Empowering sustainable manufacturing: Unleashing digital innovation in spool fabrication industries

In industrial landscapes, spool fabrication industries play a crucial role in the successful completion of numerous industrial projects by providing prefabricated modules. However, the implementation of digitalized sustainable practices in spool fabrication industries is progressing slowly and is still in its embryonic stage due to several challenges. To implement digitalized sustainable manufacturing (SM), digital technologies such as Internet of Things, Cloud computing, Big data analytics, Cyber-physical systems, Augmented reality, Virtual reality, and Machine learning are required in the context of sustainability. The scope of the present study entails prioritization of the enablers that promote the implementation of digitalized sustainable practices in spool fabrication industries using the Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF-SWARA) method integrated with Triangular Fuzzy Bonferroni Mean (TFBM). The enablers are identified through a systematic literature review and are validated by a team of seven experts through a questionnaire survey. Then the finally identified enablers are analyzed by the IMF-SWARA and TFBM integrated approach. The results indicate that the most significant enablers are management support, leadership, governmental policies and regulations to implement digitalized SM. The study provides a comprehensive analysis of digital SM enablers in the spool fabrication industry and offers guidelines for the transformation of conventional systems into digitalized SM practices.

Global equity and timely access: COVID-19 & beyond 23rd DCVMN Annual General Meeting 2022 report

The 23rd Annual General Meeting of the Developing Countries Vaccine Manufacturers' Network (DCVMN), co-hosted by Serum Institute of India (SII), gathered over 365 delegates and more than 90 high-level speakers for three days of presentations, discussions, and networking, in Pune, India. The meeting provided a platform for vaccine manufacturers from developing countries to voice their experience, challenges and successes, as they play a critical role in the global research, development and supply of vaccines for achieving vaccine equity through increased collaborations and partnerships. The key topics of the 23rd Annual General Meeting revolved around: the key learnings from COVID-19, pandemic preparedness, vaccine sustainability and scalability, strengthening Africa's local manufacturing, partnerships & collaborations, financing, innovations, and vaccine hesitancy. The overarching theme focused on equity, timely access and sustainability, which was carried through in each session, with each panelist providing their contribution to answering - how can we create a sustainable vaccine ecosystem?

Room temperature roll-to-roll additive manufacturing of polydimethylsiloxane-based centrifugal microfluidic device for on-site isolation of ribonucleic acid from whole blood

Polymer-based lab-on-a-disc (LoaD) devices for isolating ribonucleic acid (RNA) from whole blood samples have gained considerable attention for accurate biomedical analysis and point-of-care diagnostics. However, the mass production of these devices remains challenging in manufacturing cost and sustainability, primarily due to the utilization of a laser cutter or router computer numerical control (CNC) machine for engraving and cutting plastics in the conventional prototyping process. Herein, we reported the first energy-efficient room-temperature printing-imprinting integrated roll-to-roll manufacturing platform for mass production of a polydimethylsiloxane (PDMS)-based LoaD to on-site isolate ribonucleic acid (RNA) from undiluted blood samples. We significantly reduced energy consumption and eliminated thermal expansion variations between the mold, substrate, and resists by accelerating the PDMS curing time to less than 10 min at room temperature without using heat or ultraviolet radiation. The additive manufacturing technology was applied to fabricate a multi-depth flexible polymer mold that integrated macro (2 mm) and micro-sized (500 μm) features, which overcomes the economic and environmental challenges of conventional molding techniques. Our integrated R2R platform was enabled to print adhesion-promoting films at the first printing unit and continuously in-line imprint with a high replication accuracy (99%) for high-volume manufacturing of a new centrifugal microfluidic chip with an enhancement of mixing performance by integrating an efficient mixing chamber and serpentine micromixer. This research paved the way for scalable green manufacturing of large-volume polymer-based microfluidic devices, often required in real-world sample-driven analytical systems for clinical bioanalysis.

Introducing new green machining technology to enhance process performance and reduce environmental pollution in the metal processing industry

The pursuit of enhanced cooling and lubrication methods for machining processes that are energy-efficient, environmentally friendly, and cost-effective is receiving significant attention from both academia and industry. The reduction of CO2 emissions is closely tied to electrical and embodied energy consumption. This study introduces a novel LN2 oil-on-water (LNOoW) cooling/lubrication (lubricooling) approach for the machining of Ti-6Al-4V alloy. Machinability aspects, energy-related aspects, environmental-related aspects, and economic aspects are measured and compared. More specifically, surface quality, electrical energy, cutting forces, and tool wear were measured in machinability aspects. Similarly, specific total energy and specific cumulative Energy Demand (S_CED), specific carbon emission, and production costs were measured to investigate the energy and environmental and economic aspects, respectively. The LNOoW provided the best machinability results compared with other approaches. Result found that LNOoW produced 37.5% better surface quality, removed 159.17% more material, and reduced 50.56% specific cutting energy and 53.63% specific costs as compared to traditional dry cutting conditions. The 39% increment in specific carbon emissions observed in the LN2 oil-on-water (LNOoW) approach in comparison to the dry-cutting method can be mitigated through the implementation of sustainable practices in the production of liquid nitrogen (LN2). The information provided in this study serves as a valuable resource for the development of environmentally friendly machining processes. The study also helps get the sustainable development goals (SDGs) of the United Nations.

Manufacturing system reconfiguration towards sustainable production: a novel hybrid optimization methodology

Developing a sustainable manufacturing system is a progressively challenging issue as governments across the world have been enforcing increasingly severe regulations by promoting the reduction of environmental waste for manufacturing and energy-saving production activities. Thus, there is a need for developing a sustainable manufacturing system that can be fully examined by incorporating ecological aspects (e.g., consumed energy) for related operations of a manufacturing system using computer-based discrete event simulation tools. In this study, a combined framework of a novel hybrid fuzzy multi-objective optimization and discrete event simulation approach is presented. We combine ecological and economic data and optimization techniques that are aimed at minimizing economic and ecological objectives in a manufacturing system at an early design phase. Hence, the fuzzy multi-objective optimization model is formulated by incorporating economic and ecological parameters. Again, the discrete event simulation model is established based on a comprehensive performance evaluation of the production system. This study also supports design decisions in determining optimum machine numbers, lighting, and cooling equipment required for the production processes within the sustainable manufacturing in conjunction with the most effective level of material flows. In addition, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-epsilon constraint approach is applied to handle the multiple-objective optimization problem towards a set of trade-offs among the optimization objectives. A real-life application is carried out for investigating the applicability of the created hybrid framework. The findings of this study demonstrate that this framework is useful as a decision-making tool since it can develop a sustainable manufacturing system design considering an optimal solution associated with amounts of energy usage and CO2 emissions under economic constraints.

Improving the adsorption performance of non-polar benzene vapor by using lignin-based activated carbon

Both indoor and outdoor contamination continually contain benzene vapor. It has primary concerns about long-term health risks to the living environment. Benzene is a crucial airborne pollutant in the environment due to its apparent acute toxicity, high volatility, and poor degradability. It is especially urgent to restrain benzene emissions due to the persistent concentration increase and stringent processes. Benzene adsorption is a highly efficient mechanism with low cost, low energy consumption, and a simple process. In this study, biomass-derived porous carbon materials (TCACs) were synthesized by pyrolysis activation combined with H3PO4, HNO3, and HCl. TCAC44 has the best activation conclusion, showing that surface area and pore volume were 1107 m2/g and 0.58 cm3/g treated with H3PO4 and so was chosen for subsequent benzene adsorption/desorption tests. The adsorption capacities of benzene for TCAC44 were increased from 58 mg/g for 35 °C + 95% RH to 121 mg/g for 25 °C + 15% RH and presented a higher adsorption capacity of benzene than TCAC101 and TCAC133. Otherwise, well recyclability of TCAC44 was revealed as the benzene adsorption capacity reductions were 22.49% after five adsorption-desorption cycles. Furthermore, the present study established the property-application relationships to promote and encourage future research on the newly synthesized innovative TCAC44 for benzene removal.

Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding

The high energy intensity and rigorous quality demand of injection molding have received significant interest under the background of the soaring production of global plastic industry. As multiple parts can be produced in a multi-cavity mold during one operation cycle, the weight differences of these parts have been demonstrated to reflect their quality performance. In this regard, this study incorporated this fact and developed a generative machine learning-based multi-objective optimization model. Such model can predict the qualification of parts produced under different processing variables and further optimize processing variables of injection molding for minimal energy consumption and weight difference amongst parts in one cycle. Statistical assessment via F₁-score and R² was performed to evaluate the performance of the algorithm. In addition, to validate the effectiveness of our model, we conducted physical experiments to measure the energy profile and weight difference under varying parameter settings. Permutation-based mean square error reduction was adopted to specify the importance of parameters affecting energy consumption and quality of injection molded parts. Optimization results indicated that the processing parameters optimization could reduce ~ 8% energy consumption and ~ 2% weight difference compared with the average operation practices. Maximum speed and first-stage speed were identified as the dominating factors affecting quality performance and energy consumption, respectively. This study could contribute to the quality assurance of injection molded parts and facilitate energy efficient and sustainable plastic manufacturing.

Status of sustainable manufacturing practices: literature review and trends of triple bottom-line-based sustainability assessment methodologies

Due to significant requirement of energy, water, material, and other resources, the manufacturing industries significantly impact environmental, economic, and social dimensions of sustainability (triple bottom-line). In response, today's research is focused on finding solution towards sustainable manufacturing. In this regard, sustainability assessment is an essential strategy. In the past, a variety of tools was developed to evaluate the environmental dimension. Because of this fact, previous review studies were grounded mostly on tools for green manufacturing. Unlike previous review articles, this study was aimed to review and analyze the emerging sustainability assessment methodologies (published from 2010 to 2020) for manufacturing while considering the triple bottom-line concept of sustainability. In this way, the paper presents a decade review on this topic, starting from 2010 as the guidelines for the social dimension became available in 2009. This paper has analyzed various methods and explored recent progress patterns. First, this study critically reviewed the methods and then analyzed their different integrating tools, sustainability dimensions, nature of indicators, difficulty levels, assessment boundaries, etc. The review showed that life cycle assessment and analytic hierarchy process-based approaches were most commonly used as integrating tools. Comparatively, still, environmental dimension was more commonly considered than economic and social dimensions by most of the reviewed methods. From indicators' viewpoint, most of the studied tools were based on limited number of indicators, having no relative weights and validation from the experts. To overcome these challenges, future research directions were outlined to make these methods more inclusive and reliable. Along with putting more focus on economic and social dimensions, there is a need to employ weighted, validated, and applicable indicators in sustainability assessment methods for manufacturing.

A critical analysis on the triple bottom line of sustainable manufacturing: key findings and implications

Because of the environmental consequences of manufacturing activities, the general public, industry, and academia are becoming more aware of sustainable manufacturing (SM), which incorporates environmentally friendly manufacturing processes while emphasizing overall triple bottom line (TBL) performance in manufacturing. This article employs various text mining techniques and bibliometric analysis including cluster analysis, Pearson coefficient and research landscape to conduct an extensive investigation on SM with a focus on the TBL, in which the research content of SM with the TBL is reviewed and discussed systematically from a wide angle and with reduced bias. In this study, three new indicators about the ratios of the number of scientific papers between social, environmental, and economic dimensions of SM are devised to show the weight and level of importance of dimensions in SM, covering scientific papers from 30 years. The findings from this study indicate that the influential power of SM varies across the three dimensions, with a particular emphasis on the social dimension of SM from various countries, implying a current state of imbalance status in TBL for SM, at the same time, the economic and environmental dimensions share similar research topics and academic emphasis in SM. Based on these findings, recommendations based on sustainable development goals (SDGs) of the United Nations (UN) are made to increase the social influence of SM. This article firstly reveals the individual status of the social dimension and the situation of unbalanced TBL in SM, providing sustainable suggestions for enhancing the effectiveness of SM and achieving balanced TBL regarding the SDGs.

Development of an empirical model to quantify carbon emissions for machining of cylindrical parts

As a result of growing environmental issues and stringent carbon emission (CEM) regulations imposed throughout the globe, low CEM has become one of the essential requirements of manufacturing industries. Low-carbon manufacturing, which aims to reduce carbon intensity and improve process efficiency, has evolved as emerging issue that has encouraged a lot of research into quantifying the CEM of different manufacturing processes. To comply with increasingly stringent CEM regulations and achieve low carbon manufacturing, manufacturing industries require accurate CEM data for their products. In this work, an empirical model is developed to quantify carbon emissions for machining of cylindrical parts. The CEM associated with a cylindrical part machining is decomposed into CEM from electrical energy consumption, material consumption, cutting tool wear, and coolant consumption and from the disposal of machining waste materials. Electrical energy consumption of a machine tool is further decomposed into different energy modules: startup, standby, spindle acceleration, idle, rapid positioning, air-cutting, and cutting for accurate quantification of CEM. Energy consumption models are developed for each module, and are integrated to quantify the total energy consumption of the machine tool. Finally, the developed model is applied on a cylindrical part with three different process plans to validate the developed model for practical implementation in industry. The proposed model can be utilized in the manufacturing industry to quantify carbon emissions based on different process parameters before machining a cylindrical part to achieve low carbon manufacturing process planning and scheduling.

Energy benchmark for energy-efficient path planning of the automated guided vehicle

The automated guided vehicle (AGV) is a piece of promising advanced transport equipment that has been widely used in flexible manufacturing systems to increase productivity and automation. Previous studies about the AGV focused on improving the capacities of perception, navigation, and anti-collision as well as reducing the transport time, cost, and distance, but insufficient attention was paid to the energy consumption (EC) reduction of AGV. The energy benchmark is recognised as an effective analytical methodology and management tool that can improve energy efficiency. Nonetheless, research on the energy benchmark for the AGV is lacking. To finish a transport task, many AGV path plans are feasible, and we develop an energy benchmark to evaluate each path plan and select the energy-saving one. We also establish a dynamic rating system of energy efficiency which is consistent with the energy-saving potentials of the transport task. The case study shows that the transport EC is reduced by 10.98 %, validating the proposed energy benchmark methodology. In addition, the effects of AGV path plans on the EC of machine tools at the workstations are analysed. Lastly, we explore the relationship between the energy efficiency of AGV path plans and the locations of workstations.

A decision support system for environmentally-sustainable strategies for the Mauritian Textile and apparel industry using system dynamics: The materials and land perspectives

As anywhere in the world, Mauritius has seen the depletion of its natural resources, mainly as a result of the effects of global warming. Industries need to recognize this and rethink on the way products are manufactured so as to minimize the negative impact of their businesses on the environment, the workforce and the surroundings. The main objective of this research is to investigate what support systems the Mauritian Textile & Apparel industry requires to embark on a sustainable manufacturing journey. The primary focus of the research is from the perspectives of the use of materials and land availability. This present work is not about "Greenwash", it is about analyzing the full range of economic, environmental and social benefits to support the transitions to sustainable business models over time. Development of methods that can support academics, researchers and industry practitioners within the textile & apparel industry to integrate sustainable manufacturing practices into their day-to-day practices. In a nutshell, this research provides a cut-and-dried and standardized approach for the stakeholders of the textile and apparel industry to shift to more sustainable practices by reshaping their resource flows. This study confirms the dynamic behavior of the textile industry and reveals that if the appropriate strategies and decisions are made, there is still hope for the survival of the industry in the future.

Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing

Energy modeling and cutting parameter optimization of the machining process have been recognized as powerful and effective ways to save energy. However, in the actual machining process, technologists often use empirical methods to determine the final cutting parameters. Due to the lack of theoretical support and optimization tools, this method is difficult to fully consider the constraints of machine tool capability, cutting tool performance, and workpiece material, which affects the overall performance of the machine tool to give full play. To address this problem, a multi-objective parameter optimization method of computer numerical control (CNC) plane milling for sustainable manufacturing was proposed in this paper. More specifically, three tasks were carried out: (1) an accurate milling energy model considering transient processes such as spindle acceleration was established; (2) a multi-objective parameter optimization model of CNC plane milling was established with cutting parameters as optimization variables and considering various complex constraints; (3) by drawing 3D surface maps, the internal relationship between the cutting parameters and the optimization index was presented in detail and intuitively. Finally, a case study was carried out in the XHK-714F vertical machining center. The results showed that the processing efficiency is improved by 21.0%, the energy consumption is reduced by 15.3%, and the surface roughness is reduced by 5.5% through the optimization of cutting parameters, which verified the effectiveness and feasibility of the proposed model and method.

End-of-life vehicles research development in Malaysia: a comprehensive review with the integrated conceptual model of innovative sustainable manufacturing elements

The end-of-life vehicles (ELV) issue has become an essential topic in the fast-growing automotive industry. This study utilizes comprehensive content analysis to critically review the recent ELV research developments and underpinning issues in Malaysia. Fifty relevant ELV studies in Malaysia from the year 2006 to 2021 are selected and categorized based on three innovative sub-elements (product, process, system) of sustainable manufacturing. The literature review findings show that sustainable product recovery and recyclability issues in ELV treatments are still a major concern. Current studies overlook specific research on sustainable and integrated processes for ELV treatment. There is still lack of detailed ELV implementation framework equipped with the documented procedures and appropriate industrial practices in the ELV ecosystem to optimize the ELV supply chain. ELV policy is yet to be enacted in Malaysia, and public awareness of ELV is still low. There is inadequate alignment in ELV research developments with the current National Automotive Policy 2020 in Malaysia. The proposed integrated conceptual model will provide an extensive overview for scholars, policy-makers, and ELV stakeholders to implement appropriate actions to improve present ELV businesses in line with the public readiness to enact the potential ELV directives or legislation in Malaysia.

Collaborative approaches in sustainable and resilient manufacturing

In recent years, the manufacturing sector is going through a major transformation, as reflected in the concept of Industry 4.0 and digital transformation. The urge for such transformation is intensified when we consider the growing societal demands for sustainability. The notion of sustainable manufacturing has emerged as a result of this trend. Additionally, industries and the whole society face the challenges of an increasing number of disruptive events, either natural or human-caused, that can severely affect the normal operation of systems. Furthermore, the growing interconnectivity between organizations, people, and physical systems, supported by recent developments in information and communication technologies, highlights the important role that collaborative networks can play in the digital transformation processes. As such, this article analyses potential synergies between the areas of sustainable and resilient manufacturing and collaborative networks. The work also discusses how the responsibility for the various facets of sustainability can be distributed among the multiple entities involved in manufacturing. The study is based on a literature survey, complemented with the experience gained from various research projects and related initiatives in the area, and is organized according to various dimensions of Industry 4.0. A brief review of proposed approaches and indicators for measuring sustainability from the networked manufacturing perspective is also included. Finally, a set of key research challenges are identified to complement strategic research agendas in manufacturing.

Filament-based 3D-printing of placebo dosage forms using brittle lipid-based excipients

In order to expand the limited portfolio of available polymer-based excipients for fabricating three-dimensional (3D) printed pharmaceutical products, Lipid-based excipients (LBEs) have yet to be thoroughly investigated. The technical obstacle of LBEs application is, however their crystalline nature that renders them very brittle and challenging for processing via 3D-printing. In this work, we evaluated the functionality of LBEs for filament-based 3D-printing of oral dosage forms. Polyglycerol partial ester of palmitic acid and polyethylene glycols monostearate were selected as LBEs, based on their chemical structure, possessing polar groups for providing hydrogen-bonding sites. A fundamental understanding of structure-function relationship was built to screen the critical material attributes relevant for both extrusion and 3D-printing processes. The thermal behavior of lipids, including the degree of their supercooling, was the critical attribute for their processing. The extrudability of materials was improved through different feeding approaches, including the common powder feeding and a devised liquid feeding setup. Liquid feeding was found to be more efficient, allowing the production of filaments with high flexibility and improved printability. Filaments with superior performance were produced using polyglycerol ester of palmitic acid. In-house designed modifications of the utilized 3D-printer were essential for a flawless processing of the filaments.

Discover the trend and evolution of sustainable manufacturing: a thematic and bibliometric analysis

Manufacturing, as one of the most important sectors in a civilized society, has a strong impact on our city's sustainability issues, and it is therefore justified in taking a more sustainable approach in the future. As a consequence, research works and the research trend of sustainable manufacturing (SM) play a critical role in supporting the sustainable development of industries. With the knowledge of the research themes on SM from the past to the present, preferred options for planning the future of manufacturing and executing SM could be offered to industries. Motivated by this, this study presents a thematic and bibliometric analysis on research papers of SM, with the goal of providing an overall overview of SM research trends, as well as identifying the critical time of having major breakthroughs and evolution of the corresponding research works by comparing the research themes across the longitudinal timeline. A thematic analysis was used to determine the keywords and main themes of the research on SM in various time frames, as well as the perspectives on how the research works in relation to current technology and dynamic changes. Finally, the three stages of SM research were determined based on the overall results. Furthermore, this article demonstrates the research directions and advancements of SM in 2020, presenting the most recent research trends in SM to both industry and academia.

Vaccines: New challenges, new paradigms, new opportunities: Report of the 22nd DCVMN Annual General Meeting

The Developing Countries Vaccine Manufacturers Network held its 22nd Annual General Meeting in October 2021. Vaccine manufacturing experts, leaders from global public health organizations and dignitaries from governments and multilateral organizations discussed the challenges and opportunities emerging from the COVID-19 pandemic. Over 350 delegates from 33 countries, representing over 70 organizations partook in the meetings deliberations. The development and scaled-up production of several safe and effective vaccines against COVID-19 resulted in over 12 billion doses being produced by the end of 2021. Unfortunately, this scientific achievement and outstanding industry effort has been overshadowed by the striking inequity in access to COVID-19 vaccines. High and upper middle-income countries have received 75% of the vaccines, while in Africa, less than 5% of the people are fully vaccinated. The inequitable access to vaccines is an issue of national health security, which has stressed the need to establish local vaccine manufacturing capacity in Africa. Key partnerships, initiatives and the deliberate strategies required to achieve sustainable manufacturing on the continent were discussed. The ability to acquire technology, access markets and financing mechanisms, and workforce development were reported as key enablers to achieving a healthy ecosystem. Innovative vaccine technologies, new regulatory approaches, and the importance of voluntary technology transfers in increasing the global supply capacity of both COVID-19 vaccines and traditional vaccines were highlighted. In reviewing the lessons learned from the pandemic, speakers shared a consensus that innovation and partnerships will be central to any solution proposed to mitigate the current pandemic and prepare for future ones.

Energy and CO2 emissions modeling for unconventional machining industry considering processing characteristics

Unconventional machining of WEDM (Wire Electrical Discharge Machining) is playing an increasingly important role in the manufacturing industry. The processing efficiency and resource consumption of this method are research hotspots from the perspective of sustainable development. Energy and CO₂ emissions modeling of process machining have been recognized as an effective and economical ways to achieve energy-saving, emission-reducing and to improve process efficiency. However, the predictive modeling of energy and CO₂ emissions in unconventional machining of WEDM machining has not been thoroughly fully studied. This paper proposes a predictive model of energy consumption and CO₂ emissions in WEDM process considering process characteristics. The application of the energy and CO₂ emissions model proposed in this paper in an example shows that the model's energy consumption prediction accuracy for single part processing reaches 96.5%, and the energy consumption prediction accuracy for batch processing is above 99%. A new standard for cutting fluid substitution with the best machining stability and energy consumption is proposed. In the example, it is also shown that the corners in the geometric structure will reduce the processing energy consumption. The smaller the number of single folding angles, the more energy consumption will be reduced. The processing energy consumption per unit area has a greater deviation when the thickness is low, and the thickness of the workpiece will also affect the life of the electrode wire. It depends on the number of multi-layer stacks and the life of electrode wires; the quality of machine tool auxiliary materials has a greater impact on energy consumption, with a difference of up to 40% in energy consumption. The results of this research can better understand the energy consumption and CO₂ emissions characteristics of the unconventional machining of WEDM.

A mathematical model for designing a reliable cellular hybrid manufacturing-remanufacturing system considering alternative and contingency process routings

Due to the stringent awareness toward the preservation and resuscitation of natural resources and the potential economic benefits, designing sustainable manufacturing enterprises has become a critical issue in recent years. This presents different challenges in coordinating the activities inside the manufacturing systems with the entire closed-loop supply chain. In this paper, a mixed-integer mathematical model for designing a hybrid-manufacturing-remanufacturing system in a closed-loop supply chain is presented. Noteworthy, the operational planning of a cellular hybrid manufacturing-remanufacturing system is coordinated with the tactical planning of a closed-loop supply chain. To improve the flexibility and reliability in the cellular hybrid manufacturing-remanufacturing system, alternative process routings and contingency process routings are considered. The mathematical model in this paper, to the best of our knowledge, is the first integrated model in the design of hybrid cellular manufacturing systems which considers main and contingency process routings as well as reliability of the manufacturing system.

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Sustainable manufacturing has grown widely owing to recent environmental issues. This study aims to develop a multi-objective multi-pass turning optimization model to determine the optimal cutting parameters, including spindle rotation speed, feed rate, depth of cut, and number of roughing passes. The optimization model considers several criteria in the key metrics of sustainable manufacturing, i.e., energy consumption, carbon emissions, production time, and production cost. A numerical example is provided to show the application of the model, including sensitivity analysis, to study the effects of several cutting parameters on the objective functions. The model can be used by manufacturing industries to improve their manufacturing process efficiency and simultaneously produce products that support sustainable manufacturing.

Sustainability of unconventional machining industry considering impact factors and reduction methods of energy consumption: A review and analysis

The unconventional machining (UCM) processes are usually energy-intensive and mainly used to process materials with characteristics of high-quality requirements and complex geometries, etc. In response to the policy of energy-saving and emission reduction in the UCM, this paper reviews the relative literature over the last decade with a focus on Wire Electrical Discharge Machining (WEDM), and a structured analysis of the impact factors is adopted in terms of the WEDM machine parts, workpiece, processing parameters, human resources consumption, and production management. On this basis, the prediction and reduction methods of energy consumption in WEDM are systematically summarized. The result shows that the energy-saving and emission reduction methods in the unconventional machining have focused primarily on the optimization design of machine tools, process modeling and optimization, and production management. Among which, these approaches such as process parameters modeling, machining state monitoring, and the significant components designing and optimizing have been widely studied. Besides, the existing research on the resource allocation management of processing tasks is mainly about workshop scheduling algorithm and process sequencing optimization. Finally, the sustainable manufacturing methods considering multiple aspects are discussed from the perspective of WEDM, which has great significance to the research direction and sustainability of the UCM.

Enhancing the effectiveness of AHP for environmental performance assessment of Thailand and Taiwan's food industry

The concept of green manufacturing is emerging as a means of enhancing a firm's competitiveness through intelligent systems and process improvement to eliminate adverse effects on the environment. However, environmental performance (EPA) is challenging from the decision-making perspective because of difficulty determining and prioritising the proper factors that have significant effect on a firm's EPA. This study was conducted with the objective of enhancing the effectiveness of the analytic hierarchy process (AHP) to assess EPA by integrating exploratory factor analysis and confirmatory factory analysis to validate suitable criteria and sub-criteria. A questionnaire survey was employed as a tool to collect data from 341 managers of Thailand and Taiwan's food industry and the AHP approach used for normalisation, ranking, and simulation of sensitivity analysis. The results obtained indicate that quality policy, quality assurance, and quality control, respectively, are the three most important factors in the measurement of EPA, whereas organisational support in innovativeness is assigned the lowest priority. Based on simulations for sensitivity analysis, the results can be applied to guide managers' decisions in the course of steering their firms towards sustainable manufacturing.

Towards a generalized energy prediction model for machine tools

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.