Core Insights - The article discusses the application of AI technology in instrument development, focusing on structural design, tolerance analysis, and multi-physics coupling analysis, aiming to inspire industry users [2][3]. Group 1: Structural Mechanics Analysis - AI is transforming structural mechanics from "verification" to "generation" and "instant prediction," utilizing techniques like Physics-Informed Neural Networks (PINNs) and reduced-order models [7][34]. - Traditional Finite Element Analysis (FEA) is time-consuming, while AI can create a surrogate model to predict stress, strain, or displacement in milliseconds [9][10]. - The article presents a case study on generative design and topology optimization for sequencing instruments, emphasizing the importance of structural rigidity in optical components [10][12]. Group 2: Precision Tolerance Analysis - AI is used to handle complex nonlinear assembly relationships and establish feedback loops between manufacturing data and design tolerances, moving from "ideal distribution" to "real yield" [24][25]. - Key strategies include tolerance synthesis, sensitivity analysis, and visual-assisted GD&T, which leverage historical manufacturing data for more accurate tolerance allocation [25][27]. - A case study highlights the control of spacing between optical components in sequencing instruments, addressing the challenges of non-linear deformation during assembly [28][30]. Group 3: Multi-Physics Coupling - Multi-physics coupling, particularly opto-mechanical-thermal coupling, is identified as a challenging area where AI can provide significant value [34][36]. - AI applications include predicting thermal deformation in optical components and analyzing fluid-induced vibrations that may affect optical stability [38][41]. - The use of PINNs allows for the integration of physical laws into AI models, enhancing the accuracy of predictions even with limited simulation data [36][40]. Group 4: Implementation Roadmap - The article outlines a four-level roadmap for building AI capabilities in research and development, from data infrastructure to digital transformation [45]. - Each level focuses on specific goals, such as data cleaning, design assistance, surrogate modeling, and achieving active thermal compensation [45]. - The ultimate goal is to create a "virtual physicist" that enhances precision and efficiency in instrument development, moving beyond traditional engineering methods [46].