Guoxi Li

A hierarchical functional solving framework with hybrid mappings for supporting the design process in the conceptual phase

Inheritance and innovation play important roles in the conceptual design phase. Inheritance can achieve rapid design by reusing existing design knowledge, while innovation can realize innovative design by developing creative ideas and concepts. Thus, an ideal conceptual design model should administer to the implementation of inheritance and innovation. Considering rational design domains and mapping relations is beneficial to knowledge representation and inheritance. According with the thinking process of humans it is propitious to human intervention and innovation. To meet these requirements, this article proposes a hierarchical functional solving framework with hybrid mappings. In this framework, there are four design domains including function, working principle, behaviour and structure. The reasoning process of product function design consists of four mapping patterns, which are solving mapping pattern, reformulation mapping pattern, decomposition mapping pattern and derivative mapping pattern, totalling 15 basic mapping relations. The evolutionary logic of these mapping relations is developed in this article. In addition, the flow of the solving process from each design domain is established. The procedure in applying the proposed framework to the conceptual design is presented subsequently. Moreover, several embodiment strategies ensuring the effectiveness of identified design solutions are also proposed, including performance match analysis, behavioural compatibility analysis and structural compatibility analysis. Finally, a design case of the telescopic shuttle mechanism of an automatic storage/retrieval machine is studied to illustrate and demonstrate the feasibility of the proposed framework.

An improved algorithm for the normal contact stiffness and damping of a mechanical joint surface

The contact stiffness and damping play an important role in determining the dynamic characteristics at various mechanical joint surfaces in an assembly body. In this article, a bi-fractal model is proposed to overcome the drawbacks of the G-W and M-B models, which only considered the contacts that have the same fractal parameters for the joint surfaces. A surface impact coefficient is also introduced to calculate the real contact area from the microscopic and macroscopic perspectives, which can be used to obtain the contact stiffness and damping of rough surfaces by applying the contact formula between rough planes. In addition, the contact body is approximated to a number of asperities stacked on a base, similar to a series structure of two springs. The series structure can reasonably account for the limitations of the contact stiffness and damping at joint surfaces. With these improvements, this study establishes a more precise joint surface model determining the stiffness–load and damping–load relationships. An experiment is performed to compare the contact stiffness and damping determined from this theory with the experiment results. The theoretical values correspond well with the experimental values. The contact stiffness increases non-linearly with the load; the increase is initially quick and then slows down and stabilises. A value of D = 1.42 is obtained as an estimated critical value which determined the contact damping increase or decrease with load. The model can predict the trend in the contact stiffness and damping based on the loading to better prepare for a dynamic assembly analysis.

Integrating grey relational analysis and support vector machine for performance prediction of modular configured products

Evaluating whether a newly configured product can satisfy the customers’ individual requirements or not is crucially important for the modular configuration design. Product performance prediction at the end of the configuration process can estimate the performance parameter values through the soft computing method instead of practical test experiments, which enables fast and accurate evaluation of configuration schemes. In this article, we propose a novel prediction approach based on the integration of grey relational analysis and support vector machine through discovering the knowledge from the historical configuration information. The implementation process of the prediction is established, and the procedure in applying the prediction to the configuration design is presented. There are three key steps to achieve performance prediction. First, the module parameters that affect the performance need to be reduced using the grey relational analysis method and then a module parameter reduction is generated. Second, the relationship between the reduced module parameters and the performance parameter is mined from the limited existing product data. A support vector machine model used for regression prediction is constituted. Third, when the values of the module parameter reduction are determined, the performance value of a newly configured product can be predicted by means of the support vector machine model. This methodology can ensure the performance prediction executed in a short period of time with a high degree of precision, even under the small-sample conditions. A design case of the plate electrostatic precipitator is studied to illustrate and demonstrate the feasibility of the proposed method.

Assembly Data Mining Platform Based on Python

There are rich data in the manufacturing information systems, but they are not utilized in an effective way. The establishement of assembly data mining platform is to take advantage of the data to improve assembly process. Assembly factors and assembly data mining templates are the core tables in the assembly database. The methods of dealing with data vacancies and noise points are pre-set in the data mining templates. Python is used as the data mining engine by script customization and algorithm library encapsulation. Firstly, Python algorithm scripts are customized when programing the platform. Then the platform generates the execution script according to the user operation. Finally, the main program of Python executes the generated script and returns the results. Also, Mlpy is applied to make corresponding algorithm processing module. The functions are pre-compiled so that the assembly technicians without knowledge of data mining can utilize the assembly data to predict the assembly performance and analyse the assembly process.