In the conceptual design phase of eco-products, the evaluation of products eco-design is often considered by designers after determining the design scheme. In order to improve the eco-design efficiency, a CAD-based feedback knowledge push method was proposed to meet the designer’s eco-design knowledge requirements. Combine with the designer knowledge background, classified eco-design knowledge, and decomposed design tasks, eco-design knowledge requirements of designers can be obtained. Based on the cosine similarity algorithm of the knowledge vector space model (VSM), the knowledge requirements of the designer and the knowledge in the knowledge database are matched, and the eco-knowledge is actively pushed to the designer. The feedback and evaluation of designers on the initial push of knowledge was recorded by the system. When the designer is assigned relevant eco-design tasks again, the system conducts secondary filtering of eco-design knowledge through previous feedback records and pushes the filtered knowledge to designers, so as to achieve accurate feedback knowledge push. A CAD-based eco-design knowledge push prototype system for automotive products is developed. The eco-design of the front-end module of the automotive is used as an example to verify the effectiveness of the above method.
AdomaviciusGSankaranarayananRSenS, et al. (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems23: 103–145.
2.
Ammar-KhodjaSPerryNBernardA (2008) Processing knowledge to support knowledge-based engineering systems specification. Concurrent engineering16: 89–101.
3.
BaxterDRoyRDoultsinouA, et al. (2009) A knowledge management framework to support product-service systems design. International Journal of Computer Integrated Manufacturing22: 1073–1088.
4.
CalvertP (2005) Encyclopedia of database technologies and applications. Online Information Review29: 687–688.
5.
ChanNNGaaloulWTataS (2012) A recommender system based on historical usage data for Web service discovery. Service Oriented Computing and Applications6: 51–63.
6.
ChenDKDingJJGaoMZ, et al. (2016) Form gene clustering method about pan-ethnic-group products based on emotional semantic. Chinese Journal of Mechanical Engineering29: 1134–1144.
7.
ChenYJChenYMWuMS (2010) Development of an ontology-based expert recommendation system for product empirical knowledge consultation. Concurrent Engineering18: 233–253.
8.
DongRHO’MahonyMPSchaalM, et al. (2016) Combining similarity and sentiment in opinion mining for product recommendation. Journal of Intelligent Information Systems46: 285–312.
9.
HawalahAFasliM (2015) Dynamic user profiles for web personalisation. Expert Systems with Application42: 2547–2569.
10.
InoueMNahmYEOkawaS, et al. (2010) Design support system by combination of 3D-CAD and CAE with preference set-based design method. Concurrent Engineering18: 41–53.
11.
InoueMNahmYETanakaK, et al. (2013) Collaborative engineering among designers with different preferences: Application of the preference set-based design to the design problem of an automotive front-side frame. Concurrent Engineering21: 252–267.
12.
Iniguez-JarrinCPanachJILopezOP (2018) Defining interaction design patterns to extract knowledge from big data. Lecture Notes in Computer Science10816: 490–504.
13.
JungJSChoiISongMS (2007) An integration architecture for knowledge management systems and business process management systems. Computers in Industry58: 21–34.
14.
LiXRYuSHChuJJ, et al. (2017) Double push strategy of knowledge for product design based on complex network theory. Discrete Dynamics in Nature and Society2017: 1–15.
15.
LiuHWLiYChenJ, et al. (2017) A new design knowledge retrieval model based on granularity and clustering theories. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science231: 1540–1559.
16.
LiuLFLiYMaJL (2016) Application of TRIZ based on knowledge granularity in innovative design. Chinese Journal of Mechanical Engineering52: 22–32.
17.
LuQBGuoFP (2019) Personalized information recommendation model based on context contribution and item correlation. Measurement142: 30–39.
18.
NilashiMbin IbrahimOIthninN (2014) Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems60: 82–101.
19.
PetiotJFYannouB (2004) Measuring consumer perceptions for a better comprehension, specification and assessment of product semantics. International Journal of Industrial Ergonomics33: 507–525.
20.
PrasadB (2014) Knowledge-based enterprising (KBE) strategy for lean product development (LPD). In: Keynote talk at the 27th international conference on CAD/CAM, robotics and factories of the future conference (CARS & FOF 2014), London, UK, 22–24 July 2014.
21.
PrasadBRogersJ (2005) A knowledge-based system engineering process for obtaining engineering design solutions. In: Proceedings of the ASME international design engineering technical conferences and computers and information in engineering conference, Long Beach: ASME, 24–28 September 2005, vol. 3, pts A and B, pp.477–488.
22.
ShintreNPrasadB (2011). Effective use of knowledgeware for managing model size and improving performance. Catia Opeartors Exchange – An Electronic Publication – Tips and Techniques84: 3–6.
23.
SungRRitchieJMReaHJ, et al. (2011) Automated design knowledge capture and representation in single-user CAD environments. Journal of Engineering Design22: 487–503.
24.
TewariASBarmanAG (2018) Sequencing of items in personalized recommendations using multiple recommendation techniques. Expert Systems with Application97: 70–82.
25.
WangZSTianLWuYH, et al. (2016) Personalized knowledge push system based on design intent and user interest. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science230: 1757–1772.
26.
XuXWGallowayR (2005) Using behavioral modeling technology to capture designer’s intent. Computers in Human Behavior21: 395–405.
27.
XuYHYinGFNieY, et al. (2013) Research on an active knowledge push service based on collaborative intent capture. Journal of Network and Computer Applications36: 1418–1430.
28.
YuHLiZY (2010) Collaborative filtering recommendation algorithm based on forgetting curve. Journal of Nanjing University: Natural Science Edition46: 520–527.
29.
ZhangCHuangHHZhangL, et al. (2019a) Semi-quantitative method for task planning in product eco-design. International Journal of Production Research57: 2263–2280.
30.
ZhangKZhaoWWangJ, et al. (2019b) Knowledge push technology based on quality function knowledge deployment. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science233: 1119–1138.
31.
ZhangSYGuYLiuXJ, et al. (2018) A knowledge push technology based on applicable probability matching and multidimensional context driving. Frontiers of Information Technology & Electronic Engineering19: 235–245.
