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https://doi.org/10.58195/iaet.v2i1.134
Liu, X. . (2023). A New Perspective on Digital Twin-Based Mechanical Design in Industrial Engineering. Innovations in Applied Engineering and Technology, 2(1). https://doi.org/10.58195/iaet.v2i1.134
Volume 2, Issue 1

A New Perspective on Digital Twin-Based Mechanical Design in Industrial Engineering

The advent of digital twin methodologies in industrial engineering is inspiring a transformative wave in mechanical design processes. This innovative framework signifies the convergence of real and virtual worlds, enabling an unprecedented level of synergy between design, simulation, production, and evaluation cycles. The resultant 'digital counterparts' serve as dynamic blueprints that echo the life cycle of their physical kin, unraveling sophisticated opportunities for predictive maintenance, accelerated prototyping, and mitigated risks. Residing at the cusp of this evolution, mechanical design draws immense strategic advantage. The adoption of digital twins unearths potential for holistic design enhancements through real-time condition monitoring, performance prediction, and comprehensive data analytics. These processes structure efficient decision-making protocols, fostering increased product reliability, enhanced operational efficiency, and reduced time-to-market. Moreover, digital twins open new avenues for developing complex systems and large-scale machinery through seamless integration of interdisciplinary skills. Intricate simulations aid in isolating potential hurdles and foreseeing outcomes with substantial accuracy, ensuring a meticulous yet fluid design process. Ultimately, the emergence of digital twins in mechanical design embodies a remarkable shift pivoting towards Industry 4.0, redefining the paradigms of industrial engineering with the blend of cornerstone technologies. Leveraging these state-of-the-art methodologies hints towards an optimistic future regarding sustainable industry practices and technological advancements.

digital twin mechanical design industrial engineering real-time simulation predictive analytics cyber-physical systems

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Supporting Agencies

  1. Funding: Not applicable.