Grinding", "Cement Industry", "Computer Control Systems", "Fine Grinding", "Grinding Characteristics", "Operating Parameters", "Rollers (machine Components)", "Stirred Media Mill", "Dry Grinding") 268

Features of Data Collected on SEC: Publications, Yearly Prediction and Citations
The primary information extracted from Scopus on SEC research data are shown in Table 2. The SEC databases consist of 268 documents from 2001 to 2020 published in 122 different sources. The collected papers have 5.87 average years from publication, 16.88 average citations per article, and 2.288 average citations per year article. All the sources on SEC have 6708 references. The extracted data have 186 articles, two book chapters, 77 conference papers, one erratum paper, and two review papers. In addition, the 268 documents on SEC have 1863 index-keywords or keywords plus, 719 authorkeywords, 719 authors, and 962 author appearances. Of the extracted articles from the Scopus database, 11 articles written by a single author and 708 authors appeared in multiauthored documents.
From 2001 to 2020, there are 268 documents published on SEC in machining operations and the year-wise distribution is shown in Figure 1. During the first five years, 15 papers published on SEC in machining operations. Since 2011, the article publication rate on SEC increased at a faster pace. Overall, the annual article publications on SEC in machining operations shows a quickly rising trend since 2015. To better understand SEC in machining operations documents changing trend, a curve is fitted between time and documents published annually using an exponential function. The equation presented in Figure 1. can predict the documents published yearly having an R-square of 86%. The article's mean citations per article and year are shown in Figure 2.

The Most Prolific and Dominant Article-Citation Analysis
This section explains and analyses the most cited articles of SEC in machining operations [46]. A total number of 268 documents of SEC in machining operations. The most extensive set of related items consists of 112 articles divided into 17 clusters. Cluster-1 consists of 12 documents, cluster-2, 3, 4 of 11 authors' documents, cluster-5 of 10 documents, cluster-6 of eight documents, cluster-7 of seven, cluster-8 of six, cluster-9, 10, 11, 12, 13 of five documents, cluster-14, 15, 16 of three, and cluster-17 of two documents. The network visualization of connected documents based on citations by association strength method is shown in Figure 3.
The node size signifies the frequency of occurrence. The line between two nodes specifies that there is a co-occurrence association amid them. The thickness of the lines directs the strength of co-occurrence.

The Most Prolific and Dominant Article-Citation Analysis
This section explains and analyses the most cited articles of SEC in machining operations [45]. A total number of 268 documents of SEC in machining operations. The most extensive set of related items consists of 112 articles divided into 17 clusters. Cluster-1 consists of 12 documents, cluster-2, 3, 4 of 11 authors' documents, cluster-5 of 10 documents, cluster-6 of eight documents, cluster-7 of seven, cluster-8 of six, cluster-9, 10, 11, 12, 13 of five documents, cluster-14, 15, 16 of three, and cluster-17 of two documents. The network visualization of connected documents based on citations by association strength method is shown in Figure 3.
The node size signifies the frequency of occurrence. The line between two nodes specifies that there is a co-occurrence association amid them. The thickness of the lines directs the strength of co-occurrence.
The total citations are picked as one criterion for ranking, followed by the strength of links and total citations per year, and the top 10 articles are shown in Table 3. The document of Anderson et al. (2006) [46] ranked at one, as the article has 246 citations, 13 links, and 17.57 total citations per year. So, the title "Laser-assisted machining of Inconel 718 with an economic analysis" in "International Journal of Machine Tools and Manufacture" becomes the most prolific and dominant article in the field of SEC in machining operations.   [47], with 213 citations ranked second, has nine total link strength and 21.30 total citations per year. The document of Zhou et al. (2016) [33] ranked first based on total citations per year. All the top nine papers cited more than 100 times, and the top four more than 200.  The total citations are picked as one criterion for ranking, followed by the strength of links and total citations per year, and the top 10 articles are shown in Table 3.

The Most Prolific and Dominant Source-Citation Analysis
In source citation analysis, the minimum number of documents set at one and total citations at two. Out of 122 sources, 84 meet the threshold criteria. The sources with the greatest total link strength are 41. The 41 items divided into 10 clusters. Cluster-1 consists of six sources, cluster-2, 3, 4 of five sources, cluster-5, 6, and 7 of four sources, cluster-8, 9

The Most Prolific and Dominant Source-Citation Analysis
In source citation analysis, the minimum number of documents set at one and total citations at two. Out of 122 sources, 84 meet the threshold criteria. The sources with the greatest total link strength are 41. The 41 items divided into 10 clusters. Cluster-1 consists of six sources, cluster-2, 3, 4 of five sources, cluster-5, 6, and 7 of four sources, cluster-8, 9 of two sources, and cluster-10 of two sources. The network visualization of five clusters with different colors is shown in Figure 4.
The top 10 sources based on the total citations, number of articles, total link strength, and average article citations are shown in Table 4. The Int J Mach Tools Manuf ranked first when the total citations, total link strength, and average articles citations are selected as an index to the journal's impact and have 903 total citations. The first article published in 2004 SEC in machining. The Int J Adv Manuf Technol also ranked first based on a number of documents 28. The J. Clean. Prod. ranked second with 639 total citations and 13 papers. The h-index, g-index, and m-index of the top 10 journals also shown in Table 4. The time zone of the source imitates the articles that appear in a specific period is shown in Figure 5.  The top 10 sources based on the total citations, number of articles, total link strength, and average article citations are shown in Table 4. The Int J Mach Tools Manuf ranked first when the total citations, total link strength, and average articles citations are selected as an index to the journal's impact and have 903 total citations. The first article published in 2004 SEC in machining. The Int J Adv Manuf Technol also ranked first based on a number of documents 28. The J. Clean. Prod. ranked second with 639 total citations and 13 papers. The h-index, g-index, and m-index of the top 10 journals also shown in Table 4. The time zone of the source imitates the articles that appear in a specific period is shown in Figure 5.

The Most Prolific and Dominant Author-Citation Analysis
This subsection explains the visual reflection of the author productivity and their impacts based on citations. Table 5 shows the total link strength, total citations, number of articles, and average article citations of top authors having one article and 10 citations. There are 719 authors; out of this, 297 meet the threshold. The most extensive set of related authors consists of 132 items. The 132 authors divided into 10 clusters. Cluster-1 consists of 25 authors, cluster-2 of 24 authors, cluster-3 and 4 of 22 authors, cluster-5 of 11 authors, cluster-6 of nine, cluster-7 of seven authors, cluster-8 of five, cluster-9 of four, and cluster-10 of three authors. The network visualization of nine clusters with different colors is shown in Figure 6.

The Most Prolific and Dominant Author-Citation Analysis
This subsection explains the visual reflection of the author productivity and their impacts based on citations. Table 5 shows the total link strength, total citations, number of articles, and average article citations of top authors having one article and 10 citations. There are 719 authors; out of this, 297 meet the threshold. The most extensive set of related authors consists of 132 items. The 132 authors divided into 10 clusters. Cluster-1 consists of 25 authors, cluster-2 of 24 authors, cluster-3 and 4 of 22 authors, cluster-5 of 11 authors, cluster-6 of nine, cluster-7 of seven authors, cluster-8 of five, cluster-9 of four, and cluster-10 of three authors. The network visualization of nine clusters with different colors is shown in Figure 6.
The authors' rank is assigned based on total citations, number of articles, total link strength, and average article citations, as shown in Table 5   The authors' rank is assigned based on total citations, number of articles, total link strength, and average article citations, as shown in Table 5

The Most Prolific and Dominant Organizations Citation Analysis
There are 486 organizations involved in the research of SEC in machining operations. The ranks are assigned while considering a minimum number of one document only for each organization, and each document must have five citations. Out of 486 organizations, only 276 meet the threshold. The organizations rank is assigned based on total link strength, total citations, number of articles, and average article citations, as shown in Table 6

The Most Prolific and Dominant Organizations Citation Analysis
There are 486 organizations involved in the research of SEC in machining operations. The ranks are assigned while considering a minimum number of one document only for each organization, and each document must have five citations. Out of 486 organizations, only 276 meet the threshold. The organizations rank is assigned based on total link strength, total citations, number of articles, and average article citations, as shown in Table 6. School of Mechanical Engineering, Purdue University, United States ranked first based on total citations 459, the number of articles two, and ranked fifth based on total link strength of five. The School of Mechanical Engineering, Shandong University, China, ranked second based on total citations of 220.

The Most Prolific and Dominant Country-Citation Analysis
There are 52 countries involved in SEC in machining operations research, and the ranks are assigned while considering a minimum of one document for each country and five citations. Out of 52 countries, 40 meet the threshold. Therefore, the country with the greatest total link strength of 34 is connected. The 34 countries divided into nine clusters. The cluster-1 consist of seven countries, cluster-2 of five, cluster-3, 4, 5 consists of four countries, cluster-6 and 7 of three countries, clusters-8, and 9 of two countries. The density visualization of nine clusters is shown in Figure 8.

The Most Prolific and Dominant Country-Citation Analysis
There are 52 countries involved in SEC in machining operations research, and the ranks are assigned while considering a minimum of one document for each country and five citations. Out of 52 countries, 40 meet the threshold. Therefore, the country with the greatest total link strength of 34 is connected. The 34 countries divided into nine clusters. The cluster-1 consist of seven countries, cluster-2 of five, cluster-3, 4, 5 consists of four countries, cluster-6 and 7 of three countries, clusters-8, and 9 of two countries. The density visualization of nine clusters is shown in Figure 8. The country rank is assigned based on total link strength, total citations, number of articles, and average article citations, as shown in Table 7   The country rank is assigned based on total link strength, total citations, number of articles, and average article citations, as shown in Table 7

Co-Authorship Versus Authors Analysis of SEC in Machining Operations
There are 719 authors involved in SEC in machining research writing. The network visualization was seen while setting minimum documents two and citations at five. A total of 124 co-authors were meet the threshold, and the most extensive set of connected authors is 21. The 21 authors divided into five clusters. Cluster-1, 2, 3, 4, and 5 consists of 7, 4, 4, 3, and 3 authors. The density visualization of five clusters is shown in Figure 9.

Co-Authorship Versus Authors Analysis of SEC in Machining Operations
There are 719 authors involved in SEC in machining research writing. The network visualization was seen while setting minimum documents two and citations at five. A total of 124 co-authors were meet the threshold, and the most extensive set of connected authors is 21. The 21 authors divided into five clusters. Cluster-1, 2, 3, 4, and 5 consists of 7, 4, 4, 3, and 3 authors. The density visualization of five clusters is shown in Figure 9.

Co-Occurrence of Keyword and Content Analysis
Some items, like keyword or index words or feature entities pronounced in an article, appear simultaneously called co-occurrence phenomenon. The feature entities consist of organizations, title, author, or keywords, and so on in the literature. It is a quantifiable study of the co-occurrence phenomenon to disclose the content connotation of the evidence and the acquaintance disguised by specific items. Keywords can acme the exploration boundaries in studying and regulating research hotspots and growth trends [46].

Co-Occurrence of Keyword and Content Analysis
Some items, like keyword or index words or feature entities pronounced in an article, appear simultaneously called co-occurrence phenomenon. The feature entities consist of organizations, title, author, or keywords, and so on in the literature. It is a quantifiable study of the co-occurrence phenomenon to disclose the content connotation of the evidence and the acquaintance disguised by specific items. Keywords can acme the exploration boundaries in studying and regulating research hotspots and growth trends [45].

Co-Occurrence Analysis of Author Keywords of SEC in Machining Operations
This section explains and analyzes the author keywords co-occurrence in SEC in machining operations Scopus databases from 2011-2020. The keywords on SEC in machining operations are selected based on two author-keywords co-occurrences. A total number of 719 author keywords appear on SEC in machining operations, and 138 meet the two author-keyword co-occurrences threshold. The most extensive set of related items consists of 137 author-keywords divided into 14 clusters. The author keyword network visualization of co-occurrences is shown in Figure 10. Different clusters consist of various author keywords are given below: 719 author keywords appear on SEC in machining operations, and 138 meet the two author-keyword co-occurrences threshold. The most extensive set of related items consists of 137 author-keywords divided into 14 clusters. The author keyword network visualization of co-occurrences is shown in Figure 10. Different clusters consist of various author keywords are given below:
The tree plot of leading 30 author-keywords used in SEC in machining operations documents are shown in Figure 11. The SEC in machining operations documents used the author-keyword "specific cutting energy" 62 times and have 19% contribution in the top 30 author-keywords. The author-keyword "surface roughness" is used 27 times and contributes 8%, followed by "machining" 23 times with 7% contribution and "tool wear" nine times and contributes 6%. The author-keywords "energy efficiency", "specific energy consumption", "cutting force", "machinability", "cutting forces", and "turning" are used more than 10 times.

Co-Occurrence Analysis of Index-Keywords of SEC in Machining Operation
Index keywords define scholarly articles' content, but the content is picked from the pre-decided information known as a controlled vocabulary utilized for bibliographic archives. Index keywords detentions the core of the theme of an article. This section explains and analyzes the index-keywords co-occurrence in SEC in machining operations of Scopus databases from 2001 to 2020. The index-keywords on SEC in machining operations

Co-Occurrence Analysis of Index-Keywords of SEC in Machining Operation
Index keywords define scholarly articles' content, but the content is picked from the pre-decided information known as a controlled vocabulary utilized for bibliographic archives. Index keywords detentions the core of the theme of an article. This section explains and analyzes the index-keywords co-occurrence in SEC in machining operations of Scopus databases from 2001 to 2020. The index-keywords on SEC in machining operations are selected based on five index-keywords co-occurrences. A total number of 1864 of indexkeywords appear on SEC in machining operations, and 127 meet the five co-occurrences threshold. The most extensive set of related items consists of 57 index keywords divided into five clusters. The overlay visualization of index-keywords co-occurrences is shown in Figure 12.
The tree plot of the foremost 30 index-keywords or keywords-plus used in SEC in machining operations documents is shown in Figure 13. The SEC in machining operations documents used the index keyword "specific cutting energy" 138 times and have 13% contribution in the top 30 index keywords. The index keyword "energy utilization" is used 81 times and contributes 7%, followed by "cutting tools" 76 times of 7% contribution.
The tree plot of the foremost 30 index-keywords or keywords-plus used in SEC in machining operations documents is shown in Figure 13. The SEC in machining operations documents used the index keyword "specific cutting energy" 138 times and have 13% contribution in the top 30 index keywords. The index keyword "energy utilization" is used 81 times and contributes 7%, followed by "cutting tools" 76 times of 7% contribution. The index keywords "milling (machining)", "cutting", "surface roughness", "machining", "specific energy consumption" are utilized more than 50 times and have 5% or more contribution in the top 30 index keywords.
Similarly, as done for author or index-keywords, a tree plot of the top 30 title keywords is shown in Figure 14. For example, the SEC in machining operations documents used the title keyword "machining" 99 times and have 11% contribution in the top 30 title keywords. The title keyword "cutting" is used 96 times and contributes 11%, followed by "energy" 94 times with 11% contribution and "milling" 53 times and contributes 6%, followed by "specific", "process", and "consumption". The index keywords "milling (machining)", "cutting", "surface roughness", "machining", "specific energy consumption" are utilized more than 50 times and have 5% or more contribution in the top 30 index keywords. Similarly, as done for author or index-keywords, a tree plot of the top 30 title keywords is shown in Figure 14. For example, the SEC in machining operations documents used the title keyword "machining" 99 times and have 11% contribution in the top 30 title keywords. The title keyword "cutting" is used 96 times and contributes 11%, followed by "energy" 94 times with 11% contribution and "milling" 53 times and contributes 6%, followed by "specific", "process", and "consumption".   Similarly, as done for author or index-keywords, a tree plot of the top 30 title keywords is shown in Figure 14. For example, the SEC in machining operations documents used the title keyword "machining" 99 times and have 11% contribution in the top 30 title keywords. The title keyword "cutting" is used 96 times and contributes 11%, followed by "energy" 94 times with 11% contribution and "milling" 53 times and contributes 6%, followed by "specific", "process", and "consumption".

Text-Data Analysis by VOSviewer
The VOSviewer software 1.16.6 performs an exclusion procedure in a text-data analysis and computes each term's relevance score. In the text data extraction process, the words with a higher relevance score are inclined to signify explicit themes covered by the text information. The text-data extraction terms have a low relevance score incline to be general and do not represent any specific theme. An insufficient relevance score data extracted terms are sieved out, and the main focus on explicit and informative pieces. Around 40% of data extracted terms with low relevance score are excluded [45]. In a text-data analysis, the terms from the title and abstract of the document are extracted. The document's title and abstract's extracted data are analyzed with the Binary-coding method and relevance score [66].
The terms in a text-data analysis on SEC in machining operations are selected based on two occurrences. A total of 5817 text-data terms appear on SEC in machining operations. The minimum number of terms is set at five, and 302 meet the five occurrences threshold. Based on the relevance score, 60% most relevant words considered and comes out to be 181. The most extensive set of related items consists of 178 terms divided into five clusters. The density visualization of text-data analysis terms in various clusters is shown in Figure 15. ysis and computes each term's relevance score. In the text data extraction process, the words with a higher relevance score are inclined to signify explicit themes covered by the text information. The text-data extraction terms have a low relevance score incline to be general and do not represent any specific theme. An insufficient relevance score data extracted terms are sieved out, and the main focus on explicit and informative pieces. Around 40% of data extracted terms with low relevance score are excluded [46]. In a textdata analysis, the terms from the title and abstract of the document are extracted. The document's title and abstract's extracted data are analyzed with the Binary-coding method and relevance score [66].
The terms in a text-data analysis on SEC in machining operations are selected based on two occurrences. A total of 5817 text-data terms appear on SEC in machining operations. The minimum number of terms is set at five, and 302 meet the five occurrences threshold. Based on the relevance score, 60% most relevant words considered and comes out to be 181. The most extensive set of related items consists of 178 terms divided into five clusters. The density visualization of text-data analysis terms in various clusters is shown in Figure 15. The top 20 text-data terms based on occurrence and relevance score in the title and abstract are shown in Table 8. The words based on occurrence are ranked, and the terms "consumption", "specific energy consumption", "optimization", "tool wear", and "machinability" appeared more than 40 times with a relevance score of 0.3804, 0.7001, 0.9216, and 0.5272, respectively, from the top 178 text data terms. The terms or words ranked first based on relevance score are "optimal combination", having a relevance score of 3.1753 The top 20 text-data terms based on occurrence and relevance score in the title and abstract are shown in Table 8. The words based on occurrence are ranked, and the terms "consumption", "specific energy consumption", "optimization", "tool wear", and "machinability" appeared more than 40 times with a relevance score of 0.3804, 0.7001, 0.9216, and 0.5272, respectively, from the top 178 text data terms. The terms or words ranked first based on relevance score are "optimal combination", having a relevance score of 3.1753 but occurred only five times, followed by "RSM" having a relevance score of 2.9339 occurred seven times. The term "conventional machining" happened a maximum of 17 times, with a relevance score of 1.7418.

Sankey Diagrams: Three Field Plots on SEC in Machining Operations
Sankey diagrams are conventionally utilized to represent the flow of energy or materials in several networks and procedures. They represent flows, associations, and their transition with quantitative details. Sankey diagrams reflect guided and weighted graphs with features of weight that fulfil flow preservation. At each node, the amount of the influx weights is identical to its outgoing consequences. The processes with Sankey diagrams are envisioned, and communications can be explored [67]. The three-field plot in Biblioshiny 2.0 is utilized to visually assess the relationship between sources, countries, affiliations, keyword, leading authors, cited source, and author-keywords, etc.
The diagrams of rectangular shape are used to represent pertinent elements with various colors. The rectangle's height indicates the relationship among multiple features such as countries, sources, prominent authors, and author-keywords, etc. The larger the size of the rectangle, means more relationships between various components. Figure 16 shows the diagram for research in SEC in machining literature on the relation between author keyword (left), author (middle) and source (right). The analysis established that which SEC keywords in machining operations had used most frequently by different authors and journals. The study of the top keywords, authors and sources indicated that there are keywords, i.e., "specific cutting energy", "surface roughness", "laser-assisted machining", "energy efficiency", and "tool wear", and authors Shin Y.C., Iqbal A., Balogun V. A., Khan M., mainly used these keywords, published in sources such as International Journal of Advanced Manufacturing Technology, and Journal of Cleaner Production.   The study of the top keyword-plus, authors and sources indicated that there were six keywords, i.e., "specific cutting energy", "cutting" and "cutting tools", "energy utilization", "surface roughness", "machining" and six authors Khan M., Ahmad R., Warsi S.S., Iqbal A., Liu Z., and Wang Y., published in source International Journal of Advanced Manufacturing Technology, and Journal of Cleaner Production. Figure 18 shows the relationship between countries (left), author (middle) and source (right). It shows how different authors from various countries published in different journals. For example, it is revealed that the author's Khan M., Ahmad R., Warsi S.S., and Jaffey Shi published their research in source International Journal of Advanced Manufacturing Technology. China is another major contributor, and their researcher published in different sources and in a similar way the USA, authors also published in different sources.  Figure 17 shows the diagram for research in SEC literature in machining operations on the relation between index keyword or keyword-plus (left), author (middle), and source (right). The analysis established which keyword-plus of SEC literature in machining operations were used most frequently by different authors and journals published. The study of the top keyword-plus, authors and sources indicated that there were six keywords, i.e., "specific cutting energy", "cutting" and "cutting tools", "energy utilization", "surface roughness", "machining" and six authors Khan M., Ahmad R., Warsi S.S., Iqbal A., Liu Z., and Wang Y., published in source International Journal of Advanced Manufacturing Technology, and Journal of Cleaner Production. Figure 18 shows the relationship between countries (left), author (middle) and source (right). It shows how different authors from various countries published in different journals. For example, it is revealed that the author's Khan M., Ahmad R., Warsi S.S., and Jaffey      Figure 19 shows the relationship between authors (left), title-terms (middle), and source (right). It shows authors Balogun Figure 19 shows the relationship between authors (left), title-terms (middle), and source (right). It shows authors Balogun VA, Wang Y, Liu Z, Khan M utilizing most frequently used title terms like Cutting, energy, machining, specific. Their works being published in journals like the International Journal of Advanced Manufacturing Technology, Journal of Cleaner Production, Procedia manufacturing.

Thematic Areas of SEC in Machining Operations from 2001 to 2020
The thematic areas belong to article classification, as shown in Figure 20. It is observed that the extent of knowledge of Engineering is the first one, with 217 articles of 43% and 69 articles with 14% belongs to Material Science, followed by Computer Science, with 62 papers and Physics and Astronomy 31 articles with 6% contribution. Varied classification of themes such as Business, Management, and Accounting, Decision Sciences, Energy, and so on have a contribution of around 5%.

Thematic Areas of SEC in Machining Operations from 2001 to 2020
The thematic areas belong to article classification, as shown in Figure 20. It is observed that the extent of knowledge of Engineering is the first one, with 217 articles of 43% and 69 articles with 14% belongs to Material Science, followed by Computer Science, with 62 papers and Physics and Astronomy 31 articles with 6% contribution. Varied classification of themes such as Business, Management, and Accounting, Decision Sciences, Energy, and so on have a contribution of around 5%.

Thematic Areas of SEC in Machining Operations from 2001 to 2020
The thematic areas belong to article classification, as shown in Figure 20. It is observed that the extent of knowledge of Engineering is the first one, with 217 articles of 43% and 69 articles with 14% belongs to Material Science, followed by Computer Science, with 62 papers and Physics and Astronomy 31 articles with 6% contribution. Varied classification of themes such as Business, Management, and Accounting, Decision Sciences, Energy, and so on have a contribution of around 5%.   Figure 20. Classification of articles on thematic area.

Research Gaps and Hotspots
SEC publications in machining operations have become progressively popular in recent years. However, there are still many research gaps in the machining operations where SEC is anticipated to be addressed in subsequent years. The author's keyword network visualization based on co-occurrences from Figure 10 reveals that the machining operation laser-assisted milling has a smaller node size that signifies the occurrence frequency. The line between two nodes specifies that there is a co-occurrence association amid them, and the thickness of the lines directs the strength of co-occurrence. So, the laser-assisted milling operation was not investigated from the SEC viewpoint largely. It indicates that there exists a gap in the research while investigating SEC in laser-assisted milling operation.
Similarly, the other keywords signify the research gaps. For example, chip thickness, grooving, hard milling, micro-cutting, drilling, lubrication, and micromachining have smaller nodes, and the thickness of the lines refers to Figure 10. It indicates that more research needs to be conducted on "micro-cutting" and "micromachining", these two keywords have the same meaning, but researchers used them differently. SEC analysis needs to be addressed in these operations, such as drilling and hard milling, similarly chip thickness, grooving, and lubrication also have research gaps. Grooving also belongs to machining operation, chip thickness to output response and lubrication to input parameters. The SEC and surface roughness have a larger node size, indicating these two responses are investigated the maximum number of times in turning operation. The decision-making, response surface methodology, and optimization keywords smaller node size suggest that the blend of responses needs more inquiry. As temperature, force, cutting energy needs to consider while exploring SEC in machining operations. Electric discharge machining's process appears very little, and still, a gap exists in advanced or non-conventional machining operations. The slight use of keywords "decision making" and "optimization" indicates that a gap exists in decision-making methods and optimization of parameters while taking SEC as a response in machining operations. The aids of entropy weights methods in machining operation identified only two conventional turning papers that involve SEC in a multi-objective optimization model. However, the review identified 19 traditional and non-conventional machining operations [68]. This indicates a clear research gap of decision making and optimization in machining operations, considering SEC as one of the sustainable machining response. More research work using decision-making techniques can be regarded as in SEC analysis [69][70][71][72][73].
Similarly, research hotspots can be identified from index keywords Figure 12. as it is scholarly articles' content and is picked from the pre-decided information. It is revealed that the machining operations of laser-assisted milling and laser-assisted machining have smaller node size. The output responses such as hardness, tool wear, cutting force need to be inspected alongside SEC. The smaller node size of the Taguchi method, ANOVA, and multi-objective optimization indicates that the minimum use of experiments and experiments is mainly designed based on conventional methods. The smaller node size of the index keyword "geometry" and "tool wear" states that tool geometry is not considered too much while designing experiments, and tool wear is a response parameter. Another smaller node size of the effective term "life cycle" reveals that the SEC was not considered while doing life cycle analysis of machining operations. So, for the life cycle analysis of machining operations, there is a necessity to consider SEC. Specific cutting energy, energy utilization, and surface roughness are dominating in milling operations. The content text analysis also indicates research gaps can be seen from Table 8 based on relevance score and occurrence.

Managerial Implications of the Review
The review has tried to bring out a new connotation to the emerging SEC concepts in machining operations. The SEC state of the art and bibliometric analysis is beneficial for engineering researchers, societies, and the industry. Legal restrictions and stricter emissions norms are forcing enterprises to adopt sustainable manufacturing norms. The recent review assists an engineer working in an enterprise to quickly identify the quantum of research already carried out in the field of concern. Moreover, the SEC depicts how much energy is used for manufacturing a unit of an item and is convenient to find the quantity of energy. Thus, SEC is one of the energy efficiency indicators and is a good measure able to meet the objective to diminish greenhouse gas emissions by 80-95% by 2050 compared to 1990 levels.
Furthermore, SEC is one of the approaches for energy benchmarking at the multinational, national and regional, location, and process levels, most commonly utilized in process-level benchmarking [3]. The review identified the energy-intensive machining process. Therefore, the researcher, practitioner, or industry engineer can take benefit from the presented visualizations networks. Besides, it indicates the volume of research already conducted on that process. Thus, it immediately decides whether the topic of his concern is a hot spot where additional research is required, or the existing research data will suffice. The present bibliometric review and analysis have brought a new idea and aspect that earlier reviews might have ignored unknowingly.

Concluding Remarks
This paper aims to shed light on the research of specific energy consumption in machining operations. Furthermore, it offers a groundwork for imminent research. The exploration tactic directed the assortment of 268 documents from 2001 to 2020 in the Scopus repository. A systematic bibliometric analysis and visualization review is carried out to bridge the gap and identify the hot spots in research.
The citation analysis results revealed that the most productive and leading article on SEC in machining processes is by Anderson et al. (2006) [46] titled "Laser-assisted machining of Inconel 718 with an economic analysis" in "International Journal of Machine Tools and Manufacture". The most prolific and dominant source is the "International Journal of Machine Tools and Manufacture", followed by the "Journal of Cleaner Production". The author Shin Y.C. has the maximum number of citations and articles published in the SEC field. School of Mechanical Engineering, Purdue University, United States, emerges as the topmost organization. The most upstanding country is the United States of America, followed by China and then India.
The "specific cutting energy" emerges as an author keyword with a contribution of 19%, followed by "surface roughness." The "specific cutting energy" also appears 138 times as an index keyword followed by "energy utilization" and "cutting tools". The "Machining" has been used 99 times in the Title words, followed by "cutting" and "Energy." The terms "consumption", "specific energy consumption", "optimization", "tool wear", and "machinability" appeared more than 40. The terms or words "optimal combination", "RSM", and "conventional machining" are top-ranked based on relevance score. The Shankey diagrams, the three-field plot presented a valuable relationship among authors, keyword, source, and countries.
The research hotspots indicate that the responses hardness, tool wear, cutting force need to be inspected together with SEC. The smaller node size of the Taguchi method, ANOVA, and multi-objective optimization indicates that the minimum use of experiments and experiments is mainly designed based on conventional methods. The study suggests that much research potential exists in the machining processes like laser-assisted milling, Electric discharge machining, hard milling, micro-cutting, drilling, micro-cutting, micromachining, grooving, chip thickness, lubrication. This research paper is a boon for a researcher willing to work in the field of SEC for machining operations. The network visualizations help to identify the research gaps termed Hotspots. Future research can be conducted in these areas, which allows the researcher to minimize the time spent determining the exact topic and field of the study.