With growing environmental and sustainability-related concerns, recovery optimization of mechanical products has been gaining increased exposure. It facilitates environmental sustainability through the improvement in the life-cycle material efficiency and reduction in environmental impact with disassembly sequence planning, component reuse, and material recycling. Traditional product recovery separates end-of-life (EOL) products into components and selects EOL options of components. However, there are many practical cases in which the recovery of a set of subassemblies and components leads to better net revenue than that of a complete set of single components. This paper proposes to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. Finally, a case study is described to illustrate the proposed method and the influence on decision variables of the tradeoff between the recovery profit and environmental impact is discussed.

Successful product development relies on the enterprise system (ES) to manage product lifecycle data and support decision-making in various levels. Since the amount of data generated in the industrial internet of things (IIoT) is increasing dramatically, new paradigms of the ES are required to realize the distributed intelligence. Recent progress in edge computing has enabled advances in decentralized decision support systems. Although the product design is a key stage of new product development, none of the recent studies in edge-based ES have shed lights on this stage. Therefore, an edge-based ES framework for design scheme evaluation is proposed in this paper. It not only accomplishes most of the decision-making tasks within the edge mesh of an enterprise, but also solves the information island of multiple decision makers from different geographical regions. Moreover, a multi-group decision-making algorithm is proposed to enable collaborative design scheme evaluation. The evaluation processes of designers, experts and customers are analyzed systematically in this paper. For one advantage, the trapezium cloud model is applied to convert the qualitative evaluation information given by designers and experts into qualitative values. It decreases the cognitive discrepancy and solves the information distortion. For another, EEG data are utilized to explore the implicit psychological states of customers during the product operation. All the evaluation results of multiple groups of decision makers are integrated by the fuzzy measure and Choquet integral to determine the optimal design scheme. A case study is conducted to illustrate the feasibility of the method proposed in this paper.

4D printing deforms a 2D foldable structure to another shape evolve over time by using heterogeneous material. The deformation of the 2D foldable structure is stimulated by actuators that are fabricated by shape memory materials or bilayer structures. Therefore, the deformation programming method of actuators is a critical technology in 4D printing. This paper proposes a method for programming the deformation of a temperature-driven bilayer structure actuator in 4D printing. The thermo-mechanical mechanism of the bilayer structure actuator is analyzed and three kinds of deformation behavior are modeled. Then a constitutive model with five main deformation programming parameters including the line width, the print height, the print temperature, the filled form, and the stimulation temperature is fitted by the orthogonal experiment and response surface method. The permanent deformation of the bilayer structure actuator results from the programmed parameters and time evolution of the 3D printed structure upon heating. A typical temperature shape memory material polylactic acid is used as a case study to illustrate the methodology and a desired programmed deformation is achieved.

Concerns have been increasing regarding the environmental sustainability of disassembly activities that take place in various recovery operations at end of life stage of products, and subsequently, disassembly activities have been gaining increased exposure. The disassembly line is a good choice for automated disassembly of end-of-life products. Although interest in addressing energy saving in manufacturing is rising, the study of incorporating energy consumption reduction and disassembly efficiency improvement into disassembly line balancing is still limited. The analysis, design, and balanced decision-making for disassembly lines are urgently needed in order to make the disassembly line as energy-saving as possible. In this paper, an energy-saving optimization method, which was used in scheduling workstation selection and the disassembly sequence for the disassembly line, is proposed. Energy consumption of each stage of the disassembling process was analyzed and modeled. Then, a mathematical model of the disassembly line balancing problem with energy saving considerations was formulated and the objective of energy consumption was integrated into the objectives of cost and working load in order to balance a disassembly line. Optimal solutions for the disassembly line balancing problem with an energy saving consideration were obtained by using an artificial bee colony algorithm, which was a feasible way of minimizing workstations, and ensuring similar working load, as well as minimizing energy consumption. Finally, a case study of disassembly line balancing for a typical driving system is presented to illustrate the proposed method.