Design is a high-level and complex thinking activity of human beings, using existing knowledge and technology to solve problems and create new things. With the rise and development of intelligent manufacturing, design has increasingly reflected its importance in the product life cycle. Firstly, the concept and connotation of complex product design is expounded systematically, and the different types of design are discussed. The four schools of design theory are introduced, including universal design, axiomatic design, TRIZ and general design. Then the research status of complex product design is analyzed, such as innovative design, digital design, modular design, reliability optimization design, etc. Finally, three key scientific issues worthy of research in the future are indicated, and five research trends of “newer, better, smarter, faster, and greener” are summarized, aiming to provide references for the equipment design and manufacturing industry.
Given the great inconvenience caused by the randomness of the fault to the maintenance work, it is necessary to perform on-site and efficient disassembly planning for the faulty parts and present them in combination with virtual reality (VR) technology to achieve rapid repair. As a promising method in solving dynamistic and stochastic problems, deep reinforcement learning (DRL) is adopted in this paper for the solution of adaptive disassembly sequence planning (DSP) in the VR maintenance training system, in which sequences can be generated dynamically based on user inputs. Disassembly Petri net is established to describe and model the disassembly process, and then the DSP problem is defined as a Markov decision process (MDP) that can be solved by the deep Q-network (DQN). For handling the temporal credit assignment with sparse rewards, the long-term return in DQN is replaced with the fitness function of the genetic algorithm (GA). Meanwhile, the update method of gradient descent in DQN is adopted to speed up the iteration of the population in GA. A case study has been conducted to prove that the proposed method can provide better solutions for DSP problems in terms of VR maintenance training.
Maintenance is a critical aspect of complex products through entire life cycle, often requiring coordination of production planning and available resources, while previous studies appear to have rarely addressed. With this in mind, this paper presents a prescriptive maintenance framework based on digital twins for reducing operational risk and maintenance costs of complex equipment clusters. Virtual entities are firstly constructed for each single asset in multiple dimensions, which use real-time or historical sensing data collected from the physical entities to predict the corresponding remaining useful life (RUL). Then such RUL information is incorporated into a stochastic programming model with chance constraints to enable dynamic decision making. In particular, a risk-based optimization model is formulated to take full account of the physical distances between facilities and production gaps. Further, a dual-sense pyramidal transformer model is proposed to sense important details of data in both time and space while capturing temporal dependencies at different scales. We have demonstrated through a maintenance case of turbofan engines that the proposed scheme significantly lowers total maintenance costs while reducing frequent visits from maintenance personnel.
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