Computing the Assembly Guidance for Maximizing Product Quality in the Virtual Assembly

Assembly is the final process of manufacturing, and a good assembly plan reduces the effect of the tolerance generated in the early stages by the tolerance elimination. In the current assembly lines, the assemblers pick up the workpieces and install them together by the assembly instructions. When the workpieces are oversize or undersize, the product can not be installed correctly. Therefore, the assembler considers the secondary processing to fix the tolerance and then installs them together again. The product could be installed, but the product quality may be reduced by the secondary process. So, we formulate the assembly process as a combinatorial optimization problem, named by the dimensional chain assembly (DCA) problem. Given some workpieces with the corresponding actual size, computing the assembly guidance is the goal of the DCA problem, and the product quality is applied to represent the solution quality. The assemblers follow the assembly guidance to install the products. We firstly prove that the DCA problem is NP-complete and collect the requirements of solving the DCA problem from the implementation perspective: the sustainability, the minimization of computation time, and the guarantee of product quality. We consider solution refinement and the solution property inheritance of the single-solution evolution approach to discover and refine the quality of the assembly guidance. Based on the above strategies, we propose the assembly guidance optimizer (AGO) based on the simulated annealing algorithm to compute the assembly guidance. From the simulation results, the AGO reaches all requirements of the DCA problem. The variance of the computation time and the solution quality is related to the problem scale linearly, so the computation time and the solution quality can be estimated by the problem scale. Moreover, increasing the search breadth is unnecessary for improving the solution quality. In summary, the proposed AGO satisfies with the necessaries of the sustainability, the minimization of computation time, and the guarantee of product quality for the requirements of the DCA, and it can be considered in the real-world applications.