AI in manufacturing – Improving the dynamic production of distinct products
The AI manufacturing revolution provides a fundamental shift in the way companies run their production lines to optimize yields and maximize quality.
In industries such as automotive, electronics and consumer products, AI in manufacturing has already proved successful in predictive maintenance, defect detection, yield optimization, and other applications, thereby reducing costs and improving efficiency.
Cars and electronics are examples of distinct products whose production is dynamic: the product itself changes, testing parameters are modified, and more equipment is added to increase production. A manufacturing AI solution integrates those changes automatically without interfering with the process. Through predictive maintenance, artificial intelligence can also reduce unplanned downtime in manufacturing.
Detecting faults units through the use of AI in manufacturing
Early detection of faults or defects is crucial to reducing recalls and improving product quality. AI analytics has the potential to improve defect detection precision and accuracy. The technology is able to detect defects that would otherwise go undetected, and do so earlier in the process, reducing defective products, product returns, and waste materials and maximizing process yield, quality of products Return On Investment.
QualityLine AI analytics was developed to provide Automated root cause analysis using AI Analytics technology across all aspects of the manufacturing process, starting from the supplier to the after-market and field data. QualityLine’s AI analytics predict failure levels for every parameter and process.
Manufacturers strive to improve business operations and scale their production in every sector. Manufacturing yields are being optimized by integrating machines and test equipment that can provide a holistic view of all production lines to handle increasingly complex production lines.
With AI manufacturing solutions, each step of the manufacturing process is controlled to the very last detail. Creating end-to-end control and deep root cause analysis with each data source is the key to improving first pass yield.
As a result of QualityLine’s AI manufacturing analytics and automated data integration, manufacturing first pass yield is maximized since it continuously collects all manufacturing data into a unified digital twin database, harmonizing and analyzing the data from multiple locations globally in real time to create end-to-end control.
The most effective method of analyzing product test results is to use manufacturing tools. Artificial intelligence analytics can identify when productivity drops, analyze and correlate all performance metrics to determine what the reason for inefficiency is, and suggest specific changes to the production line.