Statistical Pattern Recognition for Manufacturing Quality Control

统计过程控制（SPC）在控制产品质量方面起着重要作用。SPC给出一个信号**当过程平均值或产品可变性远离允许范围。这种区域测试，也称为运行测试，是被广泛接受的识别异常进程行为的经典技术。**然而，在规范范围内运行并不一定意味着稳定的过程。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**是有争议的。**本提案的主要目标是对此类海关检验应用程序进行培训，以识别**完美和有缺陷的产品的样品。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确度，就可以开始检查并部署到生产环境中。在接收到新数据后，该模型将使用统计模式识别和机器学习算法来检测产品的任何差异，例如划痕、污染物或尺寸不当。**如果产品合格或检测到缺陷，将立即通知操作人员。随着时间的推移，模型接收到高质量的检查数据，它可以用于预测分析，这将允许人们了解何时，何地，如何以及为什么缺陷进入过程，从而提供充分的机会来采取先发制人的和主动的措施。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。

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.