Visual Inspection by Artificial Intelligence Model for Plating Mill
the objective of this report is to summarise our recent experiment in using Artificial Intelligence to help metal plating business inspect the products in QC process. This process usually is conducted by a group of well-trained staffs which contributed around 20 – 30% of total staff of the production line. We tried to find the best solution the the problems such as high spending on staffs, inaccurate results from staffs and etc.
Spending on staffs always come with high cost and welfare, moreover when we have a lot of staffs we need to train them regularly which it is time consuming as well. This cost is tend to be higher over time. So having a clear plan in reducing staff cost is a must have for this business.
Quality control by using human always have inaccurate results due to uncertainty of sight seeing and mismatch opinion. Though the factory tried to implement double layer inspection, there are always mistakes happened to the inspection result.
Inaccurate quality control process lead to reject products from customer and that also has an effect on our a huge loss of the business. With a low margin per unit that the business make on each order, just 5% product rejection could result into a huge lost at the end.
We proposed Visual Inspection Program using Artificial Intelligence as a solution to replace unreliable staff with more accurate Visual Inspection program. Furthermore, A.I. also reduce cost spending on staffs and time consuming. We believe that our program will be the solution to the problems that most factories have on quality control process.
Inspection staffs help training AI by marking color code onto the defecting area on our software. AI will keep learning and adding into AI’s neural network.
This testing round is to test the ability of AI Model in identifying flaw on final product as compared to the result of a group of inspecting staffs.
|product||staff 1||staff 2||staff 3||AI||majority answer||staff 1 score||staff 2 score||staff 3 score||AI score|
Keep training AI with more dataset to improve accuracy. Next target would be achieving 70% accuracy.