With the increased demand in the market for goods and services, the manufacturing industry is pressurized to produce and supply mass production. This has demanded industries to speed up the manufacturing process and deliver standard quality goods buy-able to customers.
To meet the demand, firms are adopting the best practices available to improve productivity. Quality control is one of the primary measures in manufacturing processes.
Quality control is demanding and failure in the detection of defects in the produced goods is an added expense to the company. Further, flawed products in the market leads to low returns and customer dropouts. Employing computer vision and deep learning in quality assurance methods will significantly reduce errors improving overall operational efficiency.
Before CV implementation, most of the quality check was done manually which is time-consuming and error-prone. Hence, the traditional way of manual quality checks done on the products after the final goods are produced has become obsolete. And industries are moving towards quality inspection by visual monitoring machines at every step of production; improving the quality of products and removing human error.
Improving Quality Control in Manufacturing plant
According to Forbes, automating quality testing with machine learning can shoot up defect detection rates by up to 90%. Visual inspection coupled with deep learning can detect faults accurately and at a faster speed removing human interference.
Deep learning enhances the monitoring machine's ability to spot flawed products. Using deep learning, computer vision monitoring gets thorough with perfect product features and can easily spot flaws. Using iterative deep learning, the machine later trains itself using the record data towards ideal accuracy.
Computer vision uses multiple cameras and scanners for inspecting the production line. CV systems employed not only ensures production is of optimal standards, but also supervises the assembly process. Recently, an agriculture firm introduced a high-metric fruit sorting machine that uses computer vision and deep learning to classify skin defects. The operator can train the sorting platform to differentiate between types of imperfections and sort the fruits into graded packets accordingly. The solution combines hardware, software and operational optimization to reduce the complexity of the sorting process.
Skyl.ai’s Computer vision implementation
Skyl.ai computer vision techniques help automate the quality control in manufacturing plants. It can assist floor supervisors and machine operators to ensure that the production line is maintaining the desired high-performance standards. Our machine learning solutions help manufacturers to get the planned returns with no loss and maintain their benchmark.
Check out the various solutions that can be built using Skyl platform here.