Product Design Optimization: An Interdisciplinary Approach

2008 ◽  
pp. 1-22
Author(s):  
Sergio Romero Hernández ◽  
Omar Romero-Hernández
2012 ◽  
Vol 215-216 ◽  
pp. 592-596
Author(s):  
Li Gao ◽  
Rong Rong Wang

In order to deal with complex product design optimization problems with both discrete and continuous variables, mix-variable collaborative design optimization algorithm is put forward based on collaborative optimization, which is an efficient way to solve mix-variable design optimization problems. On the rule of “divide and rule”, the algorithm decouples the problem into some relatively simple subsystems. Then by using collaborative mechanism, the optimal solution is obtained. Finally, the result of a case shows the feasibility and effectiveness of the new algorithm.


Author(s):  
Masataka Yoshimura ◽  
Masahiko Taniguchi ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki

This paper proposes a design optimization method for machine products that is based on the decomposition of performance characteristics, or alternatively, extraction of simpler characteristics, to accommodate the specific features or difficulties of a particular design problem. The optimization problem is expressed using hierarchical constructions of the decomposed and extracted characteristics and the optimizations are sequentially repeated, starting with groups of characteristics having conflicting characteristics at the lowest hierarchical level and proceeding to higher levels. The proposed method not only effectively enables achieving optimum design solutions, but also facilitates deeper insight into the design optimization results, and aids obtaining ideas for breakthroughs in the optimum solutions. An applied example is given to demonstrate the effectiveness of the proposed method.


Author(s):  
Masataka Yoshimura ◽  
Koichi Sasaki ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki

Product design optimizations usually require the optimization of not only all performance characteristics, but also the robustness of certain performance characteristics. Obtaining optimum design solutions is far from easy, since this requires evaluations of numerous related characteristics that usually have complicated and conflicting interrelationships. Some of these characteristics can include variations of one type or another, such as manufacturing process variations, variations pertaining to the environments where the product is used, variations in how long-term use affects certain product characteristics, and so on. The difficulty of obtaining optimum design solutions is thus compounded by the need to carry out specific optimizations that provide sufficient robustness to safely accommodate anticipated ranges of variations. This paper expands the hierarchical multiobjective optimization method based on simplification and decomposition of characteristics so that optimizations can be concurrently conducted for both performance characteristics and maximization of robustness against characteristic variances. A principal cause of variations in performance characteristics is variations in the contact conditions of joints, and the utility of the proposed robust product design optimization method is demonstrated by applying it to machine-tool models that include joints.


2013 ◽  
Vol 41 (3) ◽  
Author(s):  
Masataka Yoshimura ◽  
Masaki Sato ◽  
Tomoyuki Miyashita ◽  
Hiroshi Yamakawa

Author(s):  
Masataka Yoshimura ◽  
Atsushi Takeuchi

Abstract A user-oriented product design methodology for integrating design, manufacturing and marketing is proposed and the practical design optimization procedures are constructed and presented. First, market demand analyses are conducted by dividing users into groups based on similarities of users’ needs. The product satisfaction level of each group is formulated using the users’ satisfaction levels for product attributes. Next, in order to obtain optimum design solutions effectively in the integrated decision making processes of design, manufacturing and marketing (which include an enormous number of decision variables), multiphase procedures of design optimization are constructed according to simplicity levels of shape modelings with structural characteristics and manufacturing costs which can be evaluated. Then, practical design decision making procedures from the extraction of design alternatives through the determination of detailed decision variables are described corresponding to multiphase modeling starting with simplified models and advancing to detailed models. Here, the objective function of decision making is to maximize the satisfaction level of product user. Finally, the proposed integrated design optimization method is applied to industrial robots for demonstrating the effectiveness of the method.


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