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Abstract: A key factor deciding the capacity to increase the sustainability of final products is the energy efficiency. The energy embodied in a product is an aggregation of all of the energy embodied in the products' components and subsystems, expended through its manufacturing processes and logistical activities. Currently, accurate estimation of this energy metric is hindered due to the unavailability of energy use data traceable to individual processes and equipment associated with the product's assembly. In this paper, we propose using minimally-required energy to compute energy efficiency of a product assembly process. Based on the proposed approach, efficiency metrics established on the process, product, material and equipment characteristics have been presented at the assembly activity and equipment level. A case study has been presented for a hybrid laser welding process to demonstrate the computational methods used to arrive at these efficiency metrics. Major contributions of this paper are the metrics development and exemplifying the metrics through an actual assembly process (hybrid laser welding) case study. We will explain how these metrics can provide industries with a capability to identify opportunities to improve their sustainability performance across their assembly processes. [Copyright &y& Elsevier]

Optimizing energy consumption has become a priority in advanced manufacturing industry due to increasing sustainability and environmental consciousness, resulting in various models to characterize the relationship between cutting parameters and energy consumption for material removal processes. These existing models are subject to a principle, the higher the material removal rate, the lower the specific energy consumption, without considering the effects of cutting parameter combinations at a certain material removal rate. To address this problem in minimizing energy consumption, three tasks are conducted in this paper. First, specific energy consumption is identified as an optimization objective and expressed as a function of spindle rotation speed, cutting speed, feed rate and depth of cut. Second, based on the candidate options, decision rules are proposed for selecting optimal cutting parameters to achieve minimum energy consumption. Third, the decision rules are applied with experimental data of rough external turning. This work is expected to assist computer numerical control machine tool practitioners in selecting optimal cutting parameters for minimum energy consumption. [ABSTRACT FROM AUTHOR]

Environmental Sustainability is one of the ‘Grand Challenges’ for manufacturing success in the 21st century. The present article mainly focuses on energy use during the manufacturing steps used for assembling products. In particular, industry still needs systematic and reliable ways to capture information for ultimately accounting for energy efficiency in manufacturing processes. For that purpose, an information model in the form of a formal ontology was created using information elements as building blocks, which emerge from manufacturing process models, supply chain operations, manufacturing process parameters, 3D form features and exergy-based thermodynamic analysis. These information elements were integrated by means of semantic relationships in an ontology. Once this information model was constructed, an application example of a welded hull panel assembly was used for verification of the model's representativeness and completeness. Results show that it is feasible to correlate all required data by means of semantic relationships for estimating energy efficiency indicators prior to physical prototyping, or ultimately comparing different alternatives for manufacturing processes regarding energy efficiency. However, manufacturing processes must be described in detail in order to allow energy efficiency analyses. In addition, geometric models need to be able to represent form features that can best relate to specific manufacturing process parameters. And finally, energy efficiency indicators, such as the degree-of-perfection can be derived from a comprehensive thermodynamic analysis, to be performed in each individual process step. Results indicate it is possible to provide an information structure for the calculation of the degree-of- perfection associated with an exergy-based thermodynamic analysis in assembly processes. Therefore, it has the potential to serve as a basis for a unifying framework for developing software applications that could allow the calculation of the degree of perfection in manufacturing operations. In addition, the resulting information model may facilitate data integration in the extended enterprise environment as well as the development of standards that can lead to seamless interoperability of manufacturing information systems. This paper presents the means by which energy efficiency can be accessed directly from an integrated semantic model, and opens the possibility to be extended for many different sorts of manufacturing processes. [ABSTRACT FROM AUTHOR]

Construction is nowadays considered an extremely energy intensive industry and one of the main sources of environmental pollution in the world. Therefore, the research and the development of novel energy-saving manufacturing processes and sustainable construction materials is more than ever urgent and challenging. This paper aims at identifying the industrial process for the production of novel geopolymers to be used as a greener substitute for cement, especially the Portland one, widely used for structural applications to significantly reduce the environmental impact of the construction industry. Here, the materials sustainability and the manufacturing process are improved by valorising and reusing wastes, deriving from the pulp-paper industry, as raw materials. This industry generates a great quantity of wastes that represent a real concern both for their environmental impact and cost of disposal. The new industrial process was evaluated after scaling up the laboratory procedure to an industrial dimension. Consequently, each small-scale process was deeply analysed (raw materials involved and processes) and transposed into a massive-production. The most relevant processes that show an elevated environmental impact were identified and the energy consumption of the whole process has been assessed resulting, for the analysed geopolymeric product, an embodied energy of 645 MJ/ton, being about one sixth of the OPC. Moreover, a preliminary economic analysis has been conducted in terms of raw materials involved in the proposed industrial process revealing a decreasing cost per ton increasing the waste recycling. All considered, the main conclusions are that the industrial scale up of the proposed geopolymeric product, assessed for the first time in literature along with the related proposed considerations, could be highly competitive to the Portland production involving greener processes with a much lower energy consumption and a greater financial saving by improving the wastes quantity. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic framework using Bayesian networks to aggregate the uncertainty from multiple sources for the purpose of uncertainty quantification (UQ) in the prediction of performance of a manufacturing process. Energy consumption, one of the key metrics of manufacturing process sustainability performance, is used to illustrate the proposed methodology. The prediction of energy consumption is not straightforward due to the presence of uncertainty in many process variables and the models used for prediction. The uncertainty is both aleatory (statistical) and epistemic (lack of knowledge); both sources of uncertainty are considered in the proposed UQ methodology. The uncertainty sources occur at different stages of the manufacturing process and do not combine in a straightforward manner, thus a Bayesian network approach is found to be advantageous in uncertainty aggregation. A dimension reduction approach through variance-based global sensitivity analysis is proposed to reduce the number of variables in the system and facilitate scalability in high-dimensional problems. The proposed methodologies for uncertainty quantification and dimension reduction are demonstrated using two examples – an injection molding process and a welding process. [ABSTRACT FROM AUTHOR]

Resource, economic, and environmental impacts are three major factors in the evaluation of industrial processes. All of them might imply certain contributions of ecosystems services. A comprehensive accounting of these factors is critical to sustainability assessment of industrial activities at the ecological scale. Ecological Cumulative Exergy Consumption (ECEC) analysis is a promising approach to address these issues. However, the current knowledge on ECEC is incomplete, especially in economic investment and environmental impact. In this paper, ECEC analysis is extended to quantify purchased resources and pollutant emissions of industrial production. Accordingly, an extended ECEC framework is proposed, integrating the resource, economic, and environmental factors. Furthermore, for better understanding of sustainability, a concept of Ecological Life Cycle Cost (ELCC) is put forward, revealing the relationship between the ECEC and traditional economic evaluation. Finally, a case study of China's raw coal production is used to illustrate the features of these proposed frameworks. The ECEC and ELCC analyses indicate the great ecological influence of raw coal which has been underestimated by the traditional assessment approaches. [ABSTRACT FROM AUTHOR]

Industrial manufacturing is the largest end-use sector in terms of both final energy demand and greenhouse gas emissions (more than 30% of the total); its increase is rapidly altering the world climate. The need to mitigate the environmental impacts of manufacturing processes makes energy efficiency a key success factor for sustainable production. Accordingly, the scientific community's interest in energy management has grown considerably, resulting in several literature reviews on energy modelling and production systems analysis, emissions calculation, sustainability tools and benchmarking techniques. However, a comprehensive analysis of methods and tools aimed at improving energy awareness and assessing their effects on energy efficiency is lacking. To address this gap, this paper undertakes a systematic literature review of energy assessment methods and tools. From the 1367 papers retrieved by searching scientific literature databases, 64 scientific articles met the inclusion criteria and were analysed in detail. The study aims to provide scholars with a picture of the current state of scientific research and to identify the scientific works that could help industry practitioners in energy management. Following the ISO 50001 framework, the methods and tools were divided into three main groups (i.e. energy analysis, energy evaluation and energy-saving measures methods) and the specific findings relating to each group were synthesized. Finally, the paper addresses unresolved issues and challenges and makes suggestions for future research directions. • A systematic literature review of energy assessment methods and tools are provided. • Relevant concepts, themes and characteristics has been summarized. • A classification of energy assessment methods and tools has been presented. • The limits and the possible solution strategies to overcome them are discussed. [ABSTRACT FROM AUTHOR]

The stamping industry is critical in the automotive and aerospace industries, and the environmental impacts and sustainability of stamping systems have attracted considerable attention. To analyze the environmental impact and sustainability of stamping systems quantitatively, this paper presents an emergy-based evaluation method considering all the associated environmental impact factors of a pressing workshop including material and energy consumption, waste flows, time and mass losses, and processing costs. On the basis of analyzing the stamping processes and sustainability, this method comprises data collection, emergy based sustainability evaluation model and the development of sustainable evaluation indexes and systems. Based on the proposed method, the environmental impact of an industrial vehicle stamping workshop was evaluated. The results show that the net emergy yield ratio of the stamping system was greater than 2, and the waste emergy output ratio was very low, indicating that the system had high productivity levels, high yields, and low pollution discharge rates. However, its sustainability index was less than one owing to its high energy and material consumption during the production processes. To reduce the environmental impact of this stamping system and improve its sustainability, we investigated the potential improvements that could be achieved in energy consumption, productivity, and environmental impact by implementing energy-saving scheduling in the stamping workshop. The results show that the corresponding net emergy yield ratio and sustainability index of the system were both improved. The proposed emergy-based method could be used to quantitatively evaluate the environmental impacts and sustainability of stamping systems and facilitate efforts to reduce the environmental impacts of pressing workshops while improving their productivity and economic profitability. [ABSTRACT FROM AUTHOR]

Dielectric medium (kerosene) between the tool electrode and workpiece forms a reaction product, owing to thermal effect of discharge in the process of electrical discharge machining (EDM), which is emitted from the surface in the form of harmful gas and may get into the operator's body by breath system inhalation and contact with skin. Therefore, exhaust emissions should be considered as important performance indicators for the green machining in the process of EDM. Moreover, energy efficiency and machining productivity should be improved and the cut process must take ecological constraints into consideration. Thereafter, the definition of green manufacturing helps to evaluate different and sometimes conflicting aspects of the manufacturing process, such as machining productivity, surface quality, energy efficiency and exhaust emissions. But it is difficult to dynamically adjust the weights of different aspects according to the actual processing conditions. This paper proposes a framework for optimization of cutting parameters to minimize energy consumption and exhaust emissions during electrical discharge machining of Al 6061 and SKD 11. Three cutting process parameters (peak current, pulse period, and duty cycle) are tested against material remove rate (MRR), surface roughness (Ra), energy efficiency per volume (EEV), and exhaust emissions characteristics (EEC) for EDM of these two materials. First, the process energy and exhaust emissions are analyzed. The results show that EEV is negatively correlated with surface quality (Ra) for both Al 6061 and SKD 11 material. Second, two data analyses (Taguchi and adaptive-network-based fuzzy inference system (ANFIS) analyses) are utilized to obtain high MRR and best Ra, EEV and EEC for the desired machining performances and environmental sustainability. It is found that the relative errors between the predicted values and experimental results of MRR, Ra, EEV and EEC are all not more than 20%, and found that the maximum of mean relative errors of them is 13.04%. Finally, a framework implemented by an optimization system, based on ANFIS, enhanced cuckoo search (ECS) algorithm and desirability function, is built to obtain the desired cutting parameters. It is found that high peak current (8.94A) and pulse period (500 μ s) are recommended to obtain multi-objective optimization for machining Al 6061, and that the optimal solutions of the EEV and EEC are significantly decreased, by 87.13% and 37.09%, respectively. Similarly, high peak current (8.77A) and pulse period (261.5 μ s) are recommended to obtain multi-objective optimization for machining SKD 11, and the optimal solutions of the EEV and EEC are significantly decreased, by 93.73% and 46.85%, respectively. Thereafter, it can be concluded from the above research that the proposed technique offers significant advantages and potential for applications in the green manufacturing field. Image 1 • Energy efficiency per volume was negatively correlated with Ra. • Maximizing energy efficiency and minimizing exhaust emissions by empirical models were obtained. • Balancing the machining performances and environmental sustainability was considered. • An optimization system with manual multi-objective weights was build. • Green regions of exhaust-friendly and energy-friendly were acquired. [ABSTRACT FROM AUTHOR]

Manufacturing industries are faced with environmental challenges, so their industrial processes must be optimized in terms of both profitability and sustainability. Since most of these processes are dynamic, the previously obtained solutions cannot be valid after disruptions. This paper focuses on recovery in dynamic job-shop scheduling problems where machines can work at different rates. Machine speed scaling is an alternative framework to the on/off control framework for production scheduling. Thus, given a disruption, the main goal is to recover the original solution by rescheduling the minimum number of tasks. To this end, a new match-up technique is developed to determine the rescheduling zone and a feasible reschedule. Then, a memetic algorithm is proposed for finding a schedule that minimizes the energy consumption within the rescheduling zone but that also maintains the makespan constraint. An extensive study is carried out to analyze the behavior of our algorithms to recover the original solution and minimize the energy reduction in different benchmarks, which are taken from the OR-Library. The energy consumption and processing time of the tasks involved in the rescheduling zone will play an important role in determining the best match-up point and the optimized rescheduling. Upon a disruption, different rescheduling solutions can be obtained, all of which comply with the requirements but that have different values of energy consumption. The results proposed in this paper may be useful for application in real industries for energy-efficient production rescheduling. [ABSTRACT FROM AUTHOR]