Sustainable manufacturing is a global front-burner issue oriented to the sustainable development of humanity and society. In this context, this paper takes the human-robot collaborative disassembly (HRCD) as the topic on its contribution to economic, environmental and social sustainability. In addition, a detailed enabling systematic implementation for HRCD is presented, combined with a set of advanced technologies such as cyber-physical production system (CPPS) and artificial intelligence (AI), and it involves five aspects which including perception, cognition, decision, execution and evolution aiming at the dynamics, uncertainties and complexities in disassembly. Deep reinforcement learning, incremental learning and transfer learning are also investigated in the systematic approaches for HRCD. The demonstration in the case study contains experiment results of multi-modal perception for robot system and human body in hybrid human-robot collaborative disassembly cell, sequence planning for an HRCD task, distance based safety strategy and motion driven control method, and it manifests high feasibility and effectiveness of the proposed approaches for HRCD and verifies the functionalities of the systematic framework.

Increasing attention is being paid to remanufacturing due to environmental protection and resource saving. Disassembly, as an essential step of remanufacturing, is always manually finished which is time-consuming while robotic disassembly can improve disassembly efficiency. Before the execution of disassembly, generating optimal disassembly sequence plays a vital role in improving disassembly efficiency. In this paper, to minimise the total disassembly time, an enhanced discrete Bees algorithm (EDBA) is proposed to solve robotic disassembly sequence planning (RDSP) problem. Firstly, the modified feasible solution generation (MFSG) method is used to build the disassembly model. After that, the evaluation criterions for RDSP are proposed to describe the total disassembly time of a disassembly sequence. Then, with the help of mutation operator, EDBA is proposed to determine the optimal disassembly sequence of RDSP. Finally, case studies based on two gear pumps are used to verify the effectiveness of the proposed method. The performance of EDBA is analysed under different parameters and compared with existing optimisation algorithms used in disassembly sequence planning (DSP). The result shows the proposed method is more suitable for robotic disassembly than the traditional method and EDBA generates better quality of solutions compared with the other optimisation algorithms.

Remanufacturing has gained increasing attention due to its economic, environmental and societal benefits as well as its contribution to the sustainability of natural resources. Disassembly is the first and usually the most difficult process in remanufacturing a product. Disassembly sequence planning, which aims to find the optimal disassembly sequence, is required to improve efficiency and reduce cost. There have been many investigations in this field and several disassembly sequence planning methods have been developed. This article reviews the main existing disassembly sequence planning methods from the perspectives of disassembly mode, disassembly modelling and planning method. The characteristics of different methods are analysed and summarised from those perspectives. Future trends in disassembly sequence planning are also discussed to reveal gaps in existing research.

The digital twin-based manufacturing system is a typical representative of smart manufacturing and has a number of advantages beyond the state of the art. However, when a manufacturing system needs to be reconfigured to meet new requirements of production, manual reconfiguration is time-consuming and high labor cost because of the complexity of the digital twin-based manufacturing system and the imperfection of related models. This problem will be even worse if there are industrial robots with characteristics of complex functions and inflexible programming in the manufacturing system. This paper presents a five-dimensional fusion model of a digital twin virtual entity for robotics-based smart manufacturing systems to support automatic reconfiguration, which can not only realistically describes physical manufacturing resources, but also represents the capabilities and dependencies of the digital twins. Reconfigurable strategies based on service function blocks, which can improve the reusability of functions and algorithms, are proposed to make the robotics-based manufacturing system satisfy the various reconfigurable requirements of different granularities and goals. Finally, a prototype system is developed to demonstrate the performance of the reconfigurable digital twin-based manufacturing system, which can improve the operation efficiency of such systems for carrying out the reconfiguring production tasks in a flexible way.