Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Optimization techniques for energy efficiency in machining processes-a review

Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving.

Sustainable Human–Robot Collaboration Based on Human Intention Classification

Sustainable manufacturing plays a role in ensuring products’ economic characteristics and reducing energy and resource consumption by improving the well-being of human workers and communities and maintaining safety. Using robots is one way for manufacturers to increase their sustainable manufacturing practices. Nevertheless, there are limitations to directly replacing humans with robots due to work characteristics and practical conditions. Collaboration between robots and humans should accommodate human capabilities while reducing loads and ineffective human motions to prevent human fatigue and maximize overall performance. Moreover, there is a need to establish early and fast communication between humans and machines in human–robot collaboration to know the status of the human in the activity and make immediate adjustments for maximum performance. This study used a deep learning algorithm to classify muscular signals of human motions with accuracy of 88%. It indicates that the signal could be used as information for the robot to determine the human motion’s intention during the initial stage of the entire motion. This approach can increase not only the communication and efficiency of human–robot collaboration but also reduce human fatigue by the early detection of human motion patterns. To enhance human well-being, it is suggested that a human–robot collaboration assembly line adopt similar technologies for a sustainable human–robot collaboration workplace.