The product is often physically inspected for flaws that involve one or more steps of the manufacturing process. Visual inspection is typically a labor-intensive, highly manual process that is prone to mistakes. Although, rule-based visual inspection machines have also become more common these days.

However, each strategy has some shortcomings:

• Consistency in manual inspection is impacted by operator perception and experience.
• Traditional inspection equipment requires programming, is rigid, and cannot alter to accommodate changes to the product.
• Only a small number of problems can be found at once by current machine vision-based examination.

In other words, human/manual visual quality inspections frequently pose problems for manufacturers, incurring costs and lowering production effectiveness. Larger manufacturing companies experience these problems more severely because scaling human visual inspection just worsens the situation.

Similar issues also affect manufacturers who have upgraded to quality inspection tools and machinery.

• Since these technologies are still frequently managed by people, human error and inconsistent data are once again a possibility.
• Traditional equipment takes longer to adjust to new product modifications. Instead, businesses would have to spend money on new equipment, retooling, or extensive reprogramming.

Manufacturers are investing in AI-powered defect detection systems to automate manual quality management techniques and conduct more sensitive, thorough, and effective product quality inspections.

Utilizing a variety of AI and computer vision technologies, Artificial Intelligence solutions today can automate visual inspection operations, enabling manufacturers to improve quality control procedures by automatically identifying product flaws.

Manufacturers are gaining substantial advantages over general-purpose machine learning (ML) technologies by applying AI to various use cases:

• Autonomous on-premise operation: Manufacturers have the option of running inspection models on-site or at the network edge. The inspection can operate autonomously on the production floor or Cloud.
• Short time-to-value: Instead of the months that standard machine learning (ML) solutions often require, manufacturers can now get solutions in a matter of weeks. As AI is getting smarter, no prior knowledge of computer vision or machine learning is necessary for using these solutions.

• Advanced anomaly detection: Manufacturers can train models to identify, categorise, and precisely locate several fault kinds in a single image. This enables production line follow-up actions to be triggered automatically and without human involvement.
• Highly scalable deployment: Manufacturers can scale the solution across production lines and factories, managing the lifecycle of ML models with flexibility.

The AI-based quality control solutions can be applied in various sectors:

• Automotive manufacturing: Surface inspection in paint shops, welding seam inspection in body shops, press shop inspection for dents, scratches, and stains, and engine block inspection in foundries (cracks, deformation, anomaly)
• Semiconductor manufacturing: Wafer-level anomaly and defect localization, die crack inspection, pre-place inspection, SoC packaging inspection, and board assembly inspection.
• Electronics manufacturing: PCB soldering and gluing errors, product surface inspection, and defective or missing printed circuit board (PCB) components (screw, spring, foam, connector, shield, etc). (glue spill, mesh deformation, scratches, bubbles, etc.)
• General purpose manufacturing: Fabric inspection (mesh, tear, yarn), packaging and label inspection, metal, and plastic welding seam inspection, and surface inspection.

As was mentioned, the use of AI and computer vision systems offers several advantages over human-based visual quality assessment in production environments. These advantages consist of:

• Production-related quality problems can be considerably reduced by removing the possibility of human mistakes.
• In a couple of seconds, the program can identify several problems in the product. Humans and conventional visual inspection tools might only be able to spot a few flaws in a much longer period of time.
• To adapt to new product features and standards, personnel must be trained, which takes more time and money. Even if the AI needs to be "taught" to spot flaws, this process takes a lot less time and human involvement.

• Artificial intelligence, in contrast to humans, can go beyond simple flaw recognition. Further understanding of the flaws and their potential causes is also provided through its use of machine learning technologies.
• Unlike AI-powered quality inspection systems, which can be readily scaled across many production lines and manufacturing locations and offer more effective defect identification, human-based quality inspection is challenging to scale as production levels rise.

Manufacturing Industry is progressing at a breakneck pace and many companies are adopting modern manufacturing techniques to be ahead of the curve. Industry 3.0 is more than halfway towards Industry 4.0, join the revolution and make your operations much smoother and smarter with the power of AI and machine learning.

Daten & Wissen has helped many manufacturers ride the dream wagon of Industry 4.0, you could be next. Our team is here to ensure that you meet your business goals and fly high in the thriving market. Contact us today and let’s set a quote for your ambitious projects, we are always happy to help.