Statistical Pattern Recognition for Manufacturing Quality Control

制造质量控制的统计模式识别

• 批准号: 533141-2018
• 负责人: Pawlak, Miroslaw
• 金额: $ 1.6万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=661777
• 关键词: Statistical; Pattern; Recognition; Manufacturing; Quality

Statistical process control (SPC) plays an important role in controlling a product's quality. SPC gives a signal**when the process mean or the product variability moves away from the permissible range. Such zone tests, also**known as run tests, are widely accepted classical techniques for recognition of the abnormal process behaviour.**However, operating within the specification limits does not necessarily signify a stable process. Run tests have**proven effective in indicating out-of-control situations, their effectiveness in interpreting process data,**however, is disputed.**The prime goal of this proposal is to have such custom inspection applications trained to recognize samples of**perfect and defective items. This is also the strategic objective of the Sightline company for increasing its**market competitiveness. Once the computational model reaches a sufficient level of accuracy, it would be**deemed ready to start inspecting and be deployed in a production environment. Upon receiving new data, the**model would use statistical pattern recognition alongside machine learning algorithms to detect any variance in**the product, such as scratches, contaminants, or improper dimensions.**If the product passes or a defect is detected, the operator would be notified immediately. As the model receives**high quality inspection data over time, it can be used for predictive analysis which would allow one to**understand when, where, how, and why defects are entering the process, thereby providing ample opportunity**to take preemptive and proactive measures. The ultimate objective of this project is to develop machine**learning models which can automatically learn complex structures and trends in large image and sensor**datasets collected from manufacturing processes. Using these learned features, we can identify known defects**in newly manufactured products and, potentially, discover new ones.

统计过程控制（SPC）在产品质量控制中起着重要的作用。当过程平均值或产品变异性偏离允许范围时，SPC给出信号 **。这种区域测试，也被称为运行测试，是被广泛接受的识别异常过程行为的经典技术。但是，在质量标准限度内运行并不一定意味着工艺稳定。运行测试已被证明在指示失控情况方面是有效的，但它们在解释过程数据方面的有效性 ** 却存在争议。**本提案的主要目标是对此类定制检验应用程序进行培训，以识别 ** 完美和有缺陷项目的样本。这也是视线公司提高市场竞争力的战略目标。一旦计算模型达到足够的精度水平，它将被认为准备好开始检查并部署在生产环境中。在接收到新数据后，** 模型将使用统计模式识别以及机器学习算法来检测 ** 产品中的任何差异，例如划痕，污染物或不正确的尺寸。如果产品合格或检测到缺陷，将立即通知操作员。由于该模型随着时间的推移接收到 ** 高质量的检测数据，它可以用于预测分析，这将允许人们 ** 了解缺陷何时，何地，如何以及为什么进入过程，从而提供充足的机会 ** 采取先发制人的主动措施。该项目的最终目标是开发机器学习模型，该模型可以自动学习从制造过程中收集的大型图像和传感器数据集中的复杂结构和趋势。使用这些学习到的特征，我们可以识别新制造产品中的已知缺陷 **，并可能发现新的缺陷。