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In machining processes, there are contradictions between high efficiency and environmentally-friendly machining. This indicated that there is a major potential for sustainable machining to both increase efficiency and protect ecological balance. For this, the employment of effective methods has become imperative to reduce resource use. In this way, the use of analysis and optimization tools to select the tool material, cutting parameters, cooling/lubrication conditions, etc., will be a benefit for minimizing waste without sacrificing efficiency. In the literature, researchers have used various methods to reveal the requirements of sustainability. However, reaching the concept of sustainable machining by using one more than technique confuses the readers. Therefore, it was deemed necessary to present and discuss the current methods, models and analyzes in a comprehensive review paper. In this context, this paper reviewed the previously published works, especially focusing on sustainability and energy consumption modeling methods in machining operations. At the end of the study, it was seen that LCA provides a systematic and quantitative view of the system and thus can act as a decision support tool. Moreover, it can give an idea to the manufacturers about the improvement of the process and the areas of innovation. However, the method has several disadvantages such as relatively expensive and specific software, obtaining inventory data, impractical to use, time-consuming and requires large amounts of data. Various mathematical models and algorithms have been developed to deal with the complex situations and offer a more practical use compared to LCA. However, it is difficult to state that clear results have been achieved thanks to these models. It was seen that in complex machining processes, only approximate results can be found with models, which leads to questioning the reliability of the models. [ABSTRACT FROM AUTHOR]

Reducing energy demand in manufacture is an urgent challenge. This challenge is driven by higher consumer demand for manufactured products, increasing electricity and energy prices, volatility and uncertainty in energy supply and national policy. These factors, together with a need to reduce energy consumption derived carbon dioxide emissions, strategically call for energy efficient manufacturing. In manufacturing processes, especially mechanical machining, more than 90% of environmental impact arises from direct electrical energy demand in machine tools. At the machine tool level, the biggest share of the electrical energy associated with mechanical machining is required to bring the machine to a ready state and support non-cutting operations such as spindle torque requirements, auxiliary units and movements. These activities are controlled and related to machine commands such as NC codes. In this paper comprehensive information on energy intensity in machining process, including the influence of tool wear, was studied. Key energy states were identified to build up an energy demand for machining components. The paper defines the essential power constants for a database that can assist energy prediction for any available machines and workpiece materials. The assessment of alternative toolpaths identified major opportunities for energy demand reduction. [ABSTRACT FROM AUTHOR]

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]

The reduction of CO 2 by moving from fossil to renewable energy sources is currently high on the agenda of many governments. Simultaneously these governments are also forcing the reduction of energy consumption. The primary focus of these agendas is on mobility, building, and industrial sectors. For the latter, energy-efficient shop floors and machining processes assist the reduction of energy consumption. Previous research has focused on energy-efficient machining strategies during machining processes. However, an energy-efficient start-up of these machines or their spindle axis start-up has been neglected until now. This paper focuses on this neglected issue by comparing the energy-efficiency, production time, and cost-efficiency of the CNC (computer numeric control) machine by varying the power input at the spindle axis. This is done by analysing the high-frequency data (500 H z) of the machine from machining operations that is retrieved via the edge device. Concepts of data analytics and especially EDA (exploratory data analytics) were used to interactively visualize the inter-dependencies and develop results. It is shown that optimized reduction of spindle power input value leads to both: peak power smoothing from 20kW to 10kW and lowering of overall energy consumption by approximately 1.4%. Moreover, the costs and production time are marginally affected (0.518% and 0.523% respectively) by this optimized reduction of spindle power input value. Thus, this paper highlights a novel method from data acquisition to process improvement towards energy-efficient and sustainable machining. [Display omitted] • Energy-efficient machining via innovative technique of data acquisition and analysis. • Sustainable peak smoothing for spindle operations. • Utilization of high-frequency machine data for enhancement of process. [ABSTRACT FROM AUTHOR]

The material processing industry has to deal with material and energy reduction targets, and such a need becomes more and more urgent when future energy and resource consumption predictions are considered. The optimization of machining operations has conventionally been performed by identifying the range of process parameters that lead to the maximum efficiency, which are generally defined on the basis of maximum productivity and minimum cost criteria. Cost-based models are part of the industrial cultural background, while the emerging environmental impact analysis has not yet become fully established. The paper describes an approach that is aimed at the integration of system-level models for the environmental and economic assessment of machining. Process time, cost, energy demand and carbon dioxide emissions are assumed as optimization targets. The dry and wet turning of Ti-48Al-2Cr-2Nb and Ti-6Al-4V alloys, while varying the process parameters, is considered as a case study. The results highlight that the range of process parameters that allow the maximum process efficiency to be achieved is influenced by material machinability, and that a holistic approach to process optimization appears to be a key task which is worth further research efforts. [ABSTRACT FROM AUTHOR]

This paper contributes to green product development by identifying the green products with the highest potential for growth in a country. To address our aim, we use the concept of product proximity and product space and, borrowing from the results of recent studies on complexity economics, we advance that the green products with the highest potential for growth among all green products in a given country are those being in close proximity to the products a country produces with high Relative Comparative Advantage (RCA). We test this hypothesis performing a regression analysis. We build the product space for 141 different countries for the years between 2005 and 2013 and for each country we compute the maximum proximity of each green product to the products with high RCA (i.e., the proximity of the product source of competitive advantage closest to the green product considered). Results confirm that green products with high maximum proximity to the products with high RCA had the highest growth. So doing, we contribute to the literature by providing a new application of the product space as a policy making tool for green development. We also provide several applications of the proposed method. [ABSTRACT FROM AUTHOR]

Among machining processes, grinding has been used to achieve high dimensional tolerances and surface quality on workpieces. Yet, high levels of energy expenditure per volume of removed material and the need for cutting fluids make grinding one of the most environmentally impactful machining processes. Furthermore, changes in parameters such as grain and bond specifications of the grinding wheel, cutting speed, and specific material removal rate can lead to different productive and environmental results. Thus, the analysis of grinding processes should not be aggregated and leveraged into a single and broad output parameter. Instead, comprehensive study should be performed, in which the most relevant process parameters, inputs and outputs are considered. This paper presents a detailed study of grinding process, including the characterization of machine subunits and production modes, along with the use of a combined life cycle assessment hybrid model and real-time monitoring system to evaluate the consumption of energy, tooling, cutting fluid and compressed air. A detailed cradle-to-gate life cycle assessment study using eleven different impact categories and a productive performance assessment were performed to evaluate the effects on varying specific material removal rate and wheel type. For equal values of specific material removal rate, the change from a conventional wheel to a cubic boron nitride represented a power requirement increase of 19–24%. Cubic boron nitride wheel achieved remarkably better results on the wheel wear and part roughness indicators for all tested conditions. The environmental performance assessment showed a strict relation between the process environmental impacts and the consumption of electric energy and cutting fluid. To conclude, despite the higher power requirements, the combination of cubic boron nitride wheel with high values of specific material removal rate optimizes both the productive and the environmental results. [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]

Reducing the cutting energy consumption in machining is important to improve the sustainability of cryogenic machining. Currently, the analysis of cutting energy for cryogenic machining mostly relies on equipment testing, while less research has been conducted on energy analysis based on material removal mechanisms. In this paper, the chip formation mechanism of cryogenic machining is studied on the basis of liquid nitrogen orthogonal milling experiments with Ti–6Al–4V alloy, and the corresponding specific cutting energy model is established. First, the effects of cooling conditions and process parameters on chip microscopic morphology were analyzed based on orthogonal milling experiments. Next, a specific cutting energy model was established according to the chip formation mechanism. Finally, the reliability of the model was verified by cutting forces. The results show that in cryogenic machining, when the cutting speed is less than 200 m/min, adiabatic shear dominates the formation of serrated chips, and the cutting energy consumption is higher than that of dry cutting. As the cutting speed exceeds 200 m/min, adiabatic shear and periodic fracture together dominate the formation of serrated chips, and the energy consumption of cutting is significantly reduced at this time. This research provides a reference and guidance for the sustainable development of cryogenic machining. • This research reveals the chip formation mechanism of cryogenic machining of TC4 alloy. • In this study, an energy consumption model for cryogenic machining was established. • Cryogenic machining is an energy saving and environmental protection method. [ABSTRACT FROM AUTHOR]

Energy consumption in five-axis machining can be extremely large, especially for processing large-sized parts such as blades and dies, which often take hundreds of hours to machine. Once the specific machine tool configuration is given, one important objective in planning a five-axis machining operation is to reduce the total amount of energy consumption, which is not only particularly pertinent for mass production type operations but also plausible in today's energy-conscientious environment. While past research in this subject has been mainly focusing on finding better machining or processing parameters such as the cutting depth, feed rate, spindle speed, etc., in this paper, we study the fundamental problem of how to plan a five-axis tool path for machining an arbitrary free-form part surface on a given type of machine tool so that the total amount of energy consumption can be reduced, as compared to a traditional tool path, with everything else – e.g., the cutting depth, feed rate, spindle speed, maximum scallop-height, etc. – being equal. Towards this goal, an algebraic model called machine dependent energy potential field is formulated on the part surface, which encapsulates the specific power demand at any cutter contact point and along any feed direction. With this potential field, a simple tool path generation method is proposed that strives to find a good balance between the best fitting to the potential field (to minimize the total energy consumption) and the desired streamline patterns of the tool path. Experiments of the proposed method are carried out in both computer simulation and physical cutting, and the results are compared with that of some popular tool path generation methods such as the iso-scallop height method. The remarkable more than 25% savings in total consumed energy by the proposed method confirm the original motivation – designing better tool paths adaptive to the given specific machine tool is a powerful approach to reducing the total consumed energy, perhaps in general more effective than the passive selection of machining parameters which often have little room to adjust due to many constraints (e.g., the feed rate is highly regulated by the machine's controller during the cutting). [ABSTRACT FROM AUTHOR]

Green Low-carbon development has been recognized as a key requirement for advanced manufacturing, and reducing the energy consumption of machining system is a critical aspect. Traditionally, process planning and scheduling were carried out separately and sequentially in manufacturing industry, which limits the work for energy-efficient machining system. Considering the fact that the functions of these two systems are usually complementary, the energy-saving of machining system can be further improved if they are tightly integrated. In this paper, an integration model based on nonlinear process planning (NLPP) is proposed to implement such energy-saving method, and the Therblig-based model is used to predict the energy consumption of machine tools in product manufacturing process. Then, a genetic algorithm-based approach is adopted to solve the established integration model. Given the alternative process schemes generated by NLPP, such integration model can help select the suitable process plan and machines for each job and generate the scheduling scheme simultaneously for saving energy. To verify the energy-saving effect and the performance of the algorithm, case studies have been conducted and the experimental results show that the energy-saving potential in a shop floor environment can be further enhanced through the integration of process planning and scheduling and the solution method is feasible. [ABSTRACT FROM AUTHOR]

Choosing the optimum cutting parameters is regarded as one of the important energy saving technologies. This paper presents a method for complex optimization of cutting parameters with the objectives of energy efficiency and processing time, which integrates Taguchi method, response surface method (RSM) and multi-objective particle swarm optimization algorithm (MOPSO). In this study, specific energy consumption (SEC) is selected to evaluate energy efficiency and the calculation model is introduced firstly. Then the method is described in detail. Taguchi is used to design the experiment, signal-to-noise (S/N) ratio is subsequently employed to analyze the performance of parameters on SEC and processing time, and the significant contributions of parameters can be determined by use of range analysis method. RSM is conducted to develop regression models for the responses based on the experimental data, and the optimal machining parameters for minimizing energy and time are determined through the modified MOPSO algorithm. Finally, four machining parameters schemes with different optimization objectives are compared, and the results show that a trade-off point can be drawn between the low processing time and high energy efficiency. [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]

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]

Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. However, the ever increasing demand of customers for quality has led to better surface finish and thus more energy consumption. The energy efficiency of machines tools is generally very low particularly during the discrete part manufacturing. This paper provide a multi-objective predictive model for the minimization of power consumption and surface roughness in machining, using grey relational analysis coupled with principal component analysis and response surface methodology, to obtain the optimum machining parameters. The statistical significance of the proposed predictive model has been tested by the analysis of variance (ANOVA) test. The obtained results indicate that feed is the most significant machining parameter followed by depth of cut and cutting speed to reduce power consumption and surface roughness. The constructed response surface contours can be used by the shop floor people to find and use the best combination of machining parameters for the given situation. The reduction of peak load through optimization will results in lowering the power consumption of the machine tools during non-cutting idling time. [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]

Abstract: The aim of this research was to contribute towards the development of a new mathematical model and logic for predicting direct electrical energy requirements in machining toolpaths. This model will track the visibility and process dependence of energy and hence carbon footprint in machining process. This study includes a critical review of similar existing models and their limitations. The effect that machine modules, auxiliary units and machine codes have on power and energy consumption during machining was studied and the electrical current consumption measured. A mathematical model for electrical energy use in machining was developed addressing the limitations of existing models and validated on a milling tool path. The paper provides valuable information on the impact of machine modules, spindles, auxiliary units and motion states on the electrical energy demand budget for a machine tool resource. This knowledge is fundamentally important in evaluating toolpaths and re-designing machine tools to make them more energy efficient, to reduce electricity costs and associated carbon footprints. [Copyright &y& Elsevier]

Abstract: Reducing the energy consumption during machining of metal matrix composites (MMC) can significantly improve the environmental performance of manufacturing systems. To achieve this, calculation of energy consumption in the computerized numerical controlled (CNC) turning machine is required. It is important to minimize the power consumption and maximize tool life during machining operations, being performed on the CNC turning machine. However, this is challenging due to complexity of manufacturing systems and the nature of material being machined. This paper presents the findings of experimental investigations into the effects of cutting speed, feed rate, depth of cut and nose radius in CNC turning of 7075 Al alloy 15 wt% SiC (particle size 20–40 μm) composite. Design of experiment techniques, i.e. response surface methodology (RSM) has been used to accomplish the objective of the experimental study. The machining parameters such as cutting speed, feed rate, depth of cut and nose radius are optimized by multi-response considerations namely power consumption and tool life. A composite desirability value is obtained for the multi-responses using individual desirability values from the desirability function analysis. Based on composite desirability value, the optimum levels of parameters have been identified, and significant contribution of parameters is determined by analysis of variance. Confirmation test is also conducted to validate the test result. It is clearly shown that the multi-responses in the machining process are improved through this approach. Thus, the application of desirability function analysis in response surface methodology proves to be an effective tool for optimizing the machining parameters of 7075 Al alloy 15 wt% SiC (20–40 μm) composite. Result of this research work show that when turning is be carried out at values of machining parameters obtained by multi response optimization through desirability analysis route this will reduce power consumption by13.55% and increase tool life by 22.12%. [Copyright &y& Elsevier]

Abstract: Machining tasks are performed on machine tools with enormous amount of energy consumption in manufacturing system. Understanding and characterizing task-oriented energy consumption is significantly essential to explore the potential on energy-saving in production management. To achieve it, this paper proposes a modeling method of task-oriented energy consumption for machining manufacturing system. The energy consumption characteristics driven by task flow in machining manufacturing system are analyzed, which describes that energy consumption dynamically depends on the flexibility and variability of task flow in production processes. Based on the task-oriented energy consumption characteristics, an event graph methodology has been exploited to model the energy consumption driven by tasks in production processes of machining manufacturing system. The proposed modeling method is solved in Simulink simulation environment, and applied to select the flexible processes of tasks for optimizing energy consumption. The results show a valuable insight of energy consumption in machining manufacturing system so as to make robust decisions on the potential for improving energy efficiency. [Copyright &y& Elsevier]

Abstract: This paper has an energy consumption reduction perspective by considering alternative machining strategies and system components interactions translated into variable and constant power flows with respect to various use phase regimes of a machine tool system. The methodology is able to estimate the mechanical energy requirements of the spindle and feed axes with respect to 2.5D machining strategies by taking into account steady-state and transient regimes. In addition, the specific amount of fixed energy drawn by a machine was determined based on a careful monitoring of the energy share amongst the auxiliary equipment that supports the accomplishment of the machining tasks. The numerical results were experimentally validated and the good agreement between them led to the conclusion that the proposed methodology can be used effectively for the calculation of the total energy required by a machine tool system for the milling of a part. This enables a straightforward comparison of different milling part programs with respect to their energy consumption levels. [Copyright &y& Elsevier]

Abstract: This paper focuses on investigating several aspects of the machining process from an ecological perspective, the result being a macro-level analysis. The analysis presented here considers not only the environmental impact of the material removal process itself, but also the impact of the associated processes such as the material preparation, and the scrap processing. A macro-level assessment of the comparative life cycle environmental performance of the near-dry machining (NDM) using TiN-coated carbide tools and the flood machining (FM) is performed by a case study referring to the gear milling. The assessment, using the SimaPro 7.1.5 software and the ecoinvent1.5 database, includes combined Life Cycle Assessment (LCA) of the workpiece material, the scrap processing, the use of lubrication, and the energy consumption. [Copyright &y& Elsevier]

Advance cooling/lubrication (lubricooling) approaches are getting fame in industry and academia due to their excellent performance. A significant amount of work has been conducted to investigate the effect of various lubricooling approaches on the machinability of the hardened steel. However, sustainability-based performance evaluation of lubricooling assisted machining is missing. Thus, this paper presents 3E-based (Energy, Economics, Environment) analysis to compare the performance of various lubricooling approaches. Initially, preliminary experiments were performed to achieve optimal flow rates of various cutting fluids used in Minimum Quantity Lubrication (MQL), cryogenic cooling, and conventional emulsion (Flood) approaches. Later, optimal flow rates were used to achieve equal cutting-tool life under all machining environments. Results showed that with the same tool life method (STLM), Cryogenic-MQL(CryoMQL) could withstand aggressive cutting speeds and produce the highest productivity among all lubricooling approaches. In addition, it yielded 50% lower-priced specific production cost as compared to dry machining. However, 44.3% more specific-CO 2 emitted in CryoMQL as compared with dry machining. In summary, the hybrid CryoMQL assisted machining process is sustainable economically but not environmentally. The outcomes of the present study provide useful information, and it can help machinist to enhance the process performance. [Display omitted] • Holistic sustainability assessment of hard machining. • New holistic models for energy consumption, CO 2 emission, and cost. • Determining optimal flow rate of cooling/lubrication approaches. • Comparison of energy-conscious advanced lubricooling technologies. • Proposed CryoMQL yielded 50% lower price products. [ABSTRACT FROM AUTHOR]

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]

China's manufacturing sector accounts for more than half of the country's total energy demand, and specifically, metal processing is one of the manufacturing sectors that consume significant energy. However, reducing the electrical energy consumption of the machine tool, improving productivity, and ultimately increasing profit is a challenge while machining difficult-to-cut materials. To mitigate these challenges, advanced hybrid lubri-cooling machining is gaining attention in modern manufacturing. This paper aims to explore a holistic sustainability evaluation of advanced machining technologies specifically, the evaluation of a hybrid cryogenic-LN 2 and minimum quantity lubrication (MQL) – named 'CryoMQL'. In Addition, a comparison of CryoMQL with flood machining of Ti–6Al–4V has been performed. The machining power, specific cutting energy, specific electrical energy consumption, Cumulative Energy Demand (CED), carbon emission per part, energy cost, and production cost are considered the key sustainable performance measures. The research findings have shown that the CryoMQL approach is sustainable only when employed at high cutting conditions. Also, sustainable production of LN 2 is inevitable to reduce the CED per part in CryoMQL assisted machining. In addition, the environmental cost considering Chinese manufactured products could be an effective way to reduce carbon emissions since it shares 10.9% of the total cost of the product. The present study is applicable to implement 3E (i.e., Energy, Environment, and Economy) concepts for the Chinese Manufacturing Industry. Image 1 • External turning of Ti–6Al–4V alloy using Flood and CryoMQL conditions. • New empirical models for sustainable metrics are developed. • A holistic comparison between conventional and CryoMQL approach has been made. • During aggressive cutting, CryoMQL approach produced 27.8% lower-price products. • In present case, CryoMQL is sustainable economically but not environmentally. [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]

Process route planning is vital for implementing energy saving and low-cost production in mechanical processing as it can directly affect the energy consumption and the cost of mechanical product processing. Therefore, a multiobjective optimization approach of machining center process routes to realize energy saving and low-cost mechanical processing is proposed in this paper. To provide theoretical support for this study, process route optimization problems of a machining center are analyzed, the concept of workstep element is introduced to represent the features of machined parts, and a multiobjective optimization model is established. The optimization model is solved based on the combination of a workstep chain intelligent generation algorithm and a non-dominated sorting genetic algorithm II. Finally, the emulsion pump case process route is used as a case study to verify the feasibility and practicability of the proposed method. Comparison with actual data shows that with the single objective of energy consumption and processing cost, based on the multiobjectives of energy saving and low cost as the optimization goal, the energy consumption was 1.018 × 107 J, and the processing cost was CNY32.65. Compared with the other two experimental results, the energy consumption and the processing cost demonstrated the best comprehensive performances, consistent with energy saving, low cost and sustainable production, thereby validating the established model. Furthermore, the optimization analysis shows that the combinatorial optimization algorithm has a better solution speed and optimization precision than the general genetic algorithm. • Analyzed the underlying effects of the mechanism of process route optimization. • A multiobjective optimization model of the machining center process route was built. • The concept of workstep element was proposed. • An intelligent combinatorial optimization algorithm for the workstep chain was proposed. [ABSTRACT FROM AUTHOR]

Conventional electrical discharge machining (EDM) milling methods have some disadvantages when used to machine difficult-to-cut materials, including low machining efficiency, high energy consumption, severe electrode wear, and poor processing environments. In this paper, a sustainable and high-efficiency EDM milling method is proposed for solving the above problems. A high-efficiency green pulse power supply with high energy is designed and different environmentally friendly working fluids are used for machining experiments. The energy utilization rate, environmental performance, and machining performance under different conditions of this new method are studied. The results show that the proposed method presents high machining efficiency, low energy consumption, mild electrode wear, and environmentally friendly features. The material removal rate can reach 900.66 mm3/min and the relative electrode wear rate is 1.20% when machining the 17-4 PH stainless steel workpiece. It is found that the new pulse power supply increases the energy utilization rate by more than 200%. Compared with the conventional EDM milling methods, the proposed method can meet the requirements of energy-saving and sustainable production. Image 1 • A sustainable and high-efficiency green EDM milling method is proposed and conducted. • The effect of the injection method on machining performance is studied. • This method can obtain a high material removal rate and low electrode wear rate. • The new pulse power supply increases the energy utilization rate by more than 200%. [ABSTRACT FROM AUTHOR]

Presented in this paper is a methodology for evaluation of dry turning process along with optimal machining parameters, which allows a compromise between technological, economical, and ecological requirements. Dry longitudinal turning of Ni-based superalloy - Inconel 601 workpiece using three turning inserts coated with TiAlN + AlCr 2 O 3 by physical vapour deposition was investigated. The machining was performed at three different levels of cutting speed, feed, and depth of cut. During machining, power characteristics were measured in the form of current and interphase voltage. Machined parts and turning inserts were tested for surface roughness and tool life, respectively. Machining times, power and energy consumption were also calculated. Regression models were defined for arithmetical mean roughness, flank wear, and power. Negative environmental impact of the cutting process was monitored through midpoint and endpoint life cycle assessment (LCA). Finally, multi-objective optimization of output functions was performed. Optimization problem of minimizing the objective function was solved for eight output functions which characterize the process. The objective functions within this problem are: minimization of surface roughness, minimization of flank wear, minimization of cutting time, minimization of energy consumption, and minimization of four LCA indicators. The results indicate that dry turning of Ni-based superalloy - Inconel 601 requires higher turning inserts radii, higher cutting speeds, and smaller feeds and depths of cut. This allows acceptable flank wear of turning insert and required surface quality of workpiece, while drastically reducing cutting time, energy consumption and negative impact on the environment. The results also indicate that, within the set limitations and conditions of this experiment, increase of cutting speed can be a good strategy for reducing energy consumption and diminishing the impact of machining on the environment. • The longitudinal turning of an Inconel 601 in dry environment is presented. • Current, voltage, mean roughness, and flank wear are measured; while time, power and energy consumption are calculated. • Life cycle assessment is applied to quantify the negative environmental impacts. • Multi-objective optimization is applied to evaluate input variables of the process. • The results indicate that increasing cutting speed can be a good strategy for cleaner production. [ABSTRACT FROM AUTHOR]

As it is hard to estimate the energy consumption and to optimize the cutting parameters in different tool wear status, this paper presents a novel intelligent reasoning system for the milling process. The system consists of three parts including the improved case based reasoning (ICBR), the adaptive neural fuzzy inference system (ANFIS) and the vibration particle swarm optimization (VPSO) algorithm. The ICBR is used for providing accurate estimation of cutting power. The inputs of ICBR are cutting parameters and tool wear status, and the output is cutting power. In ICBR, the similar cases to the inputs are retrieved using K-nearest neighbor and artificial neural network (ANN) methods in the case retrieval stage. In the case reuse stage, the Gaussian fuzzy grey correlation model is proposed to estimate the cutting power based on the retrieved similar cases. The VPSO algorithm is proposed to establish ANN and ANFIS models. With the aid of ANFIS-VPSO method, the optimal cutting parameters can be obtained under different machining conditions. The experimental results have confirmed that the VPSO algorithm has better global optimization ability than PSO and DE algorithms. The cutting speed has the greatest influence on the cutting power and cutting vibration. The estimation accuracy of ICBR is up to 91.7%, which are better than that of standard CBR and other intelligent models. The optimal cutting parameters are verified with an optimization error less than 13.5% by experiment results. The intelligent reasoning system can reduce energy consumption, maintain machine tool stability and improve machining efficiency. As an important platform, this system can realize clean and intelligent production. • Vibration PSO algorithm is proved to have great global optimization ability. • The trained ANN model indicates the cutting speed is the major impact factor. • ICBR is applied to estimate the energy consumption in complex machining process. • The proposed ICBR has the overall optimal estimation performance. • Optimal cutting parameters can be obtained by using ANFIS-VPSO method. [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]