@article{4637,
  author = {Kwanchai Saeweenan, Hathairat Ketmaneechairat},
  title = {The Convergence of Data Science in Machine Design: Transforming Design, Operations, and Innovation in the Digital Era},
  journal = {Transactions on Machine Design},
  year = {2026},
  volume = {14},
  number = {1},
  doi = {https://doi.org/10.6025/tmd/2026/14/1/1-9},
  url = {https://www.dline.info/tmd/fulltext/v14n1/tmdv14n1_1.pdf},
  abstract = {This brief review explores the transformative convergence of data science and mechanical engineering,
emphasizing its growing relevance for both industry and small to medium enterprises (SMEs). The authors
argue that data science integrating mathematics, statistics, machine learning, and domain expertise enhances
traditional engineering practices by enabling the development of predictive, adaptive, and intelligent systems.
Mechanical engineers, with their strong foundation in modeling, physics, and systems thinking, are well
positioned to adopt data science methods to improve design, analysis, and maintenance processes. Key
applications highlighted include predictive maintenance, generative and simulation driven product design,
process and supply chain optimization, real time quality control, and energy management. The paper outlines
a standard data science workflow data collection, preparation, exploration, modeling, and communication
and illustrates how each phase supports engineering decision making. Notably, the authors emphasise that
SMEs can leverage affordable cloud platforms, open source tools, and IoT devices to implement these techniques
without substantial investment. The integration fosters a data driven mindset, turning raw operational
data into strategic assets that drive efficiency, innovation, and sustainability. Ultimately, the fusion of data
science with mechanical engineering represents a paradigm shift toward evidence based, intelligent
engineering systems capable of addressing 21st-century industrial challenges.},
}