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Regarding product support from intelligent design, Younesi and Roghanian proposed a comprehensive quality function deployment for environment (QFDE), a fuzzy decision-making trial-and-evaluation laboratory (DEMATEL), and a fuzzy analytic network process (FANP) for sustainable product design in order to determine the best design standards for a specific product. Pitiot et al. studied a preliminary product design method based on a primitive evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). Costa et al. presented the product range model (PRM), which combines rule-based systems with CBR to provide product design decision support. Winkelman proposed an intelligent design directory that consists of a virtual design environment associated with standard component catalogues. Hahm et al. proposed a framework to search engineering documents that has fewer semantic ambiguities and a greater focus on individualized information needs. Akmal et al. proposed an ontology-based approach that can use feature-based similarity measures to determine the similarity between two classes. Morariu et al. proposed a classification of intelligent products from the perspective of integration, and introduced formalized data structures for intelligent products. Li et al. proposed a knowledge training method based on information systems, data mining, and extension theory (extenics), and designed a knowledge-management platform to improve the quality of decision-making. Diego-Mas and Alcaide-Marzal proposed a neural-network-based approach to simulate the consumers’ emotional responses for the form design of products, and developed a theoretical framework for the perceptions of individual users. Tran and Park proposed eight groups of 29 scoring criteria that can help designers and practitioners compare and select an appropriate methodology for designing a product service system (PSS). Kuo et al. used a depth-first search to create a predictive eco-design process. Andriankaja et al. proposed a complete PSS design framework to support integrated products and services design in the PSS context. Muto et al. proposed a task-management framework that enables manufacturers to develop various PSS options for their product-selling business. Ostrosi et al. proposed a proxy-based approach to assist with the configuration of products in conceptual design. Chan et al. proposed an intelligent fuzzy regression method to generate a model that represents the nonlinear and fuzzy relationship between emotional responses and design variables.
Challenges affecting knowledge push design include: establishing an instance-based product design method; utilizing the intelligent design method based on knowledge-based engineering (KBE); and developing a knowledge push using task-oriented requirements.
An electroencephalogram (EEG) measures and records the electrical activity of the brain, using biofeedback and the biological effects of an electromagnetic field. Special sensors called electrodes are attached to the head. Changes in the normal pattern of electrical activity can show certain conditions, such as an epiphany, imagination, and reasoning. Fig. 6 shows the intelligent design of machine tools utilizing an EEG for the purpose of intelligent design. Fig. 7 shows the graphical user interface (GUI) for measuring EEGs, and Fig. 8 shows the relative voltage of the EEG along with spectral analysis for a knowledge push.
Fig. An intelligent design for machine tools. DC: direct current. |
Fig.The GUI for measuring EEGs. |
Fig.(a) The relative voltage of the EEG and (b) spectral analysis for knowledge push. |
Existing intelligent design methods for customized products usually require the establishment of design rules and design templates in advance, and the use of knowledge matching to provide a design knowledge push and to enhance design intelligence.
It is still difficult to complete a design for customized products with individual requirements. The following technical difficulties still hinder the achievement of rapid and innovative design for complex customized equipment:
(1) It is difficult to adapt the excavation of requirements based on big data. In a big data environment, the data source of individual requirements is mainly information from pictures, video, motion, and unstructured data in the form of radio frequency identification (RFID), which is not limited to structured data. It is difficult to establish a matching and coordinated relation of individual requirements between heterogeneous and unstructured multi-source data, so the accuracy of the requirements data analysis is affected.
(2) It is difficult to achieve many individual design requirements, and to rapidly respond to and support the design innovation schemes of customized products for individual requirements. It is difficult to complete intelligent design that comes from different specialties and different subject backgrounds through swarm intelligence in order to develop the intelligence of public groups.
(3) It is difficult to master the inherent knowledge and experience of designers. Existing intelligent design needs push knowledge based on learning specialties, owned skills, and existing design experience.
We have researched intelligent design theory and the method and application of customized equipment for precise numerical control (NC) machine tools, a super-large cryogenic ASU, a PFHE, injection molding equipment, and low-voltage circuit breakers (CBs) .
In recent years, humankind has entered the big data era, with the development and application of technologies such as the Internet, cyber-physical systems (CPSs), and more. Based on an Internet platform, China’s Internet Plus initiative, which began in 2015, crossed borders and connected with all industries by using information and communication technology to create new products, new businesses, and new patterns.
Big data has changed the product design and manufacturing environment. These changes strongly influence the analysis of personalized requirements and methods of customized equipment design. The specific product performance is as follows.
Designing customized equipment is different from designing general products, as it usually reflects particular requirements from customers by order. This CR information usually shows non-regularity. The relationship among different orders is not strong, which leads to situations in which the type of product demand information is extremely mixed up and the amount of information is very great. With the development of e-commerce concepts, such as online-to-offline (O2O), business-to-customer (B2C), business-to-business (B2B), and so forth, a large amount of information on effective individual needs becomes hidden in big data. An essential question in product design is how to mine and transform individual requirements in order to design customized equipment with high efficiency and low cost.
Customized equipment design is usually based on mass production, which is further developed in order to satisfy the customers’ individual requirements. Modular recombination design and variant design are carried out for the base product and its composition modules, in accordance with the customers’ special requirements, and a new evolutionary design scheme that is furnished to provide options and evolve existing design schemes is adopted. An individual customized product is provided for the customers, and the organic combination of a mass product with a traditional customized design is achieved. In the Internet age, the design of customized equipment stems from the knowledge and experience of available integrated public groups, and is not limited to a single designer. In this way, the innovation of customized equipment is enhanced via swarm intelligence design. As a result, the Internet Plus environment has transformed the original technical authorization from a manufacturing enterprise interior or one-to-one design into a design mode that fuses variant design with swarm intelligence design.
