Machine learning for engineering design toward smart customization: A systematic reviewXingzhi Wang, Ang Liu, Sami Kara2022-10-01reviewJournal of Manufacturing Systems65 citationsclosedNo readable copy stored yet — this record is metadata only.Product Development and CustomizationManufacturing Process and OptimizationFlexible and Reconfigurable Manufacturing SystemsAbstractNo abstract was published for this record.Citations in this libraryStored only when both papers are in the corpus — 65 citations exist upstream.References2003Towards a Methodology for Developing Evidence‐Informed Management Knowledge by Means of Systematic Review2020Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies2001Axiomatic Design: Advances And Applications2009Factors affecting response rates of the web survey: A systematic review2013Evolving Paradigms of Manufacturing: From Mass Production to Mass Customization and Personalization1998The extended enterprise-a context for manufacturing2016In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining2018Mapping customer needs to design parameters in the front end of product design by applying deep learningCited by2024Integration of data science with product design towards data-driven design2025A data-driven design parameter recommendation approach based on personalized requirements for product conceptual design2024A cognitive computing approach for product configuration design based on associative rule mining and deep reinforcement learning