Ceramic is one of the highly competitive products in Thailand. Many Thai ceramic companies are attempting to know the customer needs and perceptions for making favorite products. To know customer needs is the target of designers and to develop a product that must satisfy customers. This research is applied Kansei Engineering (KE) and Data Mining (DM) into the customer driven product design process. KE can translate customer emotions into the product attributes. This method determines the relationships between customer feelings or Kansei words and the design attributes. Decision tree J48 and Class association rule which implemented through Waikato Environment for Knowledge Analysis (WEKA) software are used to generate a predictive model and to find the appropriate rules. In this experiment, the emotion scores were rated by 37 participants for training data and 16 participants for test data. 6 Kansei words were selected, namely, attractive, ease of drinking, ease of handing, quality, modern and durable. 10 mugs were selected as product samples. The results of this study indicate that the proposed models and rules can interpret the design product elements affecting the customer emotions. Finally, this study provides useful understanding for the application DM in KE and can be applied to a variety of design cases.

Product appearance has become a more important influence on customers' preference in regards to product purchase. Not only do customers take into account functionality and cost, but also on aesthetic and affection value. Kansei engineering (KE) utilizes a product design methodology which translates a customers' perception regarding feeling and emotion on appearance of a product into a product's design parameters. This study applied KE methodology to determine customer emotion on the shape of wine glasses and the optimal precise design parameters to obtain customer satisfaction. This study was performed using a four-factor and three-level Box-Behnken design under response surface methodology (RSM). The data obtained from the experiments were analyzed by analysis of variance. Furthermore, the data was fitted to a second-order polynomial equation using multiple regression analysis. The effects of four parameters of wine glass, namely the rim's width (A), the bowl's width (B), the bowl's height (C) and the stem's height (D) on the surface potential of five Kansei words, namely modern, quality, durable, ease of drinking and ease of handle were examined. The optimal model of wine glass design was controlled at A=90 mm, B=61.82 mm, C=126.67 mm and D=61.97 mm, respectively. The results of RSM indicate that the proposed shape design models can interpret all of customers' emotion about a product which in this case is a wine glass. Finally, this study provides useful understanding for shape parameter design and this method can be applied to a variety of design cases.

Ceramic is one of Thai products that are always changing to meet customer's requirements. Knowing customer's need is the target of designers as well as developing a product that must satisfy customers. This research applies Affective Engineering and Fuzzy Analytic Hierarchy Process (FAHP) approach into the customer-driven product design process. Affective Engineering serves to analyze the relationships between customer's perceptions and design characteristics. Six factors were retrieved: attractive, easy to drink, easy to handle, quality, modern and durable. Quantification Theory type 1 was applied to map the relationships between physical attributes and affective values. FAHP method was used to evaluate and to identify design characteristics that were compared and ranked to determine the most suitable design characteristics for a recommended design alternative. Afterward, based on all findings, some candidate samples were designed. The result of this study shows that these techniques can be applied to ceramic design in Thai manufacturing.