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

探索性研究小额拨款（SGER）的目的是表征和关联加工零件的形状误差与加工过程序列和用于生成形状的过程变量。这些信息将用于：(a)提供反馈并建立明确的指导方针，通过最佳选择工艺序列和工艺参数来减少形状误差，(b)制定个性化的最佳检查计划方法，以评估工艺序列/工艺变量特定的每个形状误差的真实值，以及(c)为工艺/机器制定形状过程能力指数。在本研究中，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制孔工艺和车削工艺产生的形状特征。这些形式的轮廓/表面将使用解析傅立叶模型和广义圆柱体建模。通过将剖面映射到新的s-theta和psi-s域，可以提取剖面/曲面的一般形状、叶数、叶幅、偏心率、叶间距均匀性、形状误差等属性。基于神经网络的方法将用于训练系统来预测标准化轮廓，标准化属性（叶，振幅等）的范围以及基于过程顺序和参数的形状误差范围。对于每个标准化轮廓族，将使用基于实验设计（DOE）的方法来确定样本量、抽样方法和评估算法（最小二乘或最小区域）的最佳组合，以用于每个轮廓族的特定过程的形状检查，并可以以一定的置信度估计真实的形状误差。结合遗传算法（GA）的一种新的组合优化公式将被用于计算形状误差的最小区域。最后，基于标准化概要文件和每个概要文件族的表单错误，将为每个表单错误开发一个表单处理能力指数。如果成功，该研究将为基于工艺变量和顺序的形状轮廓和误差预测提供基础。这可能导致选择最佳工艺方案/变量，以减少形状错误。这项研究还将为评估表单错误的最佳检查计划提供指导，这些错误是特定于创建表单的过程的。最后，建立的成形工艺能力指标可用于优化制造公差分配、设计定心和机床选择。这样的计划可以很容易地被设计师和车间的从业者使用。

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.