Performance recovery is the main emphasis in most remanufactured machine tools, rather than the effective combination of customer requirements (CRs) and redesign processes. Because of this, remanufactured machine tools do not reach their potential competitiveness, highly restricting the implementations to recover used machine tools. To help remedy this, fuzzy nonlinear regression is applied to the fuzzy relationships between CRs and redesign parameters, and the fuzzy correlations among redesign parameters are analysed by fully considering the uncertainties between CRs and redesign parameters. Improved planning equations based on fuzzy nonlinear regression are proposed by injecting fuzziness into the original planning equations. The redesign process of a machine tool is taken as an example to implement the proposed method. The results show that the improved planning equations can obtain higher customer satisfaction compared to the unimproved planning equations. This can provide new thinking to effectively combine CRs and redesign processes.

The cladding layer formed by laser cladding is formed by solidification of the molten pool. The quality of the cladding layer is directly determined by the evolution of the molten pool. The numerical simulation of the evolution of the molten pool is carried out, revealing the relationship between different powers and the evolution of the molten pool. The results show that the volume of the molten pool exhibits a nonlinear expansion of “expansion-shrinkage-re-expansion” before reaching the steady state, and the volume and expansion rate of the molten pool are determined by the laser power. In addition, the degree of shrinkage of the molten pool and the speed of convection can also be controlled by the laser power. When the laser power reaches a certain level, evaporation occurs on the surface of the molten pool. The convection pattern of the molten pool can be changed by evaporation. However, due to the impact of shielding gas and Marangoni movement, the effect of evaporation on the geometry of the cladding layer is limited.

The development of lot streaming technology in flexible production systems has greatly benefited the aerospace, semiconductor, automotive, pharmaceutical and other discrete manufacturing industries. In this paper, we address a lot-splitting optimization problem of flexible job shop. To handle the issues of large performance gap and low searching efficiency of optimal scheme caused by strong randomness of lot-splitting, a knowledge-based method is proposed. First, under the equal lot-splitting strategy and variable minimum sub-lots constraint, a lot-splitting scheduling model of flexible job shop is constructed. Second, the effectiveness of lot-splitting schemes is assessed by appropriate data-driven algorithms according to the different scales of the problem. Moreover, a neighborhood search strategy based on the number of sub-lots is introduced to improve the search ability of the method. Then, by analyzing the characteristics of the problem, a parameter adjustment strategy based on problem-specific knowledge is presented to balance the search time and performance. Next, sensitivity analyses are carried out to calibrate the parameters based on the design of experiment Taguchi method. Finally, extensive experiments are conducted to evaluate the effectiveness of the proposed method based on the benchmark problems with single or multiple objectives. The results show that the proposed method can obtain high-quality optimized lot-splitting schemes. Note to Practitioners —In discrete manufacturing industries, the flexible production mode in multi-variety and small-batch has become the mainstream. The flexible job shop lot-splitting optimization problem in such a mode is of great significance to practitioners. We mathematically characterize the lot-splitting problem in a flexible job shop considering energy consumption and solve it through a new general multi-objective optimization framework by integrating the lot-splitting and job shop scheduling that is activated from problem-specific knowledge. Existing lot-splitting optimization approaches fail to solve the concerned problem. This work introduces appropriate data-driven algorithms to assess the scheduling schemes after lot-splitting. By updating the minimum lot-size of jobs and the archive set, the search of the optimal lot-splitting scheme is directional. Furthermore, the global and local iterations of the method are adjusted according to the problem size and the number of sub-lots to balance the performance and search time. The results of comparative study show that the proposed method outperforms other algorithms well and can greatly help practitioners manage the lot-splitting scheme of jobs.

To cope with the problems of frequent mold changes, long production cycles and serious logistics crossings in workshop of aerospace enterprise. First, a manufacturing cell layout planning method based on the feature bit code domain method and K-Means++ is proposed to realize the accurate division of manufacturing cells. Then, a multiobjective optimization method of dynamic reconstruction layout based on improved fruit fly optimization algorithm (IFOA) is proposed to solve the reconstruction layout optimization of the production workshop problem with the optimization objectives of logistics cost, reconstruction cost, loss cost, and cell integrated area. Finally, plant simulation software is applied to simulate the workshop layout before and after optimization. The simulation results show that the logistics cost of the workshop cell layout after optimization is reduced by 8.7, the utilization rate of the workshop area is improved by 5.2, and the value-added rate of products is increased by 6.6, which verifies the effectiveness and feasibility of the proposed model and method.