In this paper, an intelligent-design method to deal with conceptual optimization is proposed for the decisive impact of the concept on the product-development cycle cost and performance. On the basis of matter-element analysis, an effective functional-structure combination model satisfying multiple constraints is first established, which maps the product characteristics obtained by expert research and customer-requirements analysis of the function and structure domain. Then, the Evolutionary Game Algorithm (EGA) was utilized to solve the model, in which a strategy-combination space is mapped to the solution-search space of the conceptual-solution problem, and the game-utility function is mapped to the objective functions of concept evaluation. Constant disturbance and Best-Response Correspondence were applied cross-repeatedly until the optimal equilibrium Pareto state corresponding to the global optimal solution was obtained. Finally, the method was simulated on MATLAB 8.3 and applied to the design for fixed winch hoist, which greatly shortens its design cycle.

Tool selection is a multi-criteria decision-making problem in the presence of various selection criteria and a set of alternatives, but previous works are limited to evaluating the tools within the workshop tool library. To intelligently select proper inserts across suppliers under the Internet environment, an insert data format based on ISO 513 was established, and a framework was then designed to obtain a set of alternatives from different suppliers based on fuzzy intervals. Then, knowledge was described with convenient language and the simple membership function to build an intelligent system, which would infer the matching degree of insert characteristics to the machining conditions. Furthermore, analytic hierarchy process was applied to sort the alternatives. Finally, the case study shows that compared with previous works and machinists, this work not only obtains a set of alternatives from all suppliers who uploaded their product data with the designed format but comprehensively evaluates the insert (take finishing low-carbon steel as an example, both cemented carbide and cermet are recommended, the nose radius reduces 25%, the environmental index increases 25%, while the rake reduces 11.25%, when compared with machinists who tend to select the larger rake angle foe finishing). A platform was also developed based on Visual Studio 2015 and SQL Server 2012 to improve selection efficiency for inexperienced CNC operators, purchasers, and vendors.

A product is composed of several components, and the number, type, and combination of components plays a crucial role in the process of product design. It is difficult to get an optimized scheme in a short time. In order to improve the efficiency of product design, a product design scheme recommendation algorithm based on multi-way tree and learning-to-rank is proposed. Firstly, the product solution model, whose nodes are obtained by mapping the product attributes, is generated according to the design process, and the alternative scheme is obtained by traversing the multi-tree model. Secondly, considering users’ cognition of the importance of each product attribute, the analytic hierarchy process (AHP) is applied to assign weight to the product attribute, and then similarity to ideal solution (TOPSIS) method based on AHP is used to rank alternative solutions. Furthermore, according to users’ preference for parts’ supplier information, the learning-to-rank algorithm is used to optimize the list of alternative schemes twice. Finally, taking the design of the hoist as an example, it was verified that the proposed method had higher efficiency and better recommendation effect than the traditional parametric design method.

The adaptation of cutting tools is the key link of machining, although a lot of work has been done to achieve tool selection for single features, relatively little research on tool scheme adaptation for multi-feature parts as a whole. In order to take into account the factors of matching, efficiency, cost, manufacturability, and environment, a comprehensive optimization method of a multi-feature tool scheme is proposed in this work. Firstly, the critical tool of a complex feature is defined, and a tool combination method with complex single features is proposed to generate an efficient tool strategy. Secondly, a tool scheme optimization model of multi-feature parts is constructed, where each machining feature is mapped to a game player in the model space, and the set of available tools is mapped to the player strategies. In addition, the classical evolutionary game algorithm is improved to adapt to the model, according to the large difference in the number and type of features of parts. Finally, a square test piece is taken as an example to verify the proposed method, and the results show that the method presented in the paper can efficiently obtain the customer-preferred tool scheme.

The adaptation of cutting tools is the key link in machining. Although a large amount of work has been done to achieve tool selection for a single feature, there is relatively little research on tool scheme adaptation for multi-feature parts. Considering the factors of matching, efficiency, cost, and environment, a comprehensive optimization method for a multi-feature part tool scheme is proposed in this work. First, the critical tool of a complex feature is defined, and a tool combination method with complex single features is proposed to generate an efficient tool strategy. Then, a tool scheme optimization model for multi-feature parts is constructed in which each machining feature is mapped to a game player in the model space, and the set of available tools is mapped to the player strategies. In addition, the classical evolutionary game algorithm is improved to adapt to the model, according to the difference in the features number and type of different parts. Finally, a square test piece is taken as a case to verify the proposed method. The result shows that the method presented in this work can efficiently obtain the customer-preferred tool scheme for the part.