Comprehensive Product Platform Planning (CP3) Framework: Presenting a Generalized Product Family Model

Volume 5: 37th Design Automation Conference, Parts a and B

et al. 2011

Self Cite

This paper explores the effectiveness of the recently developed surrogate modeling method, the Adaptive Hybrid Functions (AHF), through its application to complex engineered systems design. The AHF is a hybrid surrogate modeling method that seeks to exploit the advantages of each component surrogate. In this paper, the AHF integrates three component surrogate models: (i) the Radial Basis Functions (RBF), (ii) the Extended Radial Basis Functions (E-RBF), and (iii) the Kriging model, by characterizing and evaluating the local measure of accuracy of each model. The AHF is applied to model complex engineering systems and an economic system, namely: (i) wind farm design; (ii) product family design (for universal electric motors); (iii) three-pane window design; and (iv) onshore wind farm cost estimation. We use three differing sampling techniques to investigate their influence on the quality of the resulting surrogates. These sampling techniques are (i) Latin Hypercube Sampling (LHS), (ii) Sobol’s quasirandom sequence, and (iii) Hammersley Sequence Sampling (HSS). Cross-validation is used to evaluate the accuracy of the resulting surrogate models. As expected, the accuracy of the surrogate model was found to improve with increase in the sample size. We also observed that, the Sobol’s and the LHS sampling techniques performed better in the case of high-dimensional problems, whereas the HSS sampling technique performed better in the case of low-dimensional problems. Overall, the AHF method was observed to provide acceptable-to-high accuracy in representing complex design systems.

Section: Product Family Design (For Universal Electric Motors)mentioning

confidence: 99%

Surrogate Modeling of Complex Systems Using Adaptive Hybrid Functions

Zhang

Volume 5: 37th Design Automation Conference, Parts a and B

et al. 2011

Self Cite

Comprehensive Product Platform Planning (CP3) Framework

Journal of Mechanical Design

Khire³

2011

Development of a family of products that satisfies different market niches introduces significant challenges to today’s manufacturing industries—from development time to aftermarket services. A product family with a common platform paradigm offers a powerful solution to these daunting challenges. This paper presents a new approach, the Comprehensive Product Platform Planning (CP3) framework, to design optimal product platforms. The CP3 framework formulates a generalized mathematical model for the complex platform planning process. This model (i) is independent of the solution strategy, (ii) allows the formation of sub-families of products, (iii) allows the simultaneous identification of platform design variables and the determination of the corresponding variable values, and (iv) seeks to avoid traditional distinctions between modular and scalable product families from the optimization standpoint. The CP3 model yields a mixed integer nonlinear programming problem, which is carefully reformulated to allow for the application of continuous optimization using a novel Platform Segregating Mapping Function (PSMF). The PSMF can be employed using any standard global optimization methodology (hence not restrictive); particle swarm optimization has been used in this paper. A preliminary cost function is developed to represent the cost of a product family as a function of the number of products manufactured and the commonality among these products. The proposed CP3 framework is successfully implemented on a family of universal electric motors. Key observations are made regarding the sensitivity of the optimized product platform to the intended production volume.

Comprehensive Product Platform Planning (CP3) Using Mixed-Discrete Particle Swarm Optimization and a New Commonality Index

Volume 3: 38th Design Automation Conference, Parts a and B

Khire

2012

A product family with a common platform paradigm can increase the flexibility and responsiveness of the product-manufacturing process and help take away market share from competitors that develop one product at a time. The recently developed Comprehensive Product Platform Planning (CP3) method allows (i) the formation of sub-families of products, and (ii) the simultaneous identification and quantification of platform/scaling design variables. The CP3 model is founded on a generalized commonality matrix representation of the product-platform-plan. In this paper, a new commonality index is developed and introduced in CP3 to simultaneously account for the degree of inter-product commonalities and for the overlap between groups of products sharing different platform variables. To maximize both the performance of the product family and the new commonality measure, we develop and apply an advanced mixed-discrete Particle Swarm Optimization (MDPSO) algorithm. In the MDPSO algorithm, the discrete variables are updated using a deterministic nearest-feasible-vertex criterion after each iteration of the conventional PSO. Such an approach is expected to avoid the undesirable discrepancy in the rate of evolution of discrete and continuous variables. To prevent a premature stagnation of solutions (likely in conventional PSO), while solving the high dimensional MINLP problem presented by CP3, we introduce a new adaptive diversity-preservation technique. This technique first characterizes the population diversity and then applies a stochastic update of the discrete variables based on the estimated diversity measure. The potential of the new CP3 optimization methodology is illustrated through its application to design a family of universal electric motors. The optimized platform plans provide helpful insights into the importance of accounting for the overlap between different product platforms, when quantifying the effective commonality in the product family.