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The metrology data of the product quality aim to conform to customer requirements and minimize defect costs via quality control. Product quality means the overall quality of a product, which is what really matters for the customers.

A quality report that records measurement results for a specific product (e.g. dimensional accuracy, surface roughness, and position tolerance, etc.) can be completed via the inspection of various measurement equipment, such as the coordinate measuring machine (CMM) or the automated optical inspection (AOI) device, so that it shows whether the status of the product conforms to customer's requirements, specifications, and expectations. For any case, the best situation is to measure the practical quality of all end‐products.

Note that, metrology delay varying with the inspection frequency and measurement place would restrict the feasibility of total inspection. Therefore, intelligent applications [1–8] should come in place to solve this issue.

In short, process data are the major contributors to metrology data. They are used as the predictor variables; while the metrology data of products can be used as a label variable or category for typical supervised learning or reinforcement learning methods. In other words, given that the data acquisition quality is reliable, the causation between process data and metrology data would be a strong correlation that efficiently indicates if the manufacturing process performs well or not.

Thus, the root cause analysis can be conducted to narrow down to the specific process steps that affect product quality. Manufactures, therefore, have a chance to make their production line free from deficiencies or defects via continuous improvement and corrective actions.