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Recently, sustainable development has obtained increasing attentions from governments, industry, and academia owing to the limited natural resources. In the area of energy consumption, manufacturing accounts for a major portion of the total energy usage in industry. There is a clear necessity for energy-efficient manufacturing by optimizing manufacturing activities. Condition monitoring is the technology that provides runtime information for optimization. This paper aims to provide a better understanding of past achievements and future trends of condition monitoring towards energy-efficient manufacturing. Since there are a variety of sensors and technologies that can be used for condition monitoring towards energy-efficient manufacturing, this paper divides manufacturing activities into three levels, namely unit process level, shop-floor level, and supply chain level, and summarizes and discusses the sensors and technologies required to enable energy-efficient manufacturing on each level. With the advancement of technology, condition monitoring shows the characteristic of intelligence. Intelligent sensors that can be applied to condition monitoring in energy-efficient manufacturing are also reviewed. This paper can be helpful to manufacturers who are willing to improve energy efficiency in own manufacturing practice. [ABSTRACT FROM AUTHOR]

The sustainable development and intelligent transformation of the manufacturing industry have become inevitable trends. As a typical example of the intelligent transformation, the networked manufacturing mode has been widely applied through sharing and utilizing manufacturing resources all over the world. Existing research on energy-saving use of manufacturing equipment mainly focuses on internal workshop of enterprises. However, it is also important to consider how to improve the energy efficiency in a networked manufacturing environment. Compared to traditional manufacturing mode, the networked manufacturing mode faces energy-saving challenges such as machining devices are numerous and scattered, manufacturing services are driven by customer orders, and significant random uncertainty occurs in manufacturing processes. This paper proposes a framework to select and schedule equipment resources for energy-saving in a networked manufacturing environment for different stages of manufacturing. First, to increase energy efficiency in the customer orders demand stage, a fuzzy multi-objective decision-making model is developed. This model considers economy, energy consumption, technology, and inter-enterprise collaboration. Second, to balance the competitive interests between customer orders, and to reduce manufacturing energy consumption, costs, and delivery dates in the manufacturing tasks implementation stage, an energy-saving scheduling model for equipment resources based on non-cooperative game theory is established. Then, to deal with random disturbance in a networked manufacturing, a scheduling model based on random chance constraint programming is built to optimize energy, economy, and production efficiency at the active preventive stage. Finally, a cloud platform implementation method of the proposed framework is summarized. The proposed framework helps provide theoretical analysis that is significant to energy-saving allocation of the equipment resources in the networked manufacturing mode. [ABSTRACT FROM AUTHOR]

Sustainability is a growing interest in basic industry. The ultimate goal for manufacturing industry is environmental releases, and sustainable development. By reducing energy consumption, economic and environmental performance can be significantly improved for the manufacturing systems. In this paper, the cutting force and specific cutting energy (SCE) consumption are evaluated based on the calculated material removal volume. Three different cutting tool geometries, including the C-, D-, and T-shaped turning indexable tools, are examined to investigate how cutting tool geometry affects the cutting force and SCE. Experimental research and theoretical modeling analysis have been conducted. Statistical analysis for theoretical and experimental SCE has been conducted to extract the effect of each factor on variation. Results of this research show that the more volume of cutting tool involved into the workpiece, the smaller the SCE produced. Optimal setting for sustainability target in terms of minimum cutting energy being consumed could be obtained through the established model. [ABSTRACT FROM AUTHOR]

Currently, sustainable manufacturing (SM) attracts more and more attentions due to the increasing environmental pollution and energy shortage threat. Energy consumption is a fundamental element of SM for its valuable effect in the environmental impacts and business opportunities. Analyzing the relationship between process parameters and energy consumption is helpful to reduce production costs, eliminate negative environmental impacts, and increase business opportunities. Since energy consumption is impacted by the inherent uncertainties in the machining process, how to model energy consumption presents a significant challenge. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm and Genetic Programming, and has been successfully applied in formula finding. In this paper, a Greedy Randomized Adaptive Search Procedure (GRASP)-based Gene Expression Programming, named GGEP, is proposed to predict the face milling energy consumption. In this proposed GGEP approach, a GRASP-based learning mechanism and an iterative re-start mechanism have been introduced into the basic GEP. At the basis of defining a GGEP environment for the energy consumption prediction, an explicit model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The analysis of experiment results reveals that the proposed approach models and predicts the energy consumption with high accuracy and high-speed convergence. Moreover, in order to better study the mechanism of machining, the influence and contribution of different impact factors on energy consumption in face milling are analyzed. [ABSTRACT FROM AUTHOR]

In recent years, energy sustainability has attracted increasing attention from scholars, policy makers, and practitioners. However, in practical competitiveness, companies also have to maintain flexibility to deliver customer requirements. Existing theory has largely seen flexibility and sustainability as separate issues, ignoring their relationship. To address this gap, we attempt to explore energy sustainability using flexible operations management practices in India. Following a case research methodology, we investigate how flexible operations management practices embraced by a leading organization that minimizes energy consumption in plants with better resource utilization and better quality of work life for staff to meet the future requirements based on market forecast. We solve a real-time multi-criteria strategic capacity planning problem with multiple objectives such as throughput maximization, waste minimization, and resource utilization maximization by using discrete event simulation technique. Our data stems from ongoing research within a car manufacturing company in India, located at Pune. Shift timings, setup time, production batch size, and differential rated capacities of plants are considered as the variables in this study. However, optimal routing flexibility combined with volume flexibility helps to achieve significant reduction in energy consumption. Finally, we offer future research questions on flexible operations management practices. [ABSTRACT FROM AUTHOR]