Next Article in Journal
Thermally Activated Delayed Fluorescence Emitters for Deep Blue Organic Light Emitting Diodes: A Review of Recent Advances
Previous Article in Journal
Matrix-Assisted Laser Desorption Ionization Mass Spectrometry of Compounds Containing Carboxyl Groups Using CdTe and CuO Nanoparticles
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(4), 493;

A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution

1,2,* , 1
Key Laboratory of Advanced Manufacturing Technology (Guizhou University), Ministry of Education, Guiyang 550025, China
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
Authors to whom correspondence should be addressed.
Received: 5 February 2018 / Revised: 19 March 2018 / Accepted: 22 March 2018 / Published: 26 March 2018
Full-Text   |   PDF [5251 KB, uploaded 3 May 2018]   |  


Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process. View Full-Text
Keywords: product function evolution; data analysis; Bayesian network; adaptability design product function evolution; data analysis; Bayesian network; adaptability design

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Li, S.; Wu, Y.; Xu, Y.; Hu, J.; Hu, J. A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution. Appl. Sci. 2018, 8, 493.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top