Repair and restoration is an important step in remanufacturing as it ensures end-of-life products are returned to as-new condition before entering the subsequent life cycle. Currently, such processes are carried out manually by skilled workers. The advent of additive manufacturing (AM) has encouraged researchers to investigate its potential in automated repair and restoration, thus rendering it as a more effective method for remanufacturing. However, the application of this widespread technology for repair and restoration in remanufacturing is still new. This paper provides an overview of the principles and capabilities offered by the existing metal AM technology for object repair and restoration namely, direct energy deposition, powder bed fusion, and cold spray technology. Their applications in the repair and restoration of remanufacturable components are presented and discussed along with issues requiring attention from the perspectives of object design and process systems capabilities. The study provides a compilation of the challenges in AM repair and restoration, which primarily lie in the aspects of geometrical complexity, geometric dimensioning and tolerancing, material compatibility, and pre-processing requirements since it is critical for remanufacturing to restore end-of-life components to as new-condition. The paper concludes with suggestions for further works in AM restoration to enable product life cycle extension in the circular economy.

Tuning of a proportional-integral-derivative (PID) controller for a complex multi-joint structure, such as an exoskeleton, using conventional methods is difficult and imprecise. In this paper, an optimal PID tuning method for a 3-dimensional model of a lower-limb human exoskeleton in gait training condition is presented. The dynamic equation of the human-exoskeleton is determined using a Lagrangian approach, and its transfer function is established in a closed-loop control system. PID controller gains, initialized by the Ziegler–Nichols (Z-N) method, are used as the input to an adaptive particle swarm optimization (APSO) algorithm for minimizing the multi-joint trajectory error. The optimized controller is tested in the Gazebo virtual environment and compared with the Z-N and conventional optimization methods. The numerical analysis shows that the PID controller tuned by a combination of Z-N and APSO improves the performance of a lower-limb human exoskeleton in gait training.

The conventional component repair in remanufacturing involves human decision making that is influenced by several factors such as conditions of incoming cores, modes of failure, severity of damage, features and geometric complexities of cores and types of reparation required. Repair can be enhanced through automation using additive manufacturing (AM) technology. Advancements in AM have led to the development of directed energy deposition and laser cladding technology for repair of damaged parts and components. The objective of this systematic literature review is to ascertain how intelligent systems can be integrated into AM-based repair, through artificial intelligence (AI) approaches capable of supporting the nature and process of decision making during repair. The integration of intelligent systems in AM repair is expected to enhance resource utilization and repair efficiency during remanufacturing. Based on a systematic literature review of articles published during 2005–2021, the study analyses the activities of conventional repair in remanufacturing, trends in the applications of AM for repair using the current state-of-the-art technology and how AI has been deployed to facilitate repair. The study concludes with suggestions on research areas and opportunities that will further enhance the automation of component repair during remanufacturing using intelligent AM systems.

Firms are finding it increasingly important to leverage social media to facilitate knowledge access, get valuable feedback, and improve innovations to cater for emerging markets. However, using social media without integrating other key factors does not seem to add value to innovation efforts. Therefore, this study investigates the potential of social media usage (SMU) in enhancing green product innovation (GPI) and how two types of environmental collaboration may affect that relationship, which is a subject that has been under-explored. First, the literature on the expansion of the use of social media in enhancing GPI was reviewed to develop the theoretical framework and hypotheses. Then, data collected from 211 manufacturing firms were analysed using structural equation modelling to examine the proposed relationship. The results revealed that SMU does not directly influence GPI. Rather, internal environmental collaboration (IEC) and environmental collaboration with suppliers (ECS) fully mediate the relationship between SMU and GPI. The results further disclosed a positive relationship between IEC and ECS, where both types of environmental collaboration seem to be key factors in improving GPI. Hence, this study highlights the importance of knowledge sharing through environmental collaborations for the generation of ideas to improve products in order to remain competitive in the market.

The concept of repair and restoration using additive manufacturing (AM) is to build new metal layers on a broken part. It is beneficial for complex parts that are no longer available in the market. Optimization methods are used to solve product design problems to produce efficient and highly sustainable products. Design optimization can improve the design of parts to improve the efficiency of the repair and restoration process using additive manufacturing during the end-of-life (EoL) phase. In this paper, the objective is to review the strategies for remanufacturing and restoration of products during or at the EoL phase and facilitate the process using AM. Design optimization for remanufacturing is important to reduce repair and restoration time. This review paper focuses on the main challenges and constraints of AM for repair and restoration. Various AI techniques, including the hybrid method that can be integrated into the design of AM, are analyzed and presented. This paper highlights the research gap and provides recommendations for future research directions. In conclusion, the combination of artificial neural network (ANN) algorithms with genetic algorithms as a hybrid method is a key solution in solving limitations and is the future for repair and restoration using additive manufacturing.

This study proposed the use of an LCA supported by a design efficiency evaluation based on Design for Assembly principles to reduce the environmental impact of a product. To illustrate the methodology, a water leakage alarm (WLA) was selected as the object for a case study. Based on the identification and evaluation of the LCA results, it was inferred that the stage with the highest environmental impact was the manufacturing stage (75.35%), followed by the use stage (23.88%), the disposal of the WLA (0.64%), and finally, the disposal of the batteries (0.14%). For the manufacturing stage, the most interrelated categories were the hazardous waste and human toxicity, while the use stage was the main contributor to ozone depletion and acidification. Moreover, the disposal of the WLA and batteries contributed to the bulk waste. Furthermore, from the assembly evaluation, the design efficiency of the product was 14%. Two recommendations for improving the design of the WLA were: (1) to reduce the number of screws from three units to one unit, and (2) to eliminate the use of a cable and to replace it with a wireless component. By implementing both the proposed recommendations, the design efficiency was improved by as much as 34%. From the environmental perspective, there is not much difference between the wired alarm and wireless alarm. The wired alarm was considered to be more environmentally friendly in terms of product manufacturing but the wireless alarm has an advantage in terms of design and energy efficiency. By combining LCA and DFA design evaluation, a more comprehensive perspective of the product life cycle can be achieved.