SGER: An Integrated Approach to Prediction, Assessment and Inspection of Form Errors in Machined Parts

这一小笔探索性研究补助金(SGER)的目标是描述加工零件中的形状误差，并将其与用于生成表格的加工工艺序列和工艺变量相关联。这些信息将用于：(A)提供反馈并建立明确的指导方针，通过优化选择工艺序列和工艺参数来减少成形误差，(B)制定个性化的最佳检验计划的方法，以评估工艺序列/工艺变量特定的每个成形误差的真实值，以及(C)制定工艺/机器的成形工艺能力指数。在这项研究中，将考虑两种类型的形状误差，即圆度和圆柱度。所考虑的工艺领域将限于由传统的打孔工艺和车削工艺产生的成形特征。这些形状轮廓/表面将使用解析傅立叶模型和广义圆柱体进行建模。通过将轮廓映射到新的S-theta和psi-S区域，可以提取轮廓/表面的属性，如总体形状、叶数目、叶幅度、偏心率、叶间距均匀、形状误差。将使用基于神经网络的方法来训练系统预测标准化轮廓、标准化属性的范围(波瓣、幅度等)。以及基于工艺顺序和参数的形状误差范围。对于每个标准型材系列，将使用基于实验设计(DOE)的方法来确定样本量、抽样方法和形状检查的评估算法(最小二乘或最小区域)的最佳组合，该算法针对每个型材系列的工艺而定，并且可以在一定的置信度下估计真实的形状误差。将一种新的组合优化公式与遗传算法(GA)相结合用于计算形状误差的最小区域。最后，基于每个轮廓族的标准化轮廓和表格误差，将为每个表格错误制定表格处理能力指数。如果成功，这项研究将为基于过程变量和序列预测表单轮廓和错误提供基础。这可能导致选择最佳工艺计划/变量以减少表格错误。这项研究还将为评估表格错误的最佳检查计划提供指导方针，这些错误特定于表格创建过程。最后，所开发的成形工序能力指数可用于制造公差的优化分配、设计对中和机床选型。这样的计划很容易被设计师和实践者在车间里使用。

The objective of this Small Grant for Exploratory Research (SGER) is to characterize and correlate form errors in machined parts with machining process sequences and process variables used for generating the form. This information will be used to: (a) Provide feedback and establish clear guidelines for reducing form errors by optimal selection of process sequences and process parameters, (b) Develop methods for individualized optimal inspection plans for assessing the true value of each form error that are process sequence/ process variables specific, and (c) Develop form process capability indices for processes/machines. For this research, two types of form errors, namely, Circularity and Cylindricity, will be considered. The domain of processes considered will be limited to form features generated by conventional hole making processes and turning processes. These form profiles/surfaces will be modeled using analytical Fourier Models and Generalized Cylinders. Attributes of profiles/surfaces such as general shape, number of lobes, amplitude of lobes, eccentricity, evenness of lobe spacing, form errors, will be extracted by mapping profiles to a novel s-theta and psi-s domains. A Neural network based approach will be used for training the system to predict standardized profiles, ranges of standardized attributes (lobes, amplitude etc.) as well as range of form errors based on process sequence and parameters. For each family of standardized profiles, a Design of Experiments (DOE) based approach will be used for determining the optimum combination of sample size, sampling method, and evaluation algorithm (least square or minimum zone) for form inspection that is process specific for each profile family, and can estimate the true form error with a certain level of confidence. A novel combinatorial optimization formulation in conjunction with Genetic Algorithms (GA) will be used to calculate minimum zones of the form errors. Finally, based on standardized profiles and form errors for each profile family, a form process capability index will be developed for each of the form errors. If successful, this research will provide a basis for predicting form profiles and errors based on process variables and sequence. This could result in selection of optimal process plans/variables for reducing form errors. This research will also provide guidelines for optimal inspection plans for assessing form errors that are specific to the process creating the form. Finally, the form process capability indices developed can be used for optimal manufacturing tolerance allocation, design centering and machine selection. Such a plan can readily be used by designers as well as practitioners on the shop floor.