How Computer Vision and Deep Learning Assist Defect Detection In The Manufacturing Industry

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As industrial environments become increasingly digitized, manufacturers are considering how Artificial Intelligence (AI) can be implemented to automate processes that have previously required human operators. To this end, quality control is one of the most important processes within a manufacturing system, yet manual inspections often come with substantial drawbacks as defects can easily be overlooked by the human eye. Automating defect detection with AI-powered visual inspection can create a reliable means of mitigating quality control errors even in complex manufacturing environments.

Defect Classification & Manual Visual Inspection Limitations

Operator conducted Visual inspection is the method of looking for flaws or imperfections using the naked eye and non-specialized inspection equipment. This approach is commonly used by maintenance professionals in physical asset management across industries and often serves as the final step in the manufacturing.

However, defects can be easily missed by the naked eye, even for experienced operators. Even small oversights can have wide reaching and costly consequences, resulting in lower component quality or a defective final product that may be rejected. With more complicated manufacturing systems, the number of defects will likely increase.

Engineers are continuously developing new ways to automate repetitive manual activities and having an automatic system to detect abnormalities and discrepancies in elements is critical for the quality inspection process. AI is are the forefront of these efforts, as it enables the automation of sophisticated yet respective activities.

The Concept of AI-based Visual Inspection Explained

While AI offers endless process automation potential, its initial benefits are often overstated as AI systems require careful implementation to ensure they operate successfully. In this case, AI is able to take on the task of automated visual inspection processes through the power of machine learning (ML), or learning by example In order to build an artificial neural network.

·        Artificial Intelligence: Simulates human intelligence to automate a set behavioral patterns.

·        Machine Learning: Algorithms that build models based on sample data in order to make predictions, decisions, or improvements without being explicitly programmed to do so.

·        Deep learning: Artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data.

Through a process of extracting common patterns between labeled examples of specific data points. ML systems then translate case scenarios into a systemic equation, building a neural network with labeled examples and interconnected data points.  AI systems rely their neural network to engage in deep learning, ultimately aiding in the classification of future data.

Benefits of AI Enabled Industrial Defect Detection

The following are some of the ROI that businesses can expect to see from implementing and AI powered defect detection:

·        It improves production yield while lowering waste.

·        Customer satisfaction rises as product quality rises.

·        Warranty claims and after-sales defects are drastically minimized.

·        Locating parts and ensuring that product assembly is completed efficiently.

·        It is now possible to detect faults that are unpredictable and varied.

·        Classifying problems and parts improved process control.

How to Integrate/Implement AI Visual Inspection Systems

With enough data, the neural network will eventually detect defects without any additional instructions. Deep learning-based visual inspection systems are good at detecting defects that are complex in nature. They not only address complex surfaces and cosmetic flaws—but also generalize and conceptualize the parts’ surfaces.

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