A Research Review on the Key Technologies of Intelligent Design for Customized Products

The development of technologies such as big data and cyber-physical systems （CPSs） has increased the demand for product design. Product digital design involves completing the product design process using advanced digital technologies such as geometry modeling, kinematic and dynamic simulation, multi- disciplinary coupling, virtual assembly, virtual reality （VR）, multi-objective optimization （MOO）, and human-computer interaction. The key technologies of intelligent design for customized products include： a description and analysis of customer requirements （CRs）, product family design （PFD） for the customer base, configuration and modular design for customized products, variant design for customized products, and a knowledge push for product intelligent design. The development trends in intelligent design for customized products include big-data-driven intelligent design technology for customized products and customized design tools and applications. The proposed method is verified by the design of precision computer numerical control （CNC） machine tools.

Multi-objective quality control method for cold-rolled products oriented to customized requirements

To deal with the increasing demand for low-volume customization of the mechanical properties of cold-rolled products, a two-way control method based on mechanical property prediction and process parameter optimization(PPO) has become an effective solution. Aiming at the multi-objective quality control problem of a company's cold-rolled products, based on industrial production data, we proposed a process parameter design and optimization method that combined multi-objective quality prediction and PPO. This method used the multi-output support vector regression(MSVR) method to simultaneously predict multiple quality indices. The MSVR prediction model was used as the effect verification model of the PPO results. It performed multi-process parameter collaborative design and realized the optimization of production process parameters for customized multi-objective quality requirements. The experimental results showed that, compared with the traditional single-objective quality prediction model based on support vector regression(SVR), the multi-objective prediction model could better take into account the coupling effect between process parameters and quality index, the MSVR model prediction accuracy was higher than that of the SVR, and the optimized process parameters were more capable and reflected the influence of metallurgical mechanism on the quality index,which were more in line with actual production process requirements.

DESIGN ATTRIBUTES OPTIMIZATION MODEL FOR CUSTOMIZED PRODUCT AND ITS APPLICATION

By formulating the design of customized product as a multi-objectiveoptimization problem, a method for designing customized product according to the relative importanceof customer needs is proposed. This method searches for the optimal design that maximizes customersatisfaction by establishing mapping from design attributes to the degree of customer satisfactionon-each customer need. A window product is taken as an example for case study. The result indicatesthat this method is feasible.

REQUIREMENT PRODUCT CONFIGURATION IN MASS CUSTOMIZATION

On the basis of researching on requirement product configuration in mass customization, the concept of product family requirement class （PFRC） and requirement-matching template are put forward. A case-based requirement product configuration （CB-RPC） model and corresponding requirement product model are established. The result of requirement product configuration is obtained by using the method of two-level similar matching. In addition, the effect of the method on requirement responding is analyzed. Finally, the model and the method given are applied in elevator industry, and have improved the enterorise＇s ability of rapid responding to customer＇s reouirements.

A Framework for Implementation of Green Manufacturing in Customized Products Manufacturing Enterprises

An increase in public environmental awareness and pressures from the government’s determination to deal with environmental problems force manufacturers to implement green manufacturing(GM).However,since managers and stakeholders lack understanding of GM and its complexity,the manufacturing enterprise,especially the small and medium ones,are constantly facing the problems of how to make a reasonable decision for an environmental problem,and the adopted approaches have no clear payoffs.The customized design,flexible production,and diversified services in customized production make the problems more challenging.In order to solve this problem,this paper proposes a framework for the implementation of GM in customized products manufacturing enterprises(CPME).A three-layer framework,i.e.,the goal layer,the product life-cycle layer and the supporting layer,is designed to provide a methodology to help implement GM.In this model,the GM practice processes are divided into four stages from the life-cycle perspective,i.e.design,production,use,and disposal.The preliminary practice of GM in an electrical product manufacturing company is carried out,and the implementation effect shows that the system framework is helpful to make a comprehensive understanding of GM and to improve the operability of GM practices.The integrated product model is an effective way to integrate the life cycle data.

Construct Method of Predicting Satisfaction Model Based on Technical Characteristics

In order to construct objective relatively mapping relationship model between customer requirements and product technical characteristics, a novel approach based on customer satisfactions information digging from case products and satisfaction information of expert technical characteristics was put forward in this paper. Technical characteristics evaluation values were expressed by rough number, and technical characteristics target sequence was determined on the basis of efficiency, cost type and middle type in this method. Use each calculated satisfactions of customers and technical characteristics as input and output elements to construct BP network model. And we use MATLAB software to simulate this BP network model based on the case of electric bicycles.

Industry 4.0： a way from mass customization to mass personalization production

Although mass customization, which utilizes modularization to simultaneously increase product variety and maintain mass production （MP） efficiency, has become a trend in recent times, there are some limitations to mass customization. Firstly, customers do not participate wholeheartedly in the design phase. Secondly, potential combinations are predetermined by designers. Thirdly, the concept of mass customization is not necessary to satisfy individual requirements and is not capable of providing personalized services and goods. Industry 4.0 is a collec- tive term for technologies and concepts of value chain organization. Based on the technological concepts of radio frequency identification, cyber-physical system, the Inter- net of things, Internet of service, and data mining, Industry 4.0 will enable novel forms of personalization. Direct customer input to design will enable companies to increasingly produce customized products with shorter cycle-times and lower costs than those associated with standardization and MP. The producer and the customer will share in the new value created. To overcome the gaps between mass customization and mass personalization, this paper presents a framework for mass personalization production based on the concepts of Industry 4.0. Several industrial practices and a lab demonstration show how we can realize mass personalization.