TY - GEN
T1 - A Product Concept Generation and Evaluation Method Based on Knowledge
AU - Chen, Xi
AU - Gong, Lin
AU - Liu, Fang
AU - Wang, Jinyi
AU - Tang, Ya
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/14
Y1 - 2019/5/14
N2 - Conceptual design is an important stage in product design, and function design is the basis of conceptual design. But the product that rely on the designer's experience always exists preferences and defects because of the limitations of designer's knowledge. To solve this problem, this paper proposed a product concept generation and evaluation method based on knowledge. Firstly, we adopted syntactic dependency parsing (SDP) to extract the main semantics structures from the existing product in the market. Secondly, word embedding was adopted for the vectorization of the main function structure, then a product base and a function base was constructed. Normally, every kind of product has its basic types of functions, so we used K-means to classify the functions. We then randomly choose a function from each cluster to combine a new product. As for the evaluation of each product, we calculated quantitatively the novelty, diversity and feasibility of solutions by semantic similarity. According to the comparison of the solutions and the existed products in novelty, diversity and feasibility. We can deduce that our methodology is feasible.
AB - Conceptual design is an important stage in product design, and function design is the basis of conceptual design. But the product that rely on the designer's experience always exists preferences and defects because of the limitations of designer's knowledge. To solve this problem, this paper proposed a product concept generation and evaluation method based on knowledge. Firstly, we adopted syntactic dependency parsing (SDP) to extract the main semantics structures from the existing product in the market. Secondly, word embedding was adopted for the vectorization of the main function structure, then a product base and a function base was constructed. Normally, every kind of product has its basic types of functions, so we used K-means to classify the functions. We then randomly choose a function from each cluster to combine a new product. As for the evaluation of each product, we calculated quantitatively the novelty, diversity and feasibility of solutions by semantic similarity. According to the comparison of the solutions and the existed products in novelty, diversity and feasibility. We can deduce that our methodology is feasible.
KW - function cluster
KW - product design
KW - similarity evaluation
KW - word embedding
UR - http://www.scopus.com/inward/record.url?scp=85066637364&partnerID=8YFLogxK
U2 - 10.1109/IEA.2019.8714972
DO - 10.1109/IEA.2019.8714972
M3 - Conference contribution
AN - SCOPUS:85066637364
T3 - 2019 IEEE 6th International Conference on Industrial Engineering and Applications, ICIEA 2019
SP - 871
EP - 876
BT - 2019 IEEE 6th International Conference on Industrial Engineering and Applications, ICIEA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2019
Y2 - 12 April 2019 through 15 April 2019
ER -