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Microalgae can be part of the solution to the global food crisis, as they have high nutritional values. Recently, 3D printing of microalgae-enriched snacks has been reported with the capability to customize nutritional profiles, shapes, and textures of the snacks. Because the process parameters of extrusion-based 3D printing affect the printability of cookie dough, it is important to know the levels of process parameters leading to continuous extrusion. This study investigated feasible regions of printing process parameters for continuous extrusion of microalgae (Arthrospira Platensis) enriched cookie dough. The process parameters studied were nozzle diameter, printing speed, and air pressure. The feasible regions were determined by visual inspections of printed strands. The results show that, for smaller nozzle diameters and higher printing speeds, higher air pressures are required to ensure continuous extrusion. The identified feasible regions from this study would be helpful when deciding the appropriate nozzle diameter, printing speed, and air pressure to print microalgae-enriched cookie dough and other materials with similar rheological properties in extrusion-based 3D printing. LESS      Full article

With the increasing concern due to climate change caused by a higher atmospheric concentration of CO₂ and other greenhouse gases, reducing environmental impact is becoming more important for every part of society. Manufacturing is responsible for a significant amount of energy/material consumption and environmental burden and, therefore, has a great opportunity to reduce its impact through green manufacturing. Green manufacturing presents opportunities across the manufacturing enterprise to increase the efficient usage of energy and material resources. These opportunities include designing products to consume fewer materials and energy during manufacturing and use, incorporating more efficient manufacturing processes, streamlining and optimizing manufacturing schedules and plans, and circularizing products. The goal of this paper will be to provide a perspective from the authors on the opportunities that exist within green manufacturing for discrete products through a review of pertinent topics and future directions. The paper will focus on processes, manufacturing equipment, manufacturing systems, recovering value at a product’s end-of-life, and additional thoughts that include metrics and indicators, techno-economic assessment, and a discussion of efficiency and effectiveness. Key findings from this review include a need for social indicators and renewable energy considerations in scheduling and process planning, integrating Industry 4.0 into circular economy along with social and institutional dimensions, consistency in the ability to measure and conceptualize metrics and indicators, a detailed evaluation of the life cycle impacts and cost of Addit Manuf, and more human and environment-oriented considerations for smart manufacturing. LESS      Full article

Mechanical equipment is a significant contributor to carbon emissions. By analyzing the life cycle carbon emissions of mechanical equipment, it can be obtained that the consumption of materials and energy are the key factors. Focusing on decreasing material and energy consumption in the life cycle of mechanical equipment, a series of low carbon design strategies are proposed, including material selection, lightweight design, and design for disassembly and recycling; specifically, in this paper, low carbon operation strategies on the machine and workshop levels are discussed. Operations including power matching, energy recovery, and transmission chain shortening can be performed at the machine level, as well as scheduling and production optimization at the workshop level. The proposed method is applied to a piece of typical equipment, a hydraulic forming press, and results show that the proposed low carbon manufacturing methods have significant carbon emission reduction potential. Combined with current research hotspots, the integration of design methods and tools and the carbon emission reduction techniques enabled by intelligent manufacturing are future directions. LESS      Full article

With the increasing environmental pressure, reducing the environmental impact on hybrid flow shops (HFS) has attracted extensive attention because of its broad industrial applications. The selection of machines in the implementation of HFS is a complex decision-making process that hinders the optimization efficiency in shops. However, this issue has not been addressed thoroughly. In light of this, a multi-objective mathematical model is formulated for the minimization of makespan and energy consumption of the hybrid flow shop scheduling problem (HFSP). The environmental performance of machines is calculated by the proposed evaluation index and is ranked on the basis of the integrated entropy and fuzzy technique for order performance by similarity to ideal solution method. Moreover, to solve the multi-objective model of HFSP, an improved differential evolution algorithm with a heuristic active decoding rule that incorporates the ranking of environmental performance of machines into the iterative process of the algorithm is presented. Finally, a case study is presented to evaluate the effectiveness of the proposed method and to prove the feasibility of the ranking-based differential evolution algorithm (RBDE). The result shows that the proposed RBDE outperforms RBNSGA-II and RBPSO in searching for non-dominated solutions that can solve the environmental production of HFSP effectively. LESS      Full article

In this paper, a cradle-to-gate life cycle assessment (LCA) method is used to compare the overall energy consumption and environmental impact of hybrid additive manufacturing (HAM) and the traditional CNC milling process with a case study of turbine blade manufacturing. Six environmental impacts are assessed in this study: acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), photochemical ozone creation potential (POCP), ozone depletion potential (ODP), and abiotic depletion potential (ADP). The results suggest that HAM can not only reduce energy consumption and material waste but also reduce the environmental impact by 53% from a life cycle perspective. Specifically, the results of GWP, AP, EP, ODP, POCP and ADP of the HAM are only 32.2%, 34.6 %, 44.7%, 27.2%, 25.6%, and 24.7 % of that in traditional CNC machining. LESS      Full article

Aim: The aim of this study is to shorten the product design and development cycle to meet the continuous change and growth of customer environmental requirements.Methods: The applied research method introduces the concept of the inheritable structure of green products through structural similarity theory and quantitates environmental performance. Additionally, based on the analysis of the obtained customer ecological requirements, a hierarchical mapping method for customer requirements-environmental performance weight-product structure is proposed, which achieves the rapid mapping of customer requirements (CRs) to product structure in the product design process. Then, four mutation operation methods (combination, decomposition, replacement, and material change) are combined with a genetic algorithm, and a method for generating a product structure mutation map is proposed to solve the problem of the non-inheritance of some structures in the product design process. Finally, considering the transmission shell as an example, the validity and feasibility of the method are verified.Results: The case study showed that the overall mass of the rear derailleur dropped by 27.58 kg, and the environmental performance of the transmission was mainly related to its mass; hence, the proposed improvement method effectively improved the environmental performance of the transmission.Conclusion: The hierarchical mapping method for customer requirements-environmental performance weight-product structure and the generation method for the product structure variation diagram proposed in this study can shorten the product design and development cycle. This research can provide theoretical and methodological support for product design and development. LESS      Full article

Aim: This work explores the opportunities and challenges associated with the integration of Product Service Systems (PSS) into Smart Grids (SGs) with the aim of improving manufacturing and production systems’ sustainability under the Society 5.0 framework.Methods: A bibliometric analysis was conducted to examine the existing bibliographic material and identify the primary scientific directions for this research area. The aim was to provide a thorough understanding of the research problems and perform an in-depth analysis of the distinctive aspects of development of the scientific research in this field and visualize the results with VOS viewer tool.Results: Industry, society, and academia are being reshaped and reorganized to integrate new information and communications technology (ICT) to enable complete digitization and digitalization of their infrastructures. This trend is driven by the need for more resilient, environmentally sustainable, and human-centric systems, as indicated by the terms Industry 5.0 and Society 5.0. Electrical power generation and distribution currently are critical issues for society. In this context, SGs are examined from the perspective of product-service systems (PSSs). The key enabling technologies are discussed and a technical discussion based on presentation of implemented frameworks follows.Conclusion: Intelligent societies will become the new reality. It is necessary to adapt existing infrastructures toward human centricity, resilience, and sustainability. SGs require further development, with several challenges still to be addressed. However, engineers have several technologies available, including blockchain and PSSs, to promote client activity in societal infrastructures. LESS      Full article

The big data concept has been explosive, revealing, and transformative across manufacturing industries as it provides deeper insights into manufacturing operations for decision-making. However, big green data (BGA), a dedicated subset of big data, is not adequately structured for comprehensive sustainability analysis, particularly in smart factories. With our proposed green data balance (GDB), there will be accountability for each input and output composition in a production unit within the production value chain (PVC). Data will be exhaustively and accurately collected in each workshop to help uncover unknown issues in a production value chain while facilitating the development of sustainability metrics or index systems. Additionally, a structured big green data system will fuel “greentelligence”, using intelligent systems and technologies to speed up digitalization toward sustainable manufacturing by measuring, tracking, and minimizing adverse environmental impacts. Lastly, with the support of the cognitive intelligence data analytic system (CIDAS), real-time and near real-time comprehensive sustainability analytics can be performed, leading to Self-X metacognitive adjustments and corrective actions. LESS      Full article

Laser surface treatment (LST) is essential for advanced manufacturing but is extremely energy intensive. Being energy-aware is imperative as the industry pays increasing attention to energy management and environmental protection. However, existing literature mainly focuses on the laser-material interaction in LST, while few studies have considered energy consumption when investigating the processing quality. In this article, three metamodels (Kriging, RBF, and SVR) are integrated into an ensemble of metamodels (EM) by suitable weight coefficients, and the EM incorporates the predictive advantages of different metamodels. The EM establishes the relationship between laser process parameters (laser power, scan speed, and defocusing amount) and three outputs (total energy consumption, surface roughness, and depth-width ratio of LST track). The effectiveness of the presented prediction approach is validated by the leave-one-out method and additional experiments. Furthermore, the main influences of process parameters on the three outputs are studied. According to the technique for order preference by similarity to an ideal solution (TOPSIS), the optimal process parameter is Group No. 2, with the relative closeness of 78.04%, while the worst one is Group No. 13, with the relative closeness of 2.21%. The presented prediction approach can serve as a reliable foundation in the energy-aware application of laser processing. LESS      Full article

Magnetic field-assisted forming represents an environmentally friendly, contactless and high-efficiency technology for the development of advanced in situ manufacturing. The application of this technology provides a new concept for improving the properties of ferromagnetic wear-resistant coatings. In this study, we combine magnetic field-assisted forming with supersonic plasma spraying to prepare a Ni-based coating assisted by a transverse static magnetic field. The porosity of the magnetic field-assisted coating is below 2%, the hardness of the coating increases from 638.46 to 785.45 MPa and the tribological coefficient decreases from 0.466 to 0.422. The presence of the static magnetic field directly affects the bubble movement during the forming process of the coating, which makes the bubbles escape outward and reduces the porosity of the coating. The presence of the static magnetic field also further improves the phase structure of the coating, so that the magnetic domain distribution is uniform and the hard phase inside the coating increases. Finally, the residual stress and tribological properties of the coating are also improved. LESS      Full article