One of the key areas where visual inspection is still critical to quality control is in electronics inspection. While mass produced consumer goods benefit from machine vision, there is still a place for skilled human inspection for custom or short-run products where full automation is too expensive or complex. In addition, while automated optical inspection (AOI) catches most errors, it may miss issues such as component orientation, through hole, labelling, and soldering defects.
DICA Electronics is a specialized electronics manufacturer with expertise in smaller run, customized products for high-value, high-reliability applications. The company has adopted AI based decision-support tools for products not well-suited to AOI, or as a secondary inspection following manual processes. The intent is not to replace the human inspector, but help ensure they are making consistent, informed decisions as their attention to detail may begin to fade over a long day. Additionally, new products or inspectors can be more easily added to the production line, with AI ensuring the same consistent decision is made.
The manufacturer has trained the system with number of “golden reference” images for manually inspected products. As part of the training, the operator’s own early decisions on possible defects has helped train and fine-tune the AI model. As the operator and quality manager gains confidence in the accuracy of the system, they can stop AI model training. The company was able to start using AI, without requiring algorithm development expertise.
As important as error-detection, the system’s integrated reporting tools provides the manufacturer with key data surrounding manual inspection processes. DICA uses the image save function included in the reporting capabilities to capture a photo of every manually inspected board and its bar code, along with any operator notes. Data is then saved to their manufacturing resource planning software, along with data from their automated inspection processes. If an in-field error is reported, the manufacturer can then verify if a “good part” left their facility to help speed resolution.
The ability to help pinpoint errors in manual processes, or verify that a good product did leave the facility and a downstream issue is cause for concern, saves the manufacturer from numerous hidden costs. Without data, quality managers and staff are diverted to issue resolution processes, often involving numerous emails and phone calls with customers and other suppliers. Replacement parts may be shipped at the cost to the manufacturer, even though the root cause of the issue may not have been within their process or area of responsibility.