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To provide affordable energy-saving solutions for the small and medium-sized manufacturers (SMMs), we propose a unified framework for generating predictive models that support real-time disaggregation of power consumption from combined inputs, enabling automatic machine state identification simultaneously for joint analysis of energy usage patterns. The proposed framework transforms raw power consumption into a time series with look-back and bootstrap capabilities for historical pattern detection, while a learning architecture utilizes the stacked long short-term memory (LSTM) layers as encoders for embedding generation with sequential awareness. Experimental results demonstrate 93.65% minimum accuracy in ideal case of real-time energy usage and machine state prediction. [ABSTRACT FROM AUTHOR]

Disassembly lines are the most effective way to address large-scale value recovery from end-of-life (EOL) products. Disassembly line balancing (DLB) greatly affects the economics and throughput of EOL product processing. Complete disassembly is generally not suitable for disassembly enterprises; most often, the maximum profit is realized through partial disassembly. Thus, this article proposes a partial disassembly method and establishes a new DLB model that addresses both economic benefits and environmental impacts. The objective of the model is to maximize the effectiveness of workers, increase profit, reduce energy consumption, and balance the loads of workers. Moreover, the model considers the impact of disassembly face and tool changes on the disassembly process. A discrete multiobjective artificial bee colony (MOABC) algorithm is developed, and it takes the precedence constraints into account to obtain the Pareto solutions. The MOABC algorithm is applied to the disassembly lines of two real-world EOL products, including those of an LCD TV and a refrigerator. Experiments show that the performance of the MOABC algorithm is better than those of five well-known multiobjective algorithms. The proposed model and method can provide multiple disassembly schemes for decision makers of disassembly enterprises based on their preferences. [ABSTRACT FROM AUTHOR]

Over the last decade, manufacturing companies have identified renewable energy as a promising means to cope with time-varying energy prices and to reduce energy-related greenhouse gas emissions. As a result of this development, global installed capacity of wind power has expanded significantly. To make efficient use of onsite wind power generation facilities in manufacturing, production scheduling tools need to consider the uncertainty attached to wind power generation along with changes in the energy procurement cost and in the products' environmental footprints. To this end, we propose a solution procedure that first generates a large number of wind power scenarios that characterise the variability in wind power over time. Subsequently, a two-stage stochastic optimisation procedure computes a production schedule and energy supply decisions for a flow shop system. In the first stage, a bi-objective mixed integer linear programme simultaneously minimises the total weighted flow time and the expected energy cost, based on the generated wind power scenarios. In the second stage, energy supply decisions are adjusted based on real-time wind power data. A numerical example is used to illustrate the ability of the developed decision support tool to handle the uncertainty attached to wind power generation and its effectiveness in realising energy-related objectives in manufacturing. [ABSTRACT FROM AUTHOR]

The environmental performance of machining processes is of extreme importance in the field of sustainable manufacturing. The development of an environmental impact assessment method is a crucial strategy to realize energy and material efficient manufacturing, yet few studies have addressed energy and material flows using a common perspective. In principle, to realize the minimum environmental impact, energy and material flows must be viewed using the same metric; this is required to avoid impact shifting from energy to material or vice versa. In this paper, an environmental evaluation method for milling tool path strategies is proposed to support intelligent manufacturing, which considers energy flow (electricity provided to the machine tool and air compressor) and material flows (associated with the cutting tool, workpiece, and cutting fluid) for a milling process. The proposed method provides a quantitative calculation to characterize the total exergy loss in terms of energy and material flows. It is envisioned that total exergy loss can support quantitative decisions related to electricity consumption, tool wear, metal chips recycling, and cutting fluid loss. To demonstrate the applicability of the method, a case study is considered in which a milling tool path is selected to minimize exergy loss. The proposed method will be integrated into an intelligent control system for evaluating the total exergy loss of a milling process, which can assist manufacturers to make reliable decisions to reduce the environmental impact during machining stage in the industry 4.0 era. [ABSTRACT FROM AUTHOR]

Demanufacturing aims to recover value and conserve energy from end-of-life (EOL) products, contributing to sustainable manufacturing. To make the full use of EOL products, they are usually disassembled into components that have different values and embodied energy at different EOL options. This article studies a disassembly planning (DP) that integrates the decisions on disassembly sequence and EOL strategy to maximize the recovered value and energy conservation from EOL products. We propose a multiobjective DP based on the value recovery and energy conservation (MDPVE) model, which is different from the existing DP models by focusing on the embodied energy rather than the energy consumption during disassembly. An adapted multiobjective artificial bee colony (ABC) algorithm [multiobjective ABC (MOABC)] is developed to identify the Pareto solutions for the MDPVE and is compared with a well-known metaheuristic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II). A real-world case study demonstrated the superior solution quality and computational efficiency of MOABC. Note to Practitioners—There is often more than one treatment option for EOL products or components, including reuse, remanufacturing, and recycling. However, the decision on which EOL option to select is not considered in most of the DP studies by assuming an EOL option given for each component. Hence, the disassembly plan with the EOL decision is focused in this article. As energy sustainability gains an increasing attention, it is essential to assess the profitability and energy conservation simultaneously for EOL products. Since there could be a tradeoff between recovered profit and conserved energy, a multiobjective evolutionary algorithm is developed for generating Pareto solutions which help decision-makers to find good solutions for both evaluation indicators. [ABSTRACT FROM AUTHOR]

• A life cycle-based environmental performance indicator is proposed. • An integrated life cycle analysis & fuzzy inference system model is proposed. • The framework is applied on a Chinese coal-to-energy supply chain system. • A scenario-based analysis is conducted to mitigate the environmental impacts. Coal consumption and energy production (CCEP) has received increasing attention since coal-fired power plants play a dominant role in the power sector worldwide. In China, coal is expected to retain its primary energy position over the next few decades. However, a large share of CO 2 emissions and other environmental hazards, such as SO 2 and NO x , are attributed to coal consumption. Therefore, understanding the environmental implications of the life cycle of coal from its production in coal mines to its consumption at coal-fired power plants is an essential task. Evaluation of such environmental burdens can be conducted using the life cycle assessment (LCA) tool. The main issues with the traditional LCA results are the lack of a numerical magnitude associated with the performance level of the obtained environmental burden values and the inherent uncertainty associated with the output results. This issue was addressed in this research by integrating the traditional LCA methodology with a weighted fuzzy inference system model, which is applied to a Chinese coal-to-energy supply chain system to demonstrate its applicability and effectiveness. Regarding the coal-to-energy supply chain under investigation, the CCEP environmental performance has been determined as "medium performance", with an indicator score of 39.15%. Accordingly, the decision makers suggested additional scenarios (redesign, equipment replacement, etc.) to improve the performance. A scenario-based analysis was designed to identify alternative paths to mitigate the environmental impact of the coal-to-energy supply chain. Finally, limitations and possible future work are discussed, and the conclusions are presented. [ABSTRACT FROM AUTHOR]

Manufacturing processes, such as machining, transform raw materials into finished goods, and these processes consume significant energy. There is an increasing concern about the energy required for such processes and the environmental consequences attributable to the generation of the energy. Reducing the energy required to perform machining operations will not only reduce the environmental footprint, but also provide economic benefits. To that end, the effects of cutting conditions (e.g., feed and speed) and tool geometry (diameter and number of teeth) on the power required for an end milling operation are investigated experimentally. Experimental results are presented from a cutting mechanism perspective with the goal of understanding the role of the process variables. The specific cutting energy (SCE) is found decreasing when material removal rate increases, but there is substantial variation about the general trend. In essence, the cutting parameters and the tool geometry influenced the changes of average chip thickness and cutting speed, which cause the shear deformation energy changes and eventually collectively influence the SCE's change. Based on the experiments, suggestions on selecting process parameters are provided to improve milling energy efficiency. [ABSTRACT FROM AUTHOR]

Growing demand and aging infrastructure has put the current electricity grid under increased pressure. Microgrids (μGs) equipped with storage are believed to be the future of electricity grids that will be able to achieve energy efficiency by integrating renewable energy sources. Storage can be used to mitigate the time-varying and intermittent nature of renewable energy sources. In this article, we consider optimal load scheduling in a μG for four different load types: production line loads, non-moveable loads, time moveable loads, and modifiable power loads for different types of consumers. Consumers cooperate with the System Operator to schedule their loads to achieve overall energy efficiency in the μG. Two different options for charging the storage are considered: (i) charging from excess harvest in μG and (ii) charging from the Macrogrid. We perform sensitivity analysis on the storage capacity for two pricing policies to understand its trade-offs with the total electricity cost and Peak to Average Ratio. Computational experiments with different problem instances demonstrate that: (i) charging storage from the Macrogrid allows higher flexibility in load scheduling; and (ii) load scheduling with cooperative consumers outperforms the individualistic and random scheduling in terms of total electricity cost. [ABSTRACT FROM AUTHOR]

Neodymium-iron-boron (NdFeB) magnets play a critical role in clean power products, e.g., electric vehicles and wind turbines. Since China has near monopolistic control of the supply of these magnets, many parties are interested in recovering end-of-life magnets for additional use cycles. Such a strategy requires a cost-effective approach to collect and process used magnets while maximizing the economic and environmental benefits. This paper employs fuzzy logic and non-dominated sorting genetic algorithm (NSGA-II) to solve a location-allocation problem for NdFeB magnet recovery under supply and demand uncertainties. A Pareto front is constructed to evaluate the performance of the proposed design. [ABSTRACT FROM AUTHOR]

Large energy loss caused by mismatching between the installed power and demanded power, as well as the wasted potential energy, is a serious problem for a hydraulic press. In order to reduce the energy loss, an energy-saving method by balancing the load of operations of presses was proposed based on the analysis of energy flow characteristics of the hydraulic system. In the method, the motor pumps in the drive system are shared in different time by a unit composed of two hydraulic presses so that the energy loss caused by unloading operations can be reduced. Furthermore, these two presses are combined, and the excessive energy from one press can serve as the input energy to the other one during some operations to improve the energy efficiency of the drive system, and the potential energy can be utilized directly. Meanwhile, operation durations of these combined presses are optimized for coordination of working processes. The method was applied to two hydraulic presses in a tandem line, and the energy consumption was obtained by quantifying the characteristics of conversion components. Results indicate that for a single press, 36% of electrical energy can be saved in the investigated forming processes. [ABSTRACT FROM PUBLISHER]

A novel multiple-objective model for batch scheduling of an energy-intensive manufacturing process, e.g., heat treatment, is proposed. The model minimizes energy consumption and total weighted tardiness while considering the arrival times of each workpiece and the inherent uncertainties in gas heating values, processing times, and due dates. Fuzzy logic is adopted to characterize these uncertainties and to interpret objective dominance when finding a Pareto frontier. A non-dominated sorting genetic algorithm is employed. The approach is demonstrated using a pre-treatment (soaking) process prior to a hot rolling operation. Pareto optimal performance of the model under different parameter settings is discussed. [ABSTRACT FROM AUTHOR]

Consumers, industry, and government entities are becoming increasingly concerned about the issue of environmental sustainability. With this in mind, manufacturers have begun to explore proactive means for reducing their level of resource consumption, and the amount and impact of their generated waste streams. Little research has been conducted on the development of process planning methods that consider environmental factors. In this paper, a new process planning method based on a carbon emission function model is presented that integrates both economic and environmental considerations. The proposed method consists of four steps: (1) component feature identification, (2) generation of alternative operations, (3) selection of operations with lower carbon emissions, and (4) generation of process plan based on a genetic algorithm. This method produces a comparatively ‘green’ and economical process plan. The method is demonstrated using an example part and the benefits of the method in terms of energy consumption and carbon emissions are evaluated. This paper concludes with a discussion of potential approaches that can facilitate seamless integration of environmental considerations into process planning. [ABSTRACT FROM AUTHOR]

Lightweighting is a design strategy to reduce energy consumption through the reduction of mass of a product. Lightweighting can be applied to machine tools to reduce the amount of energy consumed during the use phase. Thus, the energy cost of machine operation will be reduced. One might also hypothesize that since a lighter-weight machine tool requires less material to build, the cost to produce such a machine will be less. However, it may also be the case that lightweighting a machine tool increases its complexity, which will likely drive up the cost to manufacture the machine. To explore the cost drivers associated with building a machine tool, data on the features associated with a wide variety of vertical milling machine tools are collected. Then, empirical cost models are fit to this data. The results from the cost models show that the machine tool mass is a significant cost driver; other key drivers are the number of axes and spindle power. The models are used to predict the cost benefits of lightweighting in terms of mass, which are compared to potential increased manufacturing costs associated with complexities introduced due to lightweighting. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to provide a systematic overview of the state of the art in energy and resource efficiency increasing methods and techniques in the domain of discrete part manufacturing, with attention for the effectiveness of the available options. For this purpose a structured approach, distinguishing different system scale levels, is applied: starting from a unit process focus, respectively the multi-machine, factory, multi-facility and supply chain levels are covered. Determined by the research contributions reported in literature, the de facto focus of the paper is mainly on energy related aspects of manufacturing. Significant opportunities for systematic efficiency improving measures are identified and summarized in this area. [Copyright &y& Elsevier]

Abstract: Owing to the limited availability of freshwater, manufacturing water usage will attract more and more attention. Characterizing water usage represents a key first step to reduce manufacturing water consumption. In this paper a general approach for developing life cycle water inventory of machining processes is presented with focus on direct water usage and indirect water usage due to energy consumption. The approach is demonstrated using three representative processes i.e. turning, milling, and drilling. It is found that direct water usage (due to flood application of metalworking fluids) is comparable in size with indirect water usage (due to electricity consumption). [Copyright &y& Elsevier]

Reducing the energy consumption of machine tools is important from a sustainable manufacturing perspective. Much of a machine tool's environmental impact comes from the energy it consumes during its use phase. To move elements of a machine tool requires energy, and if the mass of those elements can be reduced, then the required energy would be reduced. Therefore, this paper proposes a genetic algorithm to design lightweight machine tools to reduce their energy consumption. This is specifically applied to optimize the structure of a machine tool slide table, which moves throughout the use of the machine tool, with the goal of reducing its mass without sacrificing its stiffness. The table is envisioned as a sandwich panel, and the proposed genetic algorithm optimizes the core of the sandwich structure while considering both mass and stiffness. A finite element model is used to assess the strength of the proposed designs. Finite element results indicate that the strength of the lightweight tables is comparable with a traditional table design. [ABSTRACT FROM AUTHOR]

A new ‘econological scheduling’ model combining the economic and ecological aspects of a multi-part multi-machine setup operating under a time-of-use tariff is presented. The operating speed of the machines and the frequency of operating speed change are allowed to vary, and the peak load and energy consumption during a shift is estimated using discrete event simulation. The electricity cost and environmental impact for a target production quota are simultaneously minimized using a multi-criterion meta-heuristic optimization. The proposed model is demonstrated via a case study on a manufacturing unit producing parts using machining and welding operations. A comparison among econological, economic, and ecological approaches and the underlying dynamics of scheduling under a time-varying electricity tariff are presented as one of several strategies for enabling a manufacturing system to be more eco-friendly without substantially increasing the electricity cost. [ABSTRACT FROM AUTHOR]

Abstract: There is growing interest in the production of biofuels from woody biomass. Critical to the financial success of producing biofuel is identifying the optimal location for the facility. The location decision is especially important for woody biomass feedstock owing to the distributed nature of biomass and the significant costs associated with transportation. This study introduces a two-stage methodology to identify the best location for biofuel production based on multiple attributes. Stage I uses a Geographic Information System approach to identify feasible biofuel facility locations. The approach employs county boundaries, a county-based pulpwood distribution, a population census, city and village distributions, and railroad and state/federal road transportation networks. In Stage II, the preferred location is selected using a total transportation cost model. The methodology is applied to the Upper Peninsula of Michigan to locate a biofuel production facility. Through the application of the two-stage methodology, the best possible location for biofuel production was identified as the Village of L’anse in Baraga County. Also investigated are the sensitivity of transportation cost and the optimal site for biofuel production to changes in several key variables. These additional variables included fuel price, transportation distance, and pulpwood availability. By applying sensitivity analysis based on limited availability of feedstock, the City of Ishpeming emerged as another viable location for the production facility. [Copyright &y& Elsevier]

Studies have indicated that reducing the process energy demand is as important as improving the energy conversion efficiency to make manufacturing equipment more energy efficient. However, little work has been done to understand the energy demand characteristics of the widely employed drawing process. In this paper, the energy demand of the cylindrical drawing process under a range of operating parameters was measured and analyzed. Since any energy saving efforts should not have negative effects on the product quality, the forming quality of the drawn part indicated by the maximum thinning and thickening ratios and variation of thickness was also considered. To identify the main contributors to energy demand and forming quality, two sets of experiments were designed based on the Taguchi method. The first set of experiments include three parameters (i.e., punch velocity, blank holder force, and drawn depth) at three levels, while the second set of experiments only include two factors (i.e., punch velocity and blank holder force) at three levels due to their impacts on the forming quality. Analysis of variance (ANOVA) and analysis of means (ANOM) were then used to analyze the experimental results. Finally, grey relational analysis (GRA) was used to reveal the correlation between the forming quality and the process energy. Results show that the mean thickness variation has the strongest relational grading with the process energy, which suggests that the process energy can be used as an effective indicator to predict mean thickness variation of the drawn part. The identified characteristics of the process energy and the forming quality can be used to select process parameters for reduced energy demands of drawing processes. [ABSTRACT FROM AUTHOR]

As energy efficiency increases in importance, researchers have identified manufacturing processes as opportunities where energy consumption can be reduced. Drawing is one widely employed, energy intensive manufacturing process, which could benefit by analysis of energy consumption during operation. To optimize the energy consumption of the drawing process, this paper developed an explicit model to quantify the process energy for the cylindrical drawing process by analyzing the dynamic punch force during the process. In this analysis, the evolution of the stress and strain was analyzed in the drawn part by considering all the structure parameters of the drawn part. The stress and strain analyses were integrated into an overall process energy model, and the behavior of the model was classified into three categories, based on their physical mechanisms, i.e., deformation energy, bending energy, and friction energy. The model was validated using numerical experiments designed by the Taguchi method where two different kinds of materials were tested over 18 runs. The results from the numerical experiments were compared with those from the model, and show that the maximum variation of the process energy predicted by this model is less than 10% for a given part. Sensitivity analysis was performed on the model to understand the contributions of the process parameters on the process energy to guide process optimization for lower energy consumption. The established model can assist in the rapid design of drawn parts with lower embodied energy. [ABSTRACT FROM AUTHOR]

Energy-efficient machining has become imperative for energy conservation of manufacturing sectors. The energy characteristics of machining process tend to be very complex, varying substantially with respect to different configurations of machine tool, workpiece and process parameters. This paper undertakes this challenge and explores the energy consumption characteristics of machining process adaptive to different machine tools, workpieces and process parameters. A multi-dimension coupling model of energy consumption for machining process is first established by considering specifications of machine tools, workpieces and processes. Then the influence factors of energy consumption are systematically analyzed from a multi-dimensional perspective. The internal interact relationship among each dimensional parameter is illustrated. To validate the effectiveness of the proposed energy model and determine the energy-efficient machining configurations with related to machine tools, workpieces and process parameters, a series of experiments are carried out on a CNC vertical machining center. Experimental results show that the optimal machining configurations can effectively reduce energy consumption and simultaneously improve energy-efficiency of CNC machining. • A multi-dimension energy-efficiency model for machining is developed. • Influence factors of energy consumption are analyzed in a multi-dimension manner. • Energy-efficient machining configurations are determined by optimal experiments. • The significance of the proposed approach is exemplified by machining experiments. [ABSTRACT FROM AUTHOR]

An urgent challenge in the manufacturing industry is increasing efficiency while decreasing energy consumption and environmental impact. Past studies addressing these issues have mainly focused on tool path optimization only considering machining efficiency. In this paper, we present a methodology to optimize the tool path for high efficiency, low energy consumption and carbon footprint in milling process. Firstly, the description and influencing factors of tool path are introduced. Then, a multi objective tool path optimization model with maximum machining efficiency, minimum energy consumption and carbon emission is proposed. Furthermore, the solution of the proposed model is introduced, which including two steps, one is the calculation of the number of cutter contact points (CCP), the other is using adaptive dynamic GA to optimize the connection sequence and ways of each CCP. Finally, the effectiveness and practicability of the method are verified by the machining experiments. [ABSTRACT FROM AUTHOR]

As environmental performance becomes increasingly important, the sintering process is receiving more attention since it consumes large amounts of energy. This paper proposes a data-driven model for sintering energy consumption, which considers both model accuracy and time efficiency. The proposed model begins with removing data anomalies using a local outlier factor (LOF) algorithm and an attribute selection module using the RReliefF method. Then, to accurately predict sintering energy consumption, an integrated predictive model is employed that uses bagging-enhanced extreme learning machine (ELM) and support vector regression (SVR) machine, combined with an entropy weight method. A case study is used to demonstrate the effectiveness of the proposed model using actual production data for a year. Results show that the proposed model outperforms other models and is computationally efficient. Optimal parameters of the LOF (1.3) and number of attributes (30) were identified. It was found that coke powder has the most significant impact on the solid energy consumption (SEC), while cooling water flow rate provides the most significant impact on the gas energy consumption (GEC) within each recorded attribute variation. Parametric analysis further revealed the relationships between energy consumption and the significant attributes mentioned above. It is suggested that the proposed model could effectively reduce the energy consumption by attaining more efficient attribute settings. [ABSTRACT FROM AUTHOR]

Dry or near dry machining is often regarded as an effective strategy for reducing ecological impacts of the cutting processes. However, due to the application limitations of dry or near dry machining, reduction of cutting fluid supply through machining parameter optimization offers a cost effective alternative. To this end, an optimization model of machining parameters considering minimum cutting fluid consumption and cost is proposed. Process cost and cutting fluid consumption are treated as the two objectives in the optimization model, which are affected by four variables, namely cutting depth, feed rate, cutting speed, and cutting fluid flow. In the model, process cost includes production operation cost and cutting tool cost, whilst cutting fluid consumption by a machining process, which consists of reusable cutting fluid and non-reusable cutting fluid, ie., the remaining cutting fluid deposited on the workpiece and chips as well as that diffused into the environment. The multi-objective optimization problem is solved by a hybrid genetic algorithm programmed in Matlab 7. An illustrative case study was implemented to verify the effectiveness of the multi-objective optimization model, and the simulation results showed 17% reduction of fluid consumption compared to that without optimization. This indicates that the proposed optimization is effective and has great potential to be adopted by industry. [ABSTRACT FROM AUTHOR]

A recent international panel study (Gutowski T, Murphy C, Allen D, Bauer D, Bras B, Piwonka T, Sheng P, Sutherland J, Thurston D, Wolff E. WTEC Panel Report on: Environmentally Benign Manufacturing (EBM), 2000 on the web at; and ) finds Environmentally Benign Manufacturing (EBM) emerging as a significant competitive dimension between companies. With differing views on future developments, companies, especially large international companies, are positioning themselves to take advantage of emerging environmental trends. Among Japanese companies visited, the panel observed an acute interest in using the environmental advantages of their products and processes to enhance their competitive position in the market. In the northern European countries visited, the panel saw what could be interpreted as primarily a protectionist posture; that is, the development of practices and policies to enhance the well-being of EU countries, that could act as barriers to outsiders. In the U.S., the panel found a high degree of environmental awareness among the large international companies, most recently in response to offshore initiatives, mixed with skepticism. In this article, we survey EBM practices at leading firms, rate the competitiveness of the three regions visited, and close with observations of change since the study. Based upon these results, major research questions are then posed. In sum, the study found evidence that U.S. firms may be at a disadvantage due in part to a lack of coherent national goals in such areas as waste management, global warming, energy efficiency and product take back. [Copyright &y& Elsevier]

Fossil fuel depletion, air pollution, and climate change are imposing great pressure on industrial sectors, especially for manufacturing sectors. Energy consumption modelling is an important measure to promote the energy efficiency in manufacturing, which offers the fundamental basis for energy efficiency-related optimization. Although dynamic characteristics have a significant effect on operation of machine tools, traditional energy consumption models hardly take dynamic characteristics into consideration. This paper takes the feed system as an example and proposes a dynamic energy consumption model of machine tools with bond graph theory. Based on the structure of feed system, the proposed model is firstly expressed to a physical model and the bond graph model are established according to the law of energy conservation. Subsequently, with the augmented bond graph model, mathematical models of dynamic characteristics and energy consumption are proposed with state variables. Finally, the simulation and analysis of the proposed model are given. Results show that the proposed dynamic characteristics model and energy consumption model based on bond graph theory are reasonable and effective. Additionally, the proposed model can be used to explore the correlation between energy-consuming components and energy consumption of machine tools for realizing the high energy efficiency design of machine tools. • Energy consumption in machine tools based on bond graph theory is modeled. • Energy consumption characteristics of machine tools component layer are examined. • Multi-motion states are used to verify energy consumption modelling method. • This work offers a novel perspective on energy consumption modelling. [ABSTRACT FROM AUTHOR]

As mechanical engineering is growing a current and urgent issue is rising in the manufacturing process, which is the ability to improve efficiency while reducing energy consumption during the processes. Cutting parameters are an important part of the computer numerical control (CNC) machining process, so a reasonable selection of cutting parameters can significantly enhance the machine's energy efficiency. Previous studies mainly focused on cutting power (P) and material removal rate (MRR), respectively, without considering the trade-off relationship between them; In this paper, an optimization model of cutting parameters is developed by establishing a multi-objective model where P and MRR are identified. The proposed model utilizes the Grey Correlation Analysis (GRA) and experimental to determine the weight of the objective. After the Linear Decreasing Particle Swarm (LDPS) optimization algorithm was utilized to solve the model, several application cases are given and their results demonstrate the ability of our method through comparing with the traditional approach. [ABSTRACT FROM AUTHOR]

With rising energy prices and environmental concerns, reduction of energy consumption has become a critical manufacturing focus. One appropriate way to reduce energy consumption in manufacturing systems is to develop energy-conscious optimization strategies for production planning. In a flexible machining job shop, this planning must accommodate common dynamic events, such as new job arrivals and machine breakdowns. Dynamic events could change production energy consumption, thus require plan changes in pursuit of energy consumption reduction. To this end, this paper proposes an energy-conscious optimization method in flexible machining job shops considering dynamic events. In this paper, a optimization method which updates the jobs and machine plan status when dynamic events occur is proposed. The method considers two states for machine tool energy consumption: actual machining and machine idling/stand-by. The optimization model considers the total energy consumption and makespan, and employs Non-dominated Sorting Gene Algorithm II (NSGA-II) approach to obtain a solution. The proposed method is evaluated with a test case in which a flexible machining job shop experiences new dynamic job arrivals and machine breakdowns. The results show that the proposed method is effective at adjusting the schedule in response to dynamic events. • Energy-conscious optimization method with dynamic events is proposed. • Actual machining energy and machine idling/stand-by energy are considered. • NSGA-II is used to solve the model with the updated jobs and machine tools status. • This method is effective at adjusting the schedule in response to dynamic events. • The results show that more energy-saving potential can be achieved with the method. [ABSTRACT FROM AUTHOR]