Han Wang

Additive manufacturing (AM), also known as 3D printing, is associated with significant promise in the manufacturing sector. However, it has been shown that the risk of build failure has a substantial impact on the costs of AM and that this results from a relatively high level of process instability. Importantly, for such a promising technology, the effects of the risk of build failure on energy consumption have not yet been studied, which creates a significant gap in the knowledge of the real environmental performance of AM. This research addresses this gap by investigating the energy consumption of AM subject to the possibility of build failure. This is done by constructing a novel expected energy consumption model, integrating process energy consumption, the energy embedded in the raw material, and the probability of build failure as a function of the number of layers deposited. Model parameters are obtained from a series of build experiments conducted on the AM technology variant polymeric laser sintering, also known as laser powder bed fusion of polymers. The energy consumption model shows that the risk of build failure accounts for a substantial share of overall expected energy consumption, amounting to up to approximately 31% at full capacity utilization. Additionally, this paper uncovers a complex relationship between the risk of build failure and efficiency gains in per unit energy consumption resulting from increasing levels of capacity utilization (Supporting Information S1 ).

Remanufacturing plays a vital role in circular economy due to its enormous contribution in promoting resources recycling and utilizing. Disassembly of end of life (EOL) products, as a prerequisite of remanufacturing, is an effective means to improve resource utilization and reduce environmental impact. However, because of the complex quality conditions of EOL products, different disassembly method and sequence for components may lead to different effects. Based on this, a multi-objective disassembly sequence optimization model considering the quality uncertainty of EOL products is proposed in this paper. Firstly, remaining life of each component of an EOL product is calculated by using the Weibull distribution and artificial neural networks (ANN), and then the disassembly modes could be chosen according to their quality conditions. Secondly, a multi-objective disassembly sequence optimization model which takes minimum disassembly time and cost as the objective is established, and the particle swarm optimization (PSO) algorithm is employed to solve this model. Finally, a case study of drum washing machine disassembly is provided to verify the feasibility and superiority of the proposed methodology.

The ecological efficiency (EE) of the remanufacturing process occupies an important position in the whole index system of remanufacturing because it will directly affect the economic and environmental benefits of remanufacturing. Therefore, in order to study the EE of the remanufacturing process, a method is proposed to optimize and evaluate the EE of the remanufacturing process. In this method, firstly, the original remanufacturing sub-schemes of used components are designed according to the extracted fault characteristics; secondly, a set of optional process schemes are integrated by using directed graph (DG) to reduce the process schemes; thirdly, the objective function of EE is established, and then an ant colony algorithm with elite strategy (ES-ACO) is proposed to optimize the process schemes. After obtaining the optimal value of EE, the quality coefficient of used components can be calculated, and then numerical simulations (NS) are used to analyze the correlation between the quality coefficient and the optimized EE, after that, polynomial function fitting (PFF) is applied to construct the evaluation model of EE oriented to the quality coefficient, then, the evaluation model is utilized to analyze the range of quality coefficient of used components suitable for remanufacturing under cost constraints. Finally, the feasibility of this method is verified by the example of the used lathe spindle remanufacturing; and the case study shows that in the optimization phase, ES-ACO can not only optimize the process schemes but also has better performance than ACO; in the evaluation stage, the probability of deviation of the evaluation function established by using PFF is 5%, meeting the small probability event. (i.e., the occurrence of very low-frequency events), that is, the accuracy of the evaluation meets the requirements.