A study on modeling customer preferences for conceptual design

Han, Soonyoung; Seo, Seokhoon; Choi, Hae-Jin

doi:10.1007/s12206-015-1105-y

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…This is an algorithm based on the human nervous system and brings it to mathematical model. It is an algorithm that learns many experiments or simulation data through feedback and adjusts the weights to derive nonlinear and complex correlations that are difficult for human to construct as shown in Figure 1 (Han, Seo, & Choi, 2015). In the back-propagation process, weight changes continuously to reduce error which is difference of real output and output of the previous function.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Assembly Quality Diagnosis of Planetary Gear Sets

Han,

Huh,

Choi

2017

PHMAP_CONF

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Product quality is one of the most important factors to be considered in manufacturing industries. Autonomous production line rapidly increases its productivity; however, quality check becomes a difficult problem and a smart system for quality assurance is indispensable in the modern production line In this study, we developed a transmission error based machine learning algorithm to check the quality of planetary gear assemblies and identify the defective parts in the planetary gear sets.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Assembly Quality Diagnosis of Planetary Gear Sets

Han,

Huh,

Choi

2017

PHMAP_CONF

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“…Therefore, inference methods such as artificial neural networks (ANN) [12,13], fuzzy inference (FI) [14,15], rough set theory (RST) [14,15], and Bayesian networks (BN)) [16][17][18] is widely used to present a set of best solutions for customers. S Han et al [13] applied ANN to the conceptual design of the car, established the ANN model, and predicted the configuration of the concept car. In YS Juang et al [15] using FI to change the subjective decisionmaking of traditional machine tool plants, the Customer Demand Information System (CRIS) was established.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Approach to Customer Requirements to Customized Product Model

Yang

Jiao

et al. 2019

Discrete Dynamics in Nature and Society

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Customizing products based on customer needs is an irreversible trend, and many companies strive to provide customized products to customers in less time. Customer requirements are a key factor in the company's ability to provide customized products. In order to better meet customer needs, solve the problem of incomplete and inaccurate expression, and improve the correlation between customized product performance and customer demand, a customized product method based on Bayesian network is proposed. First, the company built a custom product model based on Bayesian networks. According to the model, the customer selects some nodes and their related information. Then, we can accurately predict the final product model through the link tree algorithm, test the customer demand node to determine the focus of the customer's needs, and optimize the model. Finally, an example of a multifunctional nursing bed is used to illustrate the effectiveness of the method.

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Performance prediction of a modular product variant with RS-SVM

Zhang

Zheng

et al. 2019

Procedia CIRP

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A study on modeling customer preferences for conceptual design

Cited by 4 publications

References 16 publications

Assembly Quality Diagnosis of Planetary Gear Sets

Assembly Quality Diagnosis of Planetary Gear Sets

Bayesian Network Approach to Customer Requirements to Customized Product Model

Performance prediction of a modular product variant with RS-SVM

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