Simultaneous development of an industrial robot family, consisting typically of 2–10 robots, has been an engineering practice in robotics industry. In this process, significant scenario studies on defining product requirement specifications and associated design change are conducted. This implies that understanding the relation between product requirements and design of the robot family is of critical importance. However, in the current engineering practice, any change in requirement specification results in tremendous efforts in the re-design of the robot family. This discloses the need for efficient methodology and tools for simultaneously optimizing product requirements and design of an industrial robot family. In this work, methodology and tools have been successfully developed for simultaneously optimizing product requirements and design of an industrial robot family in a fully automated way. This problem is formulated to a multi-objective optimization problem and solved using multi-objective genetic algorithm (MOGA). Results of this work have demonstrated clearly the efficiency of this approach and the insight obtained on the relation between product requirement and product design. The developed methodology and results of simultaneous requirement specification and design optimization will be detailed in this paper. In addition, research experience and future work will also be discussed. To our best knowledge, the simultaneous optimization of product requirement and product design has not been widely investigated and explored in academia. The trade-off information explored by such approach is crucial in product development in industrial practice. Such approach will further increase the complexity of traditional design optimization approach where product requirement is normally pre-defined and used as constraint. It is certain that discussions of the addressed problem and developed methodology will contribute to promoting the significance of efforts in the research society of multi-objective design optimization, multi-objective design optimization of product families, and design automation.
Direct multi-objective optimization of parametric geometrical models stored in PLM systems to improve functional product design
