[1] W Beitz, K H Kuttner. Dubbel Handbook of Mechanical Engineering. Berlin: Springer, 1994.
[2] V Papanek. The future isn't what it used to be. Design Issues, 1988, 5(1): 4-17.
[3] Y B Xie. Modern design and knowledge acquisition. Chinese Journal of Mechanical Engineering, 1996(6): 36-41.
[4] A I Llewelyn. Review of CAD/CAM. Computer-Aided Design, 1989, 21(5): 297 308.
[5] D Ullman. The mechanical design process. New York: McGraw-Hill, 1992.
[6] G Gao, H Ning. The barchan-dune vortex flame stabilizer. China:CN85100305.2, 1989.(in Chinese)
[7] E Zhang. Modern design theory and method. Beijing: Science Press, 2007.
[8] G Pahl, W Beitz. Engineering design: a system process. London: Springer, 1994.
[9] T Schulz, K S Fugleruda, H Arfwedson, et al. A case study for universal design in the internet of things. International Conference on Universal Design(UD), Lund, Sweden, June 16-18, 2014: 45-54.
[10] F Loch, M Fahimipirehgalin, J N Czerniak, et al. An adaptive virtual training system based on universal design. IFAC-PapersOnLine, 2019, 51(34): 335-340.
[11] A P Zając. City Accessible for everyone – improving accessibility of public transport using the universal design concept. Transportation Research Procedia, 2016, 14: 1270-1276.
[12] N P Suh. Axiomatic design as a basic for universal design theory. Universal Design Theory, Aachen: Shaker Verlag, 1998: 3-24.
[13] J Shao, F M Lu, C H Zeng, et al. Research progress analysis of reliability design method based on axiomatic design theory. Procedia CIRP, 2016, 53: 107-112.
[14] A M Farid. Static resilience of large flexible engineering systems: axiomatic design model and measures. IEEE System Journal, 2017, 11(4): 2006-2017.
[15] V Souchkov. TRIZ: a systematic approach to conceptual design. Universal Design Theory. Aachen: Shaker Verlag, 1998: 223-234.
[16] M R M Asyraf, M R Ishak, S M Sapuan, et al. Conceptual design of creep testing rig for full-scale cross arm using TRIZ-Morphological chart-analytic network process technique. Journal of Materials Research and Technology, 2019, 10(1): 5647-5658.
[17] D Francia, G Caligiana, A Liverani, et al. PrinterCAD: A QFD and TRIZ integrated design solution for large size open molding manufacturing. International Journal on Interactive Design and Manufacturing, 2018, 12(1): 81-94.
[18] L Yang, S J Yi, X Mao, et al. Innovation design of fertilizing mechanism of seeder based on TRIZ theory. IFAC-PapersOnLine, 2018, 51(17): 141-145.
[19] T Tomiyama. General design theory and its extension and application. Universal Design Theory. Aachen: Shaker Verlag, 1998: 25-44.
[20] H Komoto. Categorical formulation of mathematical design theories applied to system design process analysis. CIRP Annals, 2019, 68(1): 157-160.
[21] W Cheng, H L Zhang, S Fu, et al. A process-performance coupled design method for hot-stamped tailor rolled blank structure. Thin-Walled Structures, 2019, 140: 132-143.
[22] S Ren, Y F Zhang, B B Huang. New pattern of lifecycle big-data-driven smart manufacturing service for complex product. Journal of Mechanical Engineering, 2018, 54(22): 194-203.(in Chinese)
[23] M Rahman, C Schimpf, C Xie, et al. A CAD-based research platform for data-driven design thinking studies. Journal of Mechanical Design, 2019: 1.
[24] D Ghosh, A Olewnik, K Lewis, et al. Cyber-empathic design: a data-driven framework for product design. Journal of Mechanical Design, 2017, 139(9): 091401.
[25] Y Z Kan, D Y Sun, Y Luo, et al. Optimal design of the gear ratio of a power reflux hydraulic transmission system based on data mining. Mechanism and Machine Theory, 2019, 142: 103600.
[26] M Zhang, G X Li, J Z Gong, et al. A hierarchical functional solving framework with hybrid mappings for supporting the design process in the conceptual phase. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2012, 226(8): 1401-1415.
[27] Y Chen, Z L Liu, Y B Xie. A knowledge-based framework for creative conceptual design of multi-disciplinary systems. Computer-Aided Design, 2012, 44(2): 146-153.
[28] Y W Huang, Z H Jiang, C N He, et al. An inner-enterprise wiki system integrated with semantic search for reuse of lesson-learned knowledge in product design. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2016, 230(3): 548-561.
[29] D Wu, E Coatanea, G G Wang. Employing knowledge on causal relationship to assist multidisciplinary design optimization. Journal of Mechanical Design, 2019, 141(4): 41402.
[30] J X Luo, B Yan, K Wood. InnoGPS for data-driven exploration of design opportunities and directions: the case of google driverless car project. Journal of Mechanical Design, 2017, 139(11): 111416.
[31] R X Ning, J H Liu, C T Tang. Modeling and simulation technology in digital manufacturing. Journal of Mechanical Engineering, 2006, 42(7): 132-137. (in Chinese)
[32] O A Turkkan, V K Venkiteswaran, H Su. Rapid conceptual design and analysis of spatial flexure mechanisms. Mechanism and Machine Theory, 2018, 121: 650-668.
[33] S Arastehfar, Y Liu, W F Lu. An evaluation methodology for design concept communication using digital prototypes. Journal of Mechanical Design, 2016,138(3): 031103.
[34] H Song, F Y Chen, Q J Peng, et al. Improvement of user experience using virtual reality in open-architecture product design. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2017, 232(13): 2264-2275.
[35] T Robinson, I Friel, C G Armstrong, et al. Computer-aided design model parameterization to derive knowledge useful for manufacturing design decisions. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2016, 232(4): 621-628.
[36] H Ai, L P Chen, Y M Li. Product configuration design method based on performance simulation. China Mechanical Engineering, 2011, 22(7): 853-859. (in Chinese)
[37] M Grieves. Digital twin: Manufacturing excellence through virtual factory replication. White Paper, 2014: 1-7.
[38] M Shafto, M Conroy, R Doyle, et al. Draft modeling, simulation, information technology & processing roadmap. Technology Area, 2010: 11.
[39] R Söderberg, K Wärmefjord, J S Carlson, et al. Toward a digital twin for real-time geometry assurance in individualized production. CIRP Annals-Manufacturing Technology, 2017, 66: 137-140.
[40] F Tao, J F Cheng, Q L Qi, et al. Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 2018, 94(9-12): 3563-3576.
[41] J F Liu, H G Zhou, X J Liu, et al. Dynamic evaluation method of machining process planning based on digital twin. IEEE Access, 2019, 7: 19312-19323.
[42] B Schleich, N Anwer, L Mathieu, et al. Shaping the digital twin for design and production engineering. CIRP Annals, 2017, 66(1): 141-144.
[43] Y Wang, D Y Mo, M M Tseng. Mapping customer needs to design parameters in the front end of product design by applying deep learning. CIRP Annals, 2018, 67(1): 145–148.
[44] H E Murat, B Marco, K Mario, et al. Mapping customer needs to engineering characteristics: an aerospace perspective for conceptual design. Journal of Engineering Design, 2014, 25(1-3): 64-87.
[45] Q Guo, C Q Xue, M J Yu, et al. A new user implicit requirements process method oriented to product design. Journal of Computing and Information Science in Engineering, 2019, 19(1): 011010.
[46] T AlGeddawy, H ElMaraghy. Optimum granularity level of modular product design architecture. CIRP Annals, 2013, 62(1): 151–154.
[47] B M Li , S Q Xie. Module partition for 3D CAD assembly models: a hierarchical clustering method based on component dependencies. International Journal of Production Research, 2015, 53(17): 5224-5240.
[48] X Xu, W Zhang, X Ding. Modular design method for filament winding process equipment based on GGA and NSGA-II. International Journal of Advanced Manufacturing Technology, 2017, 94(5–8): 2057–2076.
[49] L Jing, Y F Nie, X W Zhang, et al. A framework method of user-participation configuration design for complex products. Procedia CIRP, 2018, 70: 451-456.
[50] C Zheng, X S Qin, B Eynard, et al. Interface model-based configuration design of mechatronic systems for industrial manufacturing applications. Robotics and Computer-Integrated Manufacturing, 2019, 59: 373–384.
[51] W Wei, W H Fan, Z K Li. Multi-objective optimization and evaluation method of modular product configuration design scheme. The International Journal of Advanced Manufacturing Technology, 2014, 75(9-12): 1527-1536.
[52] T Kermavnar, A Shannon, L W O’Sullivan. The application of additive manufacturing / 3D printing in ergonomic aspects of product design: A systematic review. Applied Ergonomics, 2021, 97: 103528.
[53] I Yadroitsev, P Krakhmalev, I Yadroitsava. Hierarchical design principles of selective laser melting for high quality metallic objects. Additive Manufacturing, 2015, 7: 45-56.
[54] N Ahsan, B Khoda. AM optimization framework for part and process attributes through geometric analysis. Additive Manufacturing, 2016, 11: 85-96.
[55] N Siraskar, R Paul, Anand S. Adaptive slicing in additive manufacturing process using a modified boundary octree data structure. Journal of Manufacturing Science and Engineering, 2015, 137(1): 011007.
[56] C Tian, Y Wan, X Li, et al. Pore morphology design and grinding performance evaluation of porous grinding wheel made by additive manufacturing. Journal of Manufacturing Processes, 2022, 79:1-10.
[57] T Primo, M Calabrese, A D Prete, et al. Additive manufacturing integration with topology optimization methodology for innovative product design. International Journal of Advanced Manufacturing Technology, 2017, 93(1-4): 467-479.
[58] A M Mirzendehdel, K Suresh. Support structure constrained topology optimization for additive manufacturing. Computer-Aided Design, 2016, 81: 1-13.
[59] J H K Haertel, G F Nellis. A fully developed flow thermofluid model for topology optimization of 3D-printed air-cooled heat exchangers. Applied Thermal Engineering, 2017, 119: 10-24.
[60] Zegard, Tomás, G H Paulino. Bridging topology optimization and additive manufacturing. Structural and Multidisciplinary Optimization, 2016, 53(1): 175-192.
[61] K Yang, Y B Li, L L Zhou, et al. Energy efficient foot trajectory of trot Motion for hydraulic quadruped robot. Energies, 2019, 12(13): 2514.
[62] F Lu, G Zhou, Y Liu, et al. Ensemble transfer learning for cutting energy consumption prediction of aviation parts towards green manufacturing. Journal of Cleaner Production, 2022, 331: 129920.
[63] Y Seow, S Rahimifard, E Woolley. Simulation of energy consumption in the manufacture of a product. International Journal of Computer Integrated Manufacturing, 2013, 26(7): 663-680.
[64] C Z Guo, M A AL-Shudeifat, A F Vakakis, et al. Vibration reduction in unbalanced hollow rotor systems with nonlinear energy sinks. Nonlinear Dynamics, 2015, 79(1): 527–538.
[65] J W Jung, S H Lee, G H Lee, et al. Reduction design of vibration and noise in IPMSM type integrated starter and generator for HEV. IEEE Transactions on Magnetics, 2010, 46(6): 2454-2457.
[66] L Zoghaib, P O Mattei. Modeling and optimization of local constraint elastomer treatments for vibration and noise reduction. Journal of Sound and Vibration, 2014, 333(26): 7109-7124.
[67] J H Zhang, S Q Xia, S G Ye, et al. Experimental investigation on the noise reduction of an axial piston pump using free-layer damping material treatment. Applied Acoustics, 2018, 139: 1-7.
[68] Y S Yang, G Yuan, Q W Zhuang, et al. Multi-objective low-carbon disassembly line balancing for agricultural machinery using MDFOA and fuzzy AHP. Journal of Cleaner Production, 2019, 233: 1465-1474.
[69] Q Cheng, Y L Guo, P H Gu, et al. A new modularization method of heavy-duty machine tool for green remanufacturing. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2018, 232(23): 4237–4254.
[70] C Ke, Z Jiang, H Zhang, et al. An intelligent design for remanufacturing method based on vector space model and case-based reasoning. Journal of Manufacturing Processes, 2020, 277: 123269.
[71] Y Umeda, N Miyaji, Y Shiraishi, et al. Proposal of a design method for semi-destructive disassembly with split lines. CIRP Annals-Manufacturing Technology, 2015, 64(1): 29-32.
[72] G D Tian, Y M Liu, Q Z Tian, et al. Evaluation model and algorithm of product disassembly process with stochastic feature. Clean Technologies and Environmental Policy, 2012, 14(2): 345-356.
[73] N Gan, Q Wang. Topology optimization design of improved response surface method for time-variant reliability. Advances in Engineering Software, 2020, 146: 102828.
[74] A M Hasofer, N C Lind. Exact and invariant second-moment code format. Journal of the Engineering Mechanics division, 1974, 100(1): 111-121.
[75] J Tu, K K Choi, Y H Park. A new study on reliability-based design optimization. Journal of Mechanical Design, 1999, 121(4): 557-564.
[76] S Mahadevan, A Haldar. Probability, reliability and statistical method in engineering design. New York: John Wiley & Sons, 2000.
[77] L K Song, G C Bai, X Q Li, et al. A unified fatigue reliability-based design optimization framework for aircraft turbine disk. International Journal of Fatigue. 2021, 152: 106422.
[78] M Hohenbichler, R Rackwitz. Improvement of second-order reliability estimates by importance sampling. Journal of Engineering Mechanics, 1988, 114(12): 2195-2199.
[79] K Breitung. Asymptotic approximations for multinormal integrals. Journal of Engineering Mechanics, 1984, 110(3): 357-366.
[80] H An, B D Youn, H S Kim. Reliability-based design optimization of laminated composite structures under delamination and material property uncertainties. International Journal of Mechanical Sciences. 2021, 205: 106561.
[81] S Rahman, D Wei. A univariate approximation at most probable point for higher-order reliability analysis. International Journal of Solids and Structures, 2006, 43(9): 2820-2839.
[82] I Lee, K K Choi, D Gorsich. System reliability-based design optimization using the MPP-based dimension reduction method. Structural and Multidisciplinary Optimization, 2010, 41(6): 823-839.
[83] C W Fei, H Li, C Lu, et al. Vectorial surrogate modeling method for multi-objective reliability design. Applied Mathematical Modelling, 2022, 109: 1–20.
[84] W Hu, K K Choi, O Zhupanska, et al. Integrating variable wind load, aerodynamic, and structural analyses towards accurate fatigue life prediction in composite wind turbine blades. Structural and Multidisciplinary Optimization, 2016, 53(3): 375-394.
[85] W Hu, K K Choi, H Cho. Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty. Structural and Multidisciplinary Optimization, 2016, 54(4): 953-970.
[86] I Lee, K K Choi, L Zhao. Sampling-based RBDO using the stochastic sensitivity analysis and Dynamic Kriging method. Structural and Multidisciplinary Optimization, 2011, 44(3): 299-317.
[87] X Peng, D H Li, H P Wu, et al. Uncertainty analysis of composite laminated plate with data-driven polynomial chaos expansion method under insufficient input data of uncertain parameters. Composite Structures, 2019, 209: 625-633.
[88] X Peng, Z Y Liu, X Q Xu, et al. Nonparametric uncertainty representation method with different insufficient data from two sources. Structural and Multidisciplinary Optimization, 2018, 58(5): 1947-1960.
[89] X Peng, T J Wu, J Q Li, et al. Hybrid reliability analysis with uncertain statistical variables, sparse variables and interval variables. Engineering Optimization, 2018, 50(8): 1347-1363.
[90] X Peng, Y L Guo, C Qiu, et al. Reliability optimization design for composite laminated plate considering multiple types of uncertain parameters. Engineering Optimization, 2020, 53(2): 221-236.
[91] C Jiang, X P Wei, Z L Huang, et al. An outcrossing rate model and its efficient calculation for time-dependent system reliability analysis. Journal of Mechanical Design, 2017, 139(4): 041402.
[92] C Jiang, X P Huang, X Han, et al. Time-dependent Structural Reliability Analysis Method with Interval Uncertainty. Journal of Mechanical Engineering, 2013, 49(10): 186-193.
[93] L Wang, X J Wang, D Wu, et al. Structural optimization oriented time-dependent reliability methodology under static and dynamic uncertainties. Structural and Multidisciplinary Optimization, 2018, 57(4): 1533-1551.
[94] T Fang, C Jiang, Z L Huang, et al. Time-variant reliability-based design optimization using an equivalent most probable point. IEEE Transactions on Reliability, 2018, 68(1): 175-186.
[95] M Y Li, G X Bai, Z Q Wang. Time-variant reliability-based design optimization using sequential kriging modeling. Structural and Multidisciplinary Optimization, 2018, 58(3): 1051-1065.
[96] W Hu, X Wang, Y Wang, et al. A computational model of wind turbine blade erosion induced by raindrop impact. NAWEA WindTech 2019 Conference, Amherst, USA, October 14-16, 2019.