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The purpose of this paper is to present the comparison of sustainability characteristics of conventional and computer numerical control (CNC) turning process. The sustainability performance measures of both the processes were also being evaluated.

The study discusses the achievement of sustainability characteristics at the manufacturing process level of widely used industrial process, mechanical machining. Sustainable development includes improvements in material, product design and manufacturing process orientations. The present study narrates the sustainability characteristics at the process level.

The results confirm that the overall sustainability characteristics of CNC machining are potentially high considering the economic and environmental aspects of the machining parameters. A detailed life cycle analysis for both conventional and CNC turning was performed to evaluate the environmental impact and benefits.

The study contributed in the paper is limited to process dimension of sustainability. The economic and environmental aspects of machining were also being discussed.

The conduct of the study enabled the comparison of sustainability characteristics of conventional and CNC-turning processes. The approach could also be expanded for the comparison of sustainability characteristics of other manufacturing processes also.

The study is an attempt to explore the process sustainability by the comparison of environmental impact of making processes. Hence, the contributions are original.

In this paper the authors aim to examine the dominance of machine and organism metaphors in organisational studies. They argue that these metaphors impede progress towards sustainable development because they perpetuate a story that dehumanises and de‐prioritises humans at the expense of the organisation which in turn becomes a rarefied and prioritised subject. This result is not consistent with the whole of humanity narrative that is entwined within sustainable development. To develop these arguments, the authors discuss sustainable development, highlighting how the concept implicates the central role of humans. They then discuss the limitations of the machine and organism metaphors relative to sustainable development. The paper then offers a different view of metaphors and suggests a more holistic understanding that is compatible with the achievement of sustainable development.

As a conceptual paper, this article reviews existing literature and offers critique of the use of the dominant metaphors of machine and organism.

Machine and organism metaphors perpetuate a language and understanding that dehumanises work and organisations. The implication of this is that organisational practice and research needs to adopt new metaphors to facilitate sustainable development.

As a conceptual document, this manuscript offers new avenues for future research and practice.

The arguments presented challenge scholars', educators' and practitioners' use of machine and organism metaphors when discussing organisations.

The originality/value of this paper lies in reflecting upon the metaphors of organism and machine relative to sustainable development and in turn reflecting upon the metaphors associated with and the central role of humans within the sustainable development concept.

This study aims to understand the current production scenario emphasizing the significance of green manufacturing in achieving economic and environmental sustainability goals to fulfil future needs; to determine the viability of particular strategies and actions performed to increase the process efficiency of electrical discharge machining; and to uphold the values of sustainability in the nonconventional manufacturing sector and to identify future works in this regard.

A thorough analysis of numerous experimental studies and findings is conducted. This prominent nontraditional machining process’s potential machinability and sustainability challenges are discussed, along with the current research to alleviate them. The focus is placed on modifications to the dielectric fluid, choosing affordable substitutes and treating consumable tool electrodes.

Trans-esterified vegetable oils, which are biodegradable and can be used as a substitute for conventional dielectric fluids, provide pollution-free machining with enhanced surface finish and material removal rates. Modifying the dielectric fluid with specific nanomaterials could increase the machining rate and demonstrate a decrease in machining flaws such as micropores, globules and microcracks. Tool electrodes subjected to cryogenic treatment have shown reduced tool metal consumption and downtime for the setup.

The findings suggested eco-friendly machining techniques and optimized control settings that reduce energy consumption, lowering operating expenses and carbon footprints. Using eco-friendly dielectrics, including vegetable oils or biodegradable dielectric fluids, might lessen the adverse effects of the electrical discharge machine operations on the environment. Adopting sustainable practices might enhance a business’s reputation with the public, shareholders and clients because sustainability is becoming increasingly significant across various industries.

A detailed general review of green nontraditional electrical discharge machining process is provided, from high-quality indexed journals. The findings and results contemplated in this review paper can lead the research community to collectively apply it in sustainable techniques to enhance machinability and reduce environmental effects.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

The purpose of this paper is to review and analyze the recent sustainability assessment studies in the manufacturing industry from the triple-bottom-line (TBL) perspective. This paper aims to depict the status quo of practical sustainability assessment, summarize the different levels and boundaries of evaluation, and highlight the difficulties and further improvements needed to make the assessment more effective in the manufacturing industry.

Four keywords, namely, sustainability assessment, sustainable manufacturing, TBL and green production, were used to explore and find the relevant articles. First, this paper systematically reviewed the studies and analyzed the different levels and boundaries of sustainability assessment. Following this, the reviewed studies were critically discussed along with their merits and shortcomings.

The review showed that most of the sustainability assessment studies were conducted on product, company and process levels in the manufacturing industry. Nevertheless, there is still a need to focus more on plant and process level assessments to achieve the TBL objectives. Environmental assessment is comparatively matured in manufacturing industries. However, from the economic and social viewpoints, only cost analysis and workers’ safety, respectively, were considered in most of the studies. The economic and social indicators need to be more inclusive and should be validated and standardized for manufacturing industries.

Unlike previous sustainability assessment reviews in manufacturing industries which were mostly based on life cycle assessment, this paper has included environmental, social and economic aspects in one comprehensive review and focused on recent studies published from 2010 to 2017. This paper has explored the recent sustainability assessment trends and provided insights into the development of sustainability assessment in the manufacturing sector.

Many engineering applications in this era require new age materials; however, some classic alloys like spring steel are still used in critical applications such as aerospace, defense and automobile. To machine spring steel material, there exist various difficulties such as rapid tool wear rate, the rough surface formation of a workpiece and higher power consumption. The purpose of this paper is to address these issues, various approaches in addition to electrical discharge machines (EDM) are used such as dry EDM (DEDM) and near dry EDM (NDEDM).

This study focuses on these two approaches and their comparative analysis with respect to tool wear during machining of spring steel material. For this study, current, gap voltage, cycle time and dielectric medium pressure are considered input variables. This study shows that the near dry EDM approach yields better results. Hence, the thermo-electrical model for this approach is developed using ANSYS workbench, which is further validated by comparing with experimental results. This thermo-electrical model covers spark radius variation and formation of temperature profile due to electric discharge. Transient thermal analysis is used to simulate the electric discharge machining.

It is observed from this study that discharge environment parameters such as debris concentration and fluid viscosity largely influences the dielectric fluid pressure value. Experimental results revealed that NDEDM yields better results in comparison with DEDM as it shows a 25% lesser tool wear rate in NDEDM.

The range of predicted results and the experimental results are in close agreement, authenticating the model.

This paper aims to present the performance of deionized water in electrical discharge machining (EDM) during machining of Inconel 718, copper tool electrode and deionized water as dielectric. Three parameters, namely, pulse-on-time, pulse-off-time and discharge current were taken as control parameters with individual parameter having three levels. Influence of these control parameters on response such as tool wear rate (TWR), material removal rate (MRR) and surface roughness (Ra) is evaluated at various combinations of parametric levels. The results reveal deionized water can be effectively used as a sustainable dielectric and may substitute the hydrocarbon-based dielectric in electrical discharge machining. Also, the control parameters considered show significant impact on the process criteria. Super ranking method was adopted to achieve optimal integration of EDM control factors for obtaining higher MRR, lower TWR and Ra. Further, by applying analysis of variance test, discharge current is established as the dominant parameter during the machining process.

The experimentation was performed on Inconel 718 in SPARKONIX MOS, 35 A, ZNC EDM using deionized water as dielectric and copper tool as electrode. The dielectric circulatory system was developed without disturbing the existing dielectric circulation system. Figure 1 shows the EDM with newly developed dielectric system. The existing system consists of hydrocarbon-based dielectric, which has a number of drawbacks during the machining such as carbide deposition on the work material, which reduces removal of material from work material; carbon particle adhesion on tool, which results in inefficient discharge between the electrode; and the work material and production of CO and CH4 during machining, which makes the machining environment toxic. To overcome these drawbacks, a sustainable dielectric was adopted in present work. Trial experiments were conducted to select the ranges of parameters, namely, discharge current, pulse-on-time and pulse-off-time. The process characteristics were evaluated at different parametric combinations and the experimentation was designed as per Taguchi L9 orthogonal array. Table 1 shows the properties of Inconel 718. Table 2 shows the parameters considered with its ranges. Table 3 shows the experimental values. The difference of weight of work piece before and after was taken and divided by the machining time used for calculating the MWR. Similarly, the difference of weight of tool material before and after was taken and divided by machining time and is used for calculating TWR. Measurement of surface roughness was done using Talysurf surface roughness meter.

The experimentation was conducted at different parametric combination on Inconel 718 taking copper as electrode and deionized water as dielectric. The performance criteria was evaluated at considered parametric combination. The result shows that the EDM parameters have significant contribution on the performance criteria and deionized water can be effectively used as dielectric medium in EDM. The use of deionized water as dielectric will improve the process and sustainable green machining can be performed. Super ranking method has been implemented to achieve the best combination of control factors and it is obtained that the combination A1B1C3 (i.e. discharge current = 3 A, pulse-on-time = 1 µs and pulse-off-time = 3 µs) is best combination for obtaining the higher MRR and lower TWR and Ra. The contributing factor in the proposed research work is discharge current. Further, ANOVA was implemented to check the adequacy of these result. It was established that discharge current is the most influential factor followed by pulse-on-time and the least contributing factor as pulse-off-time. The findings of this paper may open the guidelines for researcher for performing research in the field of sustainable machining of difficult to cut materials such as Inconel 718 with sustainable dielectrics in engineering applications.

The paper is original in nature. The findings of this paper may open the guidelines for researcher for performing research in the field of sustainable machining.

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).

In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.

The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.

A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.