travel through the manufacturing line . Yet , as product demands and timeline speeds have increased , it becomes more difficult for the human eye to detect anomalies . Moreover , electronics products have components — such as printed circuit boards ( PCBs )— that are highly complex with hundreds , or even thousands of parts that are difficult for the human eye to see .
AI / ML-based defect detection systems are designed to use deep neural networks to detect defects that cannot be seen by conventional visual systems or human inspectors . This streamlines inspection processes , resulting in greater efficiency performance while optimizing factory floor space by making room for other lines and solutions through the elimination of legacy inspection stations .
Inspection staff can receive training in managing new technologies ahead of full adoption , which provides employees with advanced career opportunities and opens up new roles and skill sets within a business .
AI and ML algorithms also support quality control , and can provide insights into process optimization to make the manufacturing process faster and more efficient .
Lessons Learned from AI and ML Implementation
AI and ML technologies will continue to fundamentally change the manufacturing industry , but will only realize their full potential if manufacturers embrace their adoption across different areas of their businesses .
While doing so demands a substantial investment of time , effort , and resources , as well as upskilling workers to work with new technologies , the window of opportunity to integrate AI into
production processes is closing fast — those who have not started are at risk of being left behind .
Of course , there are still challenges ahead , from data readiness — where the quality of an AI / ML model is only as good as the training data — to quantifying the return on investment of AI / ML implementations , which can be tricky . Organizations need to identify the right use cases for the business , find relevant data , process it , and then develop , fine-tune , and eventually deploy models . These steps all take time , but they are vital to reaping the greatest benefits from an investment in AI / ML solutions .
AI is already here and will be a mainstay of the factories of the future . Skillsets are still in short supply , so there is value in implementing it sooner rather than later . Manufacturers today are successfully leveraging AI and ML in their manufacturing processes , and while hurdles remain , advanced technologies are at the forefront of Industry 4.0 and have the power to transform production and operations on every level .
For further information , please visit www . flex . com
Issue 68 PECM 59