Over the years, Digital Twin (DT) has become popular in Advanced Manufacturing (AM) due to its ability to improve production efficiency and quality. By creating virtual replicas of physical assets, DTs help in real-time monitoring, develop predictive models, and improve operational performance. However, integrating data from physical systems into reliable predictive models, particularly in precision measurement and failure prevention, is often challenging and less explored. This study introduces a Predictive Maintenance and Inspection Digital Twin (PMI-DT) framework with a focus on precision measurement and predictive quality assurance using 3D-printed 1''-4 ACME bolt, CyberGage 360 vision inspection system, SolidWorks, and Microsoft Azure. During this approach, dimensional inspection data is combined with fatigue test results to create a model for detecting failures. Using Machine Learning (ML) -- Random Forest and Decision Tree models -- the proposed approaches were able to predict bolt failure with real-time data 100% accurately. Our preliminary result shows Max Position (30%) and Max Load (24%) are the main factors that contribute to that failure. We expect the PMI-DT framework will reduce inspection time and improve predictive maintenance, ultimately giving manufacturers a practical way to boost product quality and reliability using DT in AM.

翻译：近年来，数字孪生因其在提升生产效率和产品质量方面的能力，在先进制造领域日益普及。通过创建物理资产的虚拟副本，数字孪生有助于实现实时监控、开发预测模型并提升运营性能。然而，将来自物理系统的数据整合到可靠的预测模型中，尤其是在精密测量与故障预防方面，通常具有挑战性且相关研究较少。本研究提出了一种预测性维护与检测数字孪生框架，重点利用3D打印的1''-4 ACME螺栓、CyberGage 360视觉检测系统、SolidWorks以及Microsoft Azure平台，实现精密测量与预测性质量保证。在该方法中，尺寸检测数据与疲劳测试结果相结合，用于构建故障检测模型。通过采用机器学习——随机森林与决策树模型——所提出的方法能够利用实时数据100%准确地预测螺栓故障。我们的初步结果表明，最大位置（30%）与最大载荷（24%）是导致该故障的主要因素。我们预期PMI-DT框架将减少检测时间并改进预测性维护，最终为制造商提供一种在先进制造中利用数字孪生提升产品质量与可靠性的实用途径。