scholarly journals Comparison of Two Quantitative Analysis Techniques to Predict the Evaluation of Product Form Design

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hung-Yuan Chen ◽  
Yu-Ming Chang ◽  
Ting-Chun Tung
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Predictive Performance ◽  
Consumer Perception ◽  
Product Form ◽  
Modeling Technique ◽  
Form Design ◽  
Product Form Design ◽  
Product Forms

Consumer satisfaction with a product’s form plays an essential role in determining the likelihood of its commercial success. A consumer perception-centered design approach is proposed in this study to aid product designers with incorporating consumers’ perceptions of product forms in the design process. The consumer perception-centered design approach uses the linear modeling technique (multiple linear regression) and the nonlinear modeling technique (neural network) to determine the satisfying product form design for matching a given product image. A series of experimental evaluations are conducted to collect evaluation results for examining the relationship between the automobile profile features and the consumers’ perceptions of the automobile image. The result of predictive performance comparison shows that both the nonlinear neural network modeling technique and the multiple linear regression technique are comparably good for predicting the consumers’ likely response to a particular automobile profile since the predictive performance difference between the two modeling techniques is very slight in this study. Although this study has chosen a 2D automobile profile for illustration purposes, the concept of the proposed approach is expansively applicable to 3D automotive form design or other consumer product forms.

A neural network based approach for product form design

2002 ◽  
Vol 23 (1) ◽  
pp. 67-84 ◽  
Author(s):  
Shih-Wen Hsiao ◽  
H.C Huang
Keyword(s):  
Neural Network ◽  
Product Form ◽  
Form Design ◽  
Product Form Design

Computer-Aided Form Generation for Product Design

2010 ◽  
Vol 97-101 ◽  
pp. 3785-3788
Author(s):  
Hung Cheng Tsai ◽  
Tien Li Chen ◽  
Hung Jung Tsai ◽  
Fei Kung Hung
Keyword(s):  
Computer Aided Design ◽  
Product Form ◽  
Knowledge Based ◽  
Blending Method ◽  
Computer Aided ◽  
Design Activities ◽  
Form Design ◽  
Product Form Design ◽  
Aided Design ◽  
Product Forms

The product form design activities involve a high degree of uncertainty and complexity and are therefore not easily formulated, coded and regularized. Consequently, very few of the computer-aided design approaches presented in the literature can support the conceptual form design tasks typically performed at the preliminary stages of a product’s development cycle. To enable designers to perform their design activities more objectively and efficiently, this paper combines the principles of fuzzy set theory, the shape-blending method and genetic algorithms to generate a knowledge-based approach for product form design based upon a database describing the relationships between different product forms and their corresponding perceptual image evaluations.

Kansei Quality Prediction Method of Product Form Design Based on Quantification Theory

2014 ◽  
Vol 548-549 ◽  
pp. 1922-1927
Author(s):  
Zhi Yong Xiong ◽  
Ying Xin Weng ◽  
Li Jun Jiang ◽  
Liu Qing Ruan
Keyword(s):  
Evaluation Criteria ◽  
Prediction Method ◽  
Consumer Products ◽  
Product Form ◽  
Quality Prediction ◽  
Quantification Theory ◽  
Different Types ◽  
Form Design ◽  
Product Form Design ◽  
Product Forms

Customer needs towards consumer products become diverse and sensuous in a market where many products have similar performance and functionalities. It is important to investigate the mapping relationship between product form elements and corresponding kansei qualities in order to design product those meet different types of customers’ subjective impressions. In this paper, the authors established kansei quality evaluation criteria of product forms and presented a new kansei quality prediction method of product form design which introduced quantification theory type II to model the Form-kansei quality mapping relationship. Finally, the authors gave the implementation procedures of kansei quality prediction model in detail. The proposed method is adapted to kansei quality prediction for the majority of consumer products form design.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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