Abstract

The industrial product-service system (iPSS) is a kind of system engineering methodology, integration scheme, and business model to realize service value by adding intangible services in the whole life cycle. However, the design of the system involves many difficulties such as uncertain customer demands, strong subjectivity of the experience design, and long debugging times. Methods for solving upper problems are therefore essential. This paper presents a design model that integrates an improved affinity propagation (AP) clustering algorithm, quality function development (QFD), and axiomatic design (AD). The entire process of designing an iPSS can be split into three steps. First, uncertain customer demands is determined and standardized. Second, the functions of the product-service system are investigated. Finally, the structures of the system are determined. This paper examines the example of the control service of an iPSS for a water heater tank capping press. An improved AP clustering algorithm is used to determine standardized customer demands, the proposed QFD, and an AD integration model to initially establish a mapping between the customer demands domain and the function domain and clarify the design focus. Next, a QFD- and AD-integrated model is constructed to establish a mapping between the function domain and the structure domain and optimize the control scheme through the quality of its risk prediction. Finally the paper verifies that the upper process and methods can guide the design process effectively in production applications.

Highlights

  • Introduction e Made in China2025 policy lists intelligent manufacturing as one of China’s five key projects and presents a comprehensive plan to develop the intelligent manufacturing industry [1]

  • With the continuous development of intelligent manufacturing in practice, a variety of different themes have emerged. ese themes include the Internet of things [5], big data manufacturing [6], cloud manufacturing [7], and digital twin [8], which play a positive role in guiding the technological upgrading of the manufacturing industry

  • In view of the vagueness, dispersion, and uncertainty of customer demands obtained from interviews and questionnaires, this paper proposes an affinity propagation (AP) clustering algorithm based on a weighted network. e algorithm uses an intuitionistic fuzzy decision matrix to quantify the correlation between demand nodes, uses a weighted network to determine the distribution density of demand nodes, and determines the selection of initial deviation parameters P

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Summary

Cluster Analysis of Customer Demands

QFD and AD are driven by customer demands; the accuracy of these demands is very important. E second problem is that the initial P value is set manually, and the probability of each data point being a clustering center is the same, by default, which is obviously unreasonable Considering these two problems, this paper introduces a weighted network AP clustering algorithm for the cluster analysis of customer demands. E values of the initial deviation parameters for every demand are shown, where the maximum number of iterations is set to 500 and the Figure 4: Undirected weighted network model of customer demands.

Function Domain Determination for a Control Service System
Design requirements
Structure Domain Determination for the Control Service System
Design matrix
Construction of the Control Service System
Conclusion
Full Text
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