QProblem overview

Modern industrial "automated" production has become quite popular. However, the inspection of product quality still relies heavily on a large number of manual visual inspections or manual fixture measurement. Manual inspections are not only costly in personnel, but also unable to staff turnover and defect information. Immediate feedback on the front-end manufacturing process causing material loss and other issues. Manual inspection standards rely on the subjectivity of the rule of thumb, and personnel education and training are not easy, resulting in inconsistencies in quality specifications. However, the stability of product quality is one of the important key factors related to the competitiveness of enterprises.

  • Inspection over kills cause material waste and erode corporate profits.
  • Inspection misplacement (Under Kill) caused customer complaints to withdraw and faced compensation and loss of goodwill.


Through the four major functions of AOI machine vision: defect inspection, size measurement, barcode recognition, and multi-dimensional coordinate alignment, Yue Yang Zhikong uses image digital software technology to solve the problem of artificial quality inspection implicit internalization for the industry and assist customers Define quantitative standards for quality inspection. And combined with artificial intelligence deep learning technology, it can classify defects and quantify defect data, providing real-time defect cause analysis information. Save labor inspection costs, reduce product defect rates, effectively improve production line manufacturing processes, and assist companies in creating unmanned "intelligent quality inspection" operations.