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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]

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]

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]

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]

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]