A typical manufacturing process consists of a machining (material removal) process followed by an inspection system for the quality checks. Usually these checks are performed at the end of the process and they may also involve removing the part to a dedicated inspection area. This paper presents an innovative perturbation signal based data generation and machine learning approach to build a robust process model with uncertainty quantification. The model is to map the in-process signal features collected during machining with the post-process quality results obtained upon inspection of the finished product. In particular, a probabilistic framework based on Gaussian Process Regression (GPR) is applied to build the process model that accurately and reliably predicts key process quality indicators. Raw data provided by multiple sensors including accelerometers, power transducers and acoustic emissions is first collected and then processed to extract a large number of signal features from both time and frequency domains. A strategy for the selection of most relevant features is also explored in this work in order to reduce the input space dimension and achieve faster training times. The proposed GPR model was tested on a multi-robot countersinking application for monitoring of the machined countersink depths in composite aircraft components. Experimental results showed that the model can be used as a tool to predict the part quality through in-process sensory information, which in turn, helps to reduce the total inspection time by identifying the parts that would require further investigation.

典型的制造过程包括机加工（材料去除）过程，然后是用于质量检查的检查系统。通常，这些检查在过程结束时执行，并且还可能涉及将零件移至专用检查区域。本文提出了一种创新的基于扰动信号的数据生成和机器学习方法，以建立具有不确定性量化的鲁棒过程模型。该模型将映射在加工过程中收集的过程中信号特征与对成品进行检查时获得的过程后质量结果。特别是，基于高斯过程回归（GPR）的概率框架可用于构建过程模型，该过程模型可准确可靠地预测关键过程质量指标。首先收集由多个传感器（包括加速度计，功率传感器和声发射）提供的原始数据，然后进行处理以从时域和频域提取大量信号特征。在这项工作中，还探索了一种选择最相关特征的策略，以减少输入空间的尺寸并缩短训练时间。拟议的GPR模型已在多机器人埋头孔应用中进行了测试，以监控复合飞机部件中机加工的埋头孔深度。实验结果表明，该模型可以用作通过过程中的感官信息预测零件质量的工具，从而通过识别需要进一步研究的零件来帮助减少总检查时间。首先要收集功率传感器和声发射，然后进行处理以从时域和频域提取大量信号特征。在这项工作中，还探索了一种选择最相关特征的策略，以减少输入空间尺寸并缩短训练时间。拟议的GPR模型已在多机器人埋头孔应用中进行了测试，以监控复合飞机部件中机加工的埋头孔深度。实验结果表明，该模型可以用作通过过程中的感官信息预测零件质量的工具，从而通过识别需要进一步研究的零件来帮助减少总检查时间。首先要收集功率传感器和声发射，然后进行处理以从时域和频域提取大量信号特征。在这项工作中，还探索了一种选择最相关特征的策略，以减少输入空间的尺寸并缩短训练时间。拟议的GPR模型已在多机器人埋头孔应用中进行了测试，以监控复合飞机部件中机加工的埋头孔深度。实验结果表明，该模型可以用作通过过程中的感官信息预测零件质量的工具，从而通过识别需要进一步研究的零件来帮助减少总检查时间。