It is very important to measure the number of product defects produced by a manufacturer because it is directly related to the profits of the company. The traditional method of identifying defective products is to identify defective products manually by humans, which is expensive because it is highly prone to human error and requires relying on a large number of people.
Using deep learning technology through data such as pressure, speed, and temperature that may affect product quality, you can quickly and accurately predict product defects. Predicting the quality of the product can not only reduce costs but also optimize the company's profits.
|Data||Data type||Content||Use mode|
|Input data||CSV||Manufacturing process information (environmental pressure, speed, temperature, cause of defective products)||API|
|Output data||CSV||Defective product prediction||API|
||Attached file upon application
||Customer data required for model creation
|API Setting||$1,800 ~|