Remanufacturing is one of the fast-growing industries for sustainable manufacturing (SM) development. However, there is still lack of attention given to Industry 4.0 technologies application, specifically in the remanufacturing industry. This study explicitly performed a content analysis of Industry 4.0 technologies in the remanufacturing context. Thirty-six relevant studies from 2017 to 2021 were selected for critical review. The identified research gaps for future study are: The existing studies only take into account single elements of SM, not the three integrated elements of SM in Industry 4.0 technologies on the development of remanufacturing. There is a need to identify the integrated product and process data-driven sharing method to improve the efficiency in remanufacturing operations throughout the supply chain management system. It is suggested that a framework for the implementation of sustainable remanufacturing that incorporates the key elements of Industry 4.0 technology to promote SM advancement toward the circular economy should be developed.
Industry 4.0, the fourth industrial revolution, offers a chance to enhance the sustainability of manufacturing. It leverages interconnectivity, data sharing, and smart machines/production systems/supply chains. Internet-connected smart machines improve productivity, and energy efficiency, and reduce environmental impact. Machine sensors provide data for analysing energy consumption, optimising efficiency, and quantifying environmental impact. Data assists in scheduling, improving throughput and reducing energy use in production systems. Sharing data across the supply chain decreases energy wasted in unnecessary movements. This paper reviews the literature on Industry 4.0, focusing on smart and sustainable manufacturing at the machine/process, production system, and supply chain levels. It explores applications, benefits, challenges, opportunities, and suggests future research.
To promote sustainable manufacturing it is imperative that closed-loop material flows are considered during sustainable product design. The usefulness of various sustainable product design decision support tools depends on the ability to access accurate data related to the different lifecycle stages. The importance of digitally integrating disparate data sources and the potential for using digital threads to facilitate integration has been widely discussed. However, the approach to develop such digitally integrated capabilities is lacking in extant literature. This paper presents the development of an interoperable digital thread to integrate data from different sources across the total lifecycle interfaced with a suite of decision support tools. The application of the digitally integrated capability is demonstrated using an industrial case study. Results demonstrate the effectiveness of the digital thread to acquire data from different lifecycle stages and to conduct sustainability performance analyses to help identify the most sustainable product configuration designs.
A low-carbon transition is underway in the Australian ammonia industry, underpinned by the development of intelligent technologies. A novel framework to assess the sustainability of ammonia production is developed herein by utilising the triple bottom line (TBL) methodology. As the first TBL study of the ammonia production process, industry and academic professionals are consulted to establish nine weighted performance indicators using survey data. Three scenarios are evaluated, involving increasing levels of automation and Industry 4.0 technologies applied to blue ammonia production. Process data are acquired from literature and a local case study to validate the assessment procedure. An aggregated sustainability index is generated, simultaneously considering a firm's environmental, social, and economic performance relative to the increasing technological advances. The resulting analysis determined scenario 3 as the most sustainable manufacturing process. This suggests further implementation of emerging technologies will continue to yield positive sustainability outcomes for each bottom line.
Image processing and machine learning were applied to detect production quality characteristics of parts printed via fused deposition modelling. The influence of the nozzle temperature, infill density, and feed rate on the surface roughness and energy consumption of the printed parts was analysed. The surface roughness of the printed parts was predicted using a fine tree machine learning model; the infill density and feed rate were positively correlated to energy consumption, while temperature had little effect on energy consumption. Process parameters for 3D printing are recommended to achieve the desired surface quality, while avoiding print failure and excess energy consumption.
To prevent material starvation and global warming caused by manufacturing, disassembly systems for end-of-life (EOL) products should be environmentally and economically designed to promote a closed-loop supply chain for assembly products. With parts selection in the disassembly systems, parts/materials with higher CO2 volumes should be recycled for environmental reasons. On the other hand, parts/materials with higher profit, which is the difference between the revenue of recovered materials and disassembly costs, should be disassembled for economic reasons. A disassembly system design considering not only the environmental loads but also the recovered parts/materials is proposed by using a product lifecycle management (PLM) tool. However, from a technical and financial standpoint, it is not easy for the disassembly factory sites to create the 3D-CAD models and obtain the environmental information using the PLM tool. This paper proposes a disassembly system design with the environmental and economic parts selection using a life cycle inventory database by input-output tables.
Rapid industrialisation had led to a scarcity of resources. The concept of sustainable manufacturing has emerged to address this scarcity and to minimise environmental degradation. 3D printing also known as additive manufacturing, could potentially reduce material wastage, energy consumption and resulting emissions. A 'techno-eco-efficiency' framework was developed to produce technically, economically, and environmentally feasible centrifugal pump impellers 3D printed using the fused filament fabrication process. Firstly, surface properties, geometric properties, build material properties, static structural and dynamic properties, and the hydraulic performance of impellers were assessed in order to investigate how process parameters, such as infill pattern, infill rate and reinforcement material affect the technical performance. Secondly, the eco-efficiency performance of technically suitable impellers was assessed using environmental life cycle assessment, life cycle costing tools and portfolio analysis. Thus, this 'techno-eco-efficiency' framework was used to achieve sustainable manufacturing and could act as a decision support tool for selecting cost-competitive, environmentally benign, and technically feasible products. Alternatively, it would assist product designers and manufacturers to minimise a trade-off between technical and resulting eco-efficiency performance.
Minimising energy consumption and making machining processes more environmentally friendly are the two essential requirements of sustainable machining. As a result, development of technologically advanced yet efficient machining processes with minimum energy consumption and least toxic wastes is an evolving field of research and study. In this paper, effects of several lubrication techniques as well as different machining strategies (classic and hybrid), when machining Ti-6Al-4V, have been studied. Three different factors, namely energy consumption, surface quality and flank wear have been measured during the tests to evaluate the effectiveness of different lubrication techniques and machining strategies. Analysis of variance (ANOVA) has also been employed to analyse the influence of process parameters on the above-mentioned factors to determine the optimum levels of machining parameters and verify the experimental results. Accuracy of the model has finally been verified using the ANOVA results.
In order to characterize the environmental performance of Additive Manufacturing (AM) processes, comparative analyses are required.Different manufacturing approaches (such as additive and subtractive ones), besides adopting different equipment, use different kinds and amounts of material.Therefore, the material-related flow has to be followed throughout the entire product life.Differences in environmental impact arise at each step of the life cycle: material production, manufacturing, use, disposal, and transportation.A life cycle-based methodology able to take due account of all the factors of influence on the total energy demand for the production of metal components is given in this paper.Decision support tools for identifying the most sustainable manufacturing route (subtractive versus AM-based approaches) are presented for different scenarios.The aim of the present paper is to contribute to the debate concerning the environmental impact characterization of AM processes.
In the scientific literature there is a scarcity of comprehensive and organic studies on performance indicators encompassing sustainability and their influence on decision-making. This work aims at selecting the most suitable material to manufacture an automotive component using a high pressure die casting (HPDC) process according to four classes of metrics: cost, time, quality and sustainability. The performance of three different alloys (aluminium-A380, magnesium-AZ91D and zinc-ZA8) was evaluated considering overall product life cycle aspects and process characteristics through a deterministic technique for order of preference by similarity to ideal solution (TOPSIS). Results show that the zinc alloy should be chosen on a unit mass-basis mainly thanks to its significantly superior quality and sustainability performance. This study demonstrates that the inclusion of the sustainability dimension in a multi-criteria decision analysis context challenges well-established material selection trends in the automotive industry developed during the past decades.
The objective of this research is to investigate the morphology and microstructure of the chips formed during milling of Ti-6Al-4V alloy using conventional flood coolant and sustainable dry and MQL machining conditions. It was found that the chips formed in dry machining suffered a higher degree of serration indicating higher chip temperature and ineffective cooling of the chips. The bi-modal structure of Ti-6Al-4V remained the same in the bulk part of the chips. However, phase transformations were seen for all three machining conditions at the shearing plane of the chips. The chips formed in dry machining had comparatively higher percentage of β-phase due to phase transformation. For the chips obtained in flood coolant machining, the transformed β-phase possibly returned to martensitic α-phase due to rapid cooling. The MQL machined chips had the least transformation of β-phase, indicating minimal changes in mechanical properties of the machined parts in sustainable MQL machining.
Mobility and transnational migration are current social developments among the population of the European Union. These developments in both society-at-large and companies, linked to the challenges of sustainability, lead to new requirements for working in the European Union. Teaching and learning in higher education needs to adapt to these requirements. As a result, new and innovative teaching and learning practices in higher education should provide competencies for transnational teamwork in the curriculum of tomorrow's engineers in order to ensure their competitiveness in the job market. A transnational project-oriented teaching and learning framework, which provides the future key competencies for young engineers was implemented in the course European Engineering Team (EET). Engineering students from four countries participated in a new project-based course that focused on the development of innovative and sustainable products and opportunities. The goal of this paper is to present results and lessons learnt from three cohorts of EET.
High process forces and temperatures in turning operations cause high tool wear rates. Tool wear such as flank face abrasion has direct impact on workpiece geometry and resulting surface roughness. Since tools are used until tool life criterion is reached, surface quality can vary widely over the workpiece even when constant cutting parameters are utilised. A measurement system based on laser triangulation has been developed which enables the online measurement of surface roughness on the workpiece during the turning process. Using the online surface roughness measurements, closed-loop controllers were developed in order to adapt the tool feed and the cutting velocity to retain constant surface roughness even when tool wear is progressing. An optimised process with constant cutting parameters was benchmarked to the developed processes with adaptive cutting parameters. It can be shown that parameter adaption has the potential to lead to efficient processes and increases the tool-lifetime TToollife significantly.
A major limitation in sustainable product design is the lack of comprehensive methods to evaluate the effect of various risks on its total lifecycle sustainability performance. Most risk management methods are qualitative in nature, making them unsuitable to fully capture the interdependencies between risk events. In this paper, we propose a methodology for identifying risks related to a product design over its total lifecycle and developing a risk network map to capture the interdependencies between these risks. A Bayesian belief network-based method is employed to quantitatively model and evaluate risks and to conduct risk sensitivity analysis on the total lifecycle sustainability performance. An industrial case study is presented to demonstrate the application of the proposed methodology and evaluate risks related to toner cartridge design. Sensitivity analysis is conducted to assess the likelihood of performance measures such as total lifecycle cost, global warming potential (GWP), water and energy use being influenced as various risks related to the product design changes. The proposed methodology can be useful for product designers to assess how different product design performance can be affected by risks and identify designs that will meet desired performance indicators.
Hard machining of brittle materials such as ceramics is a process-oriented challenge. For the machining of such materials, abrasive water jet cutting is an appropriate alternative to the commonly used diamond grinding and laser cutting processes. In abrasive water jet machining the injection technology is currently used almost exclusively. Due to the realisation of the suspension jet technology a higher cutting performance can be achieved. In this article, the known injection technology and the suspension technology are compared. To do so, the influences on the quality were examined for trimming technical ceramics. The investigation illustrates that suspension technology trims with a higher accuracy than injection technology in matters of kerf geometry but with a lower material removal rate.
The tea industry is one of the main export earners of Sri Lanka with over 150 years of history. It faces severe threats to sustainability due to contamination issues, low productivity, yield drop, climate changes, labour shortage, internal migration of workforce etc. This study focuses on examining the environmental and social impacts of Sri Lankan tea processing industry to withstand global market challenges. The environmental impacts of tea processing were analysed using life cycle assessment techniques, while social impacts were evaluated by UNEP framework on social life cycle assessment. The fieldwork was carried out in one of the tea plantation companies located at multiple areas, within and external to the tea processing needs improvements in terms of environmental and social sustainability. Furthermore, the selected plantation company is lagging behind environmental sustainability when compared to the reference factory of Tea Research Institute Sri Lanka. Recommendations are provided to mitigate environmental and social hotspots identified in the study.
Environmental and societal concerns have fuelled an ever growing need for more sustainable products and machining processes. Much research has been focused on this issue in aviation, automotive, and medical industries where austenitic stainless steels have been often used. During machining of these materials, high cutting forces and carbon emissions make the machining process significantly more challenging. Therefore, in this study sustainable orthogonal turning experiments were conducted using dry cutting, MQL, and cryogenic cooling at different cutting speeds and undeformed chip thicknesses. Experimental cutting forces were measured and used to analytically determine the carbon (CO2) emissions. In order to determine the optimal machining parameters for minimising the CO2 emissions and the overall economic cost with improved human health conditions, a multi-objective optimisation problem was established. The optimal machining parameters were determined to be a cutting speed of 100 m/min and undeformed chip thickness of 0.12 mm, while using cryogenic cooling.
The various challenges of a sustainable industrial production such as demographic change and resource scarcity induce the increasing need of a resource-efficient, eco-friendly, flexible and adaptive production with human-centred and ergonomic suitable working stations and conditions. The novel production approach of human-robot collaboration promises to contribute to meeting these requirements. Nevertheless, for designing and realising concrete applications, a significant evaluation comprising the economic, ecological and social dimensions of sustainability is needed. The paper presents a methodology for such evaluations of human-robot collaboration with respect to their contribution to sustainability. Additionally, the application of the methodology is illustrated by assessing a human-robot collaboration solution in a concrete industrial use case.
An efficient learning environment is required to cope with today's increasing innovation speed. Companies need methods and tools to transfer knowledge to employees in a fast way. Learners' cognitive focus should be shifted towards learning at the learning object, instead of transferring information from teaching material to the real world. Current learning environments are mostly incapable to merge physical learning tools with digital content at its point of use; therefore, the learner has to do it. Augmented reality offers the opportunity to show learning content directly on physical objects and to interact with it. Within this paper, two approaches on how to use augmented reality for teaching purposes are shown. One is for special machinery assembly of turbomachinery and the other for cocoa liquor production.
