Abstract
Consumers’ emotion has become imperative in product design. In affective design field, Kansei Engineering (KE) has been recognized as a technology that enables discovery of consumer’s emotion and formulation of guide to design products that win consumers in the competitive market. Albeit powerful technology, there is no rule of thumb in its analysis and interpretation process. KE expertise is required to determine sets of related Kansei and the significant concept of emotion. Many research endeavours become handicapped with the limited number of available and accessible KE experts. This work is performed to simulate the role of experts with the use of Natphoric algorithm and thus provides solution to the complexity and flexibility in KE. The algorithm is designed to learn the process by implementing training datasets taken from previous KE research works. A framework for automated KE is then designed to realize the development of automated KE system.
Highlights
Advancement in production technology and market research has flooded the market with many products with similar design, functions as well as usability
Traditional artificial intelligence (AI) mainly concerned with reproducing the abilities of human brain, but the newer approaches is simulated based on inspiration from biological structures and behavior that are capable of autonomous self-organization
Studies show that Ant Colony Optimization, one of Natphoric algorithm seems promising to be formulated and automate the Factor Analysis process
Summary
Advancement in production technology and market research has flooded the market with many products with similar design, functions as well as usability This means that they are competing in a highly competitive market. Two methods of product development process are “product-out” and “market-in” philosophies [2]. “Product-out” philosophy or strategy takes place when a product is developed for the market based on the needs of the society. The other way of product development process is “market-in” philosophy. This approach is based on what consumers want, need and their emotional feelings [4]. The framework includes step-by-step technique of the use of KE Type 1 and will automate the word classification process that normally requires KE expert
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