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Abstract Electrical discharge machining is a significant non-traditional technique, widely used in the precise manufacturing field. However, the electrical discharge machining process possesses plenty of energy consumption and hazardous exhaust emissions. Since a sustainable machining technique, external magnetic field assisted, could dramatically develop sustainable machining performance of electrical discharge machining process, this paper presents a comparative study energy efficiency and environmental impact in magnetic field assisted electrical discharge machining and conventional electrical discharge machining. A novel thermo-physical model was proposed to calculate energy distribution under the single/continuous discharge, which was verified by a series of corresponding experiments. Then, based on simulation and experimental methods, a comparative study on the magnetic field assisted electrical discharge machining and conventional electrical discharge machining was conducted to analyze the discharge energy flowing and temperature distribution of workpiece, electrode tool and debris. Moreover, the effects of external magnetic field on the machining performance, environmental impacts and energy efficiency were presented. Eventually, it can be found out that the effect of magnetic field contributes to increasing energy utilization efficiency by 15.2%, material erosion efficiency by 22.6%, and material removal rate by 21.9%. To balance energy efficiency and surface roughness (Ra), the optimal parameter combination is obtained as below: magnetic field intensity: 0.2T, discharge current: 7–9A, and pulse duration: 300s. Highlights • Magnetic field assisted EDM improve energy efficiency and environmental impact. • A thermo-physical model is proposed to calculate energy distribution of MF-EDM process. • Sustainable machining performance of EDM and MF-EDM are comparatively investigated. • The optimal parameter is: magnetic field intensity: 0.2T, current: 7∼9A, and pulse duration: 300μs. [ABSTRACT FROM AUTHOR]

Machining workshop is a widely distributed manufacturing system that consumes massive energy in a low efficiency. In recent years, the increasing pressure from energy price and environmental directive have forced machinery manufacturers to address energy efficiency monitoring and management to improve economic benefits and environmental performance. However, due to the complicated energy flow and dynamic energy changes of the machining workshop, machinery manufacturers still lack an effective method to monitor and manage the energy efficiency in a practical manner. To this end, this paper proposes an energy efficiency monitoring and management system with the support of the newly emerging Internet of Things (IoT) technology. Firstly, the energy characteristics and energy efficiency indicators of the machining workshop are analyzed and defined. Then the framework of the IoT based energy efficiency monitoring and management system is proposed. The key approaches for energy efficiency monitoring and management are then illustrated. Finally, an industrial application is demonstrated to validate the effectiveness and benefits of the proposed system. With the application of the proposed system, potential opportunities for energy consumption decrement and energy efficiency improvement can be identified. Machinery manufacturers can easily reduce energy consumption and energy cost by managing the machining process. [ABSTRACT FROM AUTHOR]

CNC machining is one of the major activities in the manufacturing industry which is responsible for a significant portion of the total consumed energy. However, the energy efficiency of CNC machining is surprisingly low. It is important to provide valuable insights for practitioners to enhance energy efficiency of CNC machining. This paper analyzes the energy consumption characteristics of CNC machining process and exploits the influence factors for energy efficiency improvement through a theoretical analysis. A series of operational strategies for energy efficiency improvement of CNC machining are presented through a review and research framework. The future research directions are identified and summarized for more energy savings. [ABSTRACT FROM AUTHOR]

Soaring energy price, increasing environmental concerns, and stringent legislations have forced enterprises to reduce energy consumption in discrete parts manufacturing. In order to achieve this reduction, accurate estimation of energy consumption at the manufacturing planning stage is needed. In this paper, a new model for energy consumption prediction in machining processes is proposed and validated in experiments. Using the established cutting force model, cutting power at the tool tip is obtained analytically. Further, the relationship between total power consumed by the machine tool and cutting power at the tool tip is empirically characterized. To demonstrate the new model, this hybrid approach has been applied for machine power prediction in a slot milling process. A comparison study has also been conducted between the proposed model and existing models under various cutting conditions. The results show that the proposed model could achieve better prediction accuracy than other models. Moreover, a major limitation of the existing models is successfully addressed. By considering cutting power at the tool tip, the proposed hybrid model could reflect the nature of material removal and is able to provide valuable information regarding the impact of specific cutting parameters on power consumption. This knowledge is important in evaluating operation parameters, process plans, and even production schedule in terms of energy efficiency. [ABSTRACT FROM AUTHOR]

Abstract: Increasing pressures from energy price and environmental directive force the manufacturing enterprises to consider and initiate the implementation of energy assessment and energy quota practices to improve both their economic benefit and environmental performance. Against this background this paper presents an integrated method to evaluate energy efficiency in machining workshop. The integrated features of the assessing method are reflected in three aspects: (1) In this evaluation system, the energy profile of machining workshop is viewed from the machine tool layer, manufacturing unit layer, task layer and workshop layer to reveal the energy performance inside the workshop completely. (2) The assessing indexes for each layer include both effective energy indicator and Specific Energy Consumption (SEC) indicator. (3) An integrated calculation method of energy indexes is introduced, which combines off-line experiments with theoretical formulas. To verify its feasibility and validity, the assessment method is applied to a small machining workshop. The assessing results showed that the assessment method is helpful in providing support for machining scheme selection, energy saving discovery and energy quota allocation in plant, which has significance in practice. [Copyright &y& Elsevier]

As the most important part of machining system, machine tools are widely used in industry, and the resource consumption and environmental problems caused by their use are becoming more and more serious. For promoting energy efficiency of machining system, various methods have been developed based on statistical approaches with design of experiments. However, the methods cannot be easily utilized when the optimization target or machine tool design is modified because the optimal solution is determined based on the experimentally measured data. An effective and practical monitoring method is not only the precondition of energy efficiency promoting, but also the basis of the integration with industry 4.0 applications. This paper presents a modelling approach for energy efficiency of machining system which can be used to monitor energy efficiency on-line and without any extra cutting force dynamometer. The approach is proposed based on the energy consumption mechanism of machining system and the dynamic factors (temperature, lubrication, etc.) of machining processes are considered, so that it has a quite high accuracy under various machining conditions. The case study and validation have been carried out and the experimental results indicate that the accuracy of the approach can reach over 90% under various processing conditions. Finally, the static errors and dynamic errors which may affect monitoring accuracy are discussed. [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]

The manufacturing sector consumes a significant amount of energy and their outputs, like solid and gaseous waste streams, can result in substantial stress on the environment. This paper aims to analyze and compare the electrical energy and material efficiency of machining, additive and hybrid manufacturing. The analysis of the manufacturing processes is based on machine tool data from a sample process. To get a generalized statement about the energy consumption of the investigated processes the electrical energy demand was extrapolated as a function of the material removal ratio. The results indicate that hybrid manufacturing becomes beneficial from an environmental point of view compared to milling, when the material removal ratio exceeds 55%. The electrical break-even point for selective laser melting is approximated to 82% material removal ratio from data extrapolation. Subsequently, opportunities for electrical energy and material efficiency improvements are presented for these technologies to gain an understanding of how each can contribute to a more sustainable manufacturing landscape. • Additive processes show a smaller amount of wasted material. • For material removal ratios over 55% additive processes show less demand of energy. • For material removal ratios over 75% additive processes show less processing time. [ABSTRACT FROM AUTHOR]

Spindle system is the major mechanical component in machine tools, and its performance is responsible for a significant portion of the total consumed energy of machine tools. Conventional design optimization of spindle system is partially focused on parameter optimization of spindle motor or transmission system, contributing to an increase of the motor efficiency. Given that concurrent interactions among them is complex, very little efforts has been done to conduct integration optimization for optimum energy efficiency. To this end, a new approach of spindle system design is presented with consideration of the above two aspects adequately, to achieve the maximum energy and material efficiency. Firstly, the energy characteristic of spindle system is explicitly modelled on the basis of energy flow analysis. Then, a multi-objective optimization model for parameter optimization of spindle motor and transmission system is developed to take the both maximum energy efficiency and minimum volume as objectives, which is subjecting to a set of constraints with related to the cutting parameters boundary, processing requirements and shifting power losses. Finally, a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is presented to solve the developed optimization model. The performance of the proposed design approach of lathe spindle system is demonstrated through different working conditions. The experimental results indicate that the design of energy and material efficient machine tools can be achieved. [ABSTRACT FROM AUTHOR]