Revolutionizing Manufacturing Processes: Simulation-Based Optimization Enhances Assembly Geometry
Disturbances in manufacturing and assembly processes can often lead to deviations and geometrical variations from the ideal geometry, which in turn result in functional and aesthetic issues with the final product. The manufacturing industry has long desired the ability to control these disturbances and transform noise factors into control factors through a robust design perspective. With recent technological breakthroughs, digitalization reforms, and the availability of big data from manufacturing processes, the concept of digital twins has gained significant attention from researchers and practitioners.
In line with this trend, Söderberg et al. have introduced the concept of a geometry assurance digital twin, along with the idea of a self-compensating individualized assembly line. The vision of this concept is to steer the assembly process through online real-time optimization using the digital twin medium. It is important to note that joining sequences have a substantial impact on the final geometrical outcome in assembly. However, optimizing these sequences for optimal geometrical outcomes can be both experimentally and computationally expensive.
To address this challenge, the study focuses on simulation-based joining sequence optimization using compliant variation simulation. The researchers initially identified limitations in the formulations and algorithms used in existing literature. To overcome these limitations, two evolutionary algorithms were introduced to compare their computational performances with the genetic algorithm.
Additionally, a reduced formulation of the sequence optimization was developed by identifying critical points, or geometry joints, to lock the geometry. A rule-based method was proposed to initiate the evolutionary algorithm and enhance its computational efficiency. Further improvements were made by introducing a contact displacement minimization approach to generate model-dependent rules.
Moreover, a surrogate-assisted approach was introduced to parallelize the computation process, significantly reducing computation time. This approach also revealed the potential of simulation-based geometry joint identification alongside complete sequence determination. The results obtained from these studies demonstrate that simulation-based real-time optimization of joining sequences is indeed achievable through a parallelized search algorithm, which can be implemented within the geometry assurance digital twin concept.
These findings provide valuable insights for controlling the joining sequence in the assembly process, ultimately improving geometrical quality in a cost-effective manner and saving significant computational time. The utilization of simulation-based optimization can revolutionize manufacturing processes and contribute to the advancement of assembly geometry in various industries.
