Machine vision technology has revolutionised the manufacturing industry by enabling automated inspection and quality control of various components. In this case study, we will explore how a shock absorber manufacturer used machine vision technology to improve its quality control processes.
The company had decided to implement an automated control system to maintain the quality of their shock absorber components, as manual inspection processes were prone to errors and could not keep up with the increasing demand for their products. They decided to implement a machine vision system that could inspect each component automatically and identify defects in real time. The system also provided the option of full traceability, including images of every component of every shock absorber and test results, which were fed directly into the company’s ERP system.
The machine vision system was designed to capture high-resolution images of the components and analyse them for various quality parameters, such as size, shape, colour and surface defects. The system was also equipped with advanced algorithms that could detect even the smallest defects that were difficult to detect with the naked eye.
The machine vision system was integrated into the production line and each shock absorber component was automatically fed through the system for inspection. If any defects were detected, the system would immediately send a signal to the production line and the defective component would be rejected and removed from the production line.
The implementation of machine vision technology resulted in a significant improvement in the quality of shock absorber components. The system was able to detect even the smallest defects with a high degree of accuracy, and the automated inspection process eliminated errors and increased efficiency.
Machine vision technology has proven to be a game changer for the manufacturing industry, and the case study of the shock absorber manufacturer is a great example of how this technology can be used to improve quality control processes.