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New-generation artificial intelligence boosts product forward design capability in manufacturing industry

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  • Corresponding author: egi@zju.edu.cn (J.T.)
  • Product forward design is generally posited as an essential pathway for surmounting bottlenecks in technological innovation and augmenting the core capabilities of the manufacturing industry. Bolstered by knowledge engineering, new-generation intelligent products are distinguished by the salient attributes of self-sensing, self-adaptation, self-learning, and self-decision-making. These advancements are propelling mechanical design to evolve from a traditional paradigm rooted in single-discipline expertise, empirical heuristics, and function-satisfaction orientation, toward a sophisticated framework that features multidisciplinary coupling, collaborative architecture-knowledge driving, and integrated lifecycle-wide optimization—from which defining characteristics including innovativeness, customizability, intelligence, virtuality, sustainability, optimality, and human-machine collaboration distinctly emerge. This perspective hereby explores the conceptual essence of product forward design, elaborates on the enabling role of frontier artificial intelligence technologies thereof, and underscores their typical application scenarios in high-end equipment, where these intelligent capabilities substantively drive breakthroughs in performance and adaptive functionality.
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  • [1] Xu J., Chen X., Zhang S., et al. (2017). Thermal design of large plate-fin heat exchanger for cryogenic air separation unit based on multiple dynamic equilibriums. Appl. Therm. Eng. 113:774−790. DOI:10.1016/j.applthermaleng.2016.10.177

    View in Article CrossRef Google Scholar

    [2] Xu J., Wang K., Sheng H., et al. (2020). Energy efficiency optimization for ecological 3D printing based on adaptive multi-layer customization. J. Cleaner Prod. 245:118826. DOI:10.1016/j.jclepro.2019.118826

    View in Article CrossRef Google Scholar

    [3] Xu J., Tao M., Gao M., et al. (2025). Super-resolution 3D reconstruction from low-dose biomedical images based on expertized multi-layer refining. Expert Syst. Appl. 281:127474. DOI:10.1016/j.eswa.2025.127474

    View in Article CrossRef Google Scholar

    [4] Xu J., Liu K., Wang L., et al. (2023). Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins. Vis. Comput. Ind. Biomed. Art. 6:1−18. DOI:10.1186/s42492-023-00131-w

    View in Article CrossRef Google Scholar

    [5] Chen C., Xu J., Zhang S. (2026). Reducing idle trajectory for numerical controlled manufacturing of porous structures via neighborhood stratified heuristic. Comput. Aided Des. 194:104049. DOI:10.1016/j.cad.2026.104049

    View in Article CrossRef Google Scholar

    [6] Zhang S. and Xu J. (2009). Acquisition and active navigation of knowledge particles throughout product variation design process. Chin. J. Mech. Eng. 22:395−402. DOI:10.3901/cjme.2009.03.395

    View in Article CrossRef Google Scholar

    [7] Zhang S., Xu J., Gou H., et al. (2017). A research review on the key technologies of intelligent design for customized products. Engineering 3:631−640. DOI:10.1016/j.eng.2017.04.005

    View in Article CrossRef Google Scholar

    [8] Boiko D. A., MacKnight R., Kline B., et al. (2023). Autonomous chemical research with large language models. Nature. 624:570−578. DOI:10.1038/s41586-023-06792-0

    View in Article CrossRef Google Scholar

    [9] Alenjareghi M. J., Ghorbani F., Keivanpour S., et al. (2026). Proactive safety reasoning in human-robot collaboration in disassembly through LLM-augmented STPA and FMEA. Robot. Comput.-Integr. Manuf. 98:103162. DOI:10.1016/j.rcim.2025.103162

    View in Article CrossRef Google Scholar

    [10] Huy T. H. B., Duy N. T. M., Van Phu P., et al. (2024). Robust real-time energy management for a hydrogen refueling station using generative adversarial imitation learning. Appl. Energy. 373:123847. DOI:10.1016/j.apenergy.2024.123847

    View in Article CrossRef Google Scholar

    [11] Watson J. L., Juergens D., Bennett N. R., et al. (2023). De novo design of protein structure and function with RFdiffusion. Nature. 620:1089−1100. DOI:10.1038/s41586-023-06415-8

    View in Article CrossRef Google Scholar

    [12] Okumura K., Bonnet F., Tamura Y., et al. (2023). Offline time-independent multiagent path planning. IEEE Trans. Robot., 39:2720−2737. DOI:10.1109/TRO.2023.3258690

    View in Article CrossRef Google Scholar

    [13] Rassil A., Chougrad H. and Zouaki H. (2023). Deep multi-agent fusion Q-Network for graph generation. Knowl.-Based Syst. 269:110509. DOI:10.1016/j.knosys.2023.110509

    View in Article CrossRef Google Scholar

    [14] Sartore C., Elobaid M., Rapetti L., et al. (2026). Towards shared embodied intelligence in humanoid robots through optimization, development and testing of the human-aware ergoCub robot. Nat. Mach. Intell. 8:1221-1237. DOI:10.1038/s42256-026-01272-2

    View in Article CrossRef Google Scholar

    [15] Polonara M., Yang X., Carbonari L., et al. (2026). VL-GRiP3: A hierarchical pipeline leveraging vision-language models for autonomous robotic 3D grasping. Robot. Comput.-Integr. Manuf. 100:103244. DOI:10.1016/j.rcim.2026.103244

    View in Article CrossRef Google Scholar

    [16] Karniadakis G. E., Kevrekidis I. G., Lu L., et al. (2021). Physics-informed machine learning. Nat. Rev. Phys. 3:422−440. DOI:10.1038/s42254-021-00314-5

    View in Article CrossRef Google Scholar

    [17] Haarnoja T., Moran B., Lever G., et al. (2024). Learning agile soccer skills for a bipedal robot with deep reinforcement learning. Sci. Robot. 9:eadi8022. DOI:10.1126/scirobotics.adi8022

    View in Article CrossRef Google Scholar

    [18] Ferrari A. and Willcox K. (2024). Digital twins in mechanical and aerospace engineering. Nat. Comput. Sci. 4:178−183. DOI:10.1038/s43588-024-00613-8

    View in Article CrossRef Google Scholar

    [19] Dubarry M., Howey D. and Wu B. (2023). Enabling battery digital twins at the industrial scale. Joule, 7:1134−1144. DOI:10.1016/j.joule.2023.05.005

    View in Article CrossRef Google Scholar

    [20] Song L. and Gao G. (2026). Deep Learning-Based Error Compensation for High-Precision CNC Machining. Array, 32:101074. DOI:10.1016/j.array.2026.101074

    View in Article CrossRef Google Scholar

    [21] Panzer H., Wenzler D. L., Rauner D., et al. (2026). Prediction and homogenization of optical tomography images and microstructure during powder bed fusion of metals using a laser beam by means of a style-based generative adversarial network. Addit. Manuf. 116:105077. DOI:10.1016/j.addma.2026.105077

    View in Article CrossRef Google Scholar

    [22] Chen K., Qiu C., Liu Z., et al. (2026). A novel performance-geometry collaborated generative design frame for complex surface product. Aerosp. Sci. Technol. 176:112049. DOI:10.1016/j.ast.2026.112049

    View in Article CrossRef Google Scholar

    [23] Kender R., Kaufmann F., Rößler F., et al. (2021). Development of a digital twin for a flexible air separation unit using a pressure-driven simulation approach. Comput. Chem. Eng. 151:107349. DOI:10.1016/j.compchemeng.2021.107349

    View in Article CrossRef Google Scholar

    [24] Deng Q. and Chen M. (2026). Graph neural network-accelerated multi-objective design optimization of fractal flow fields for anion exchange membrane electrolyzers. J. Power Sources, 690:240841. DOI:10.1016/j.jpowsour.2026.240841

    View in Article CrossRef Google Scholar

  • Cite this article:

    Xu J., Chen M., Chen C., et al. (2026). New-generation artificial intelligence boosts product forward design capability in manufacturing industry. AI Plus 1:100002. https://doi.org/10.59717/ipj.aiplus.2026.100002
    Xu J., Chen M., Chen C., et al. (2026). New-generation artificial intelligence boosts product forward design capability in manufacturing industry. AI Plus 1:100002. https://doi.org/10.59717/ipj.aiplus.2026.100002

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