The Importance of Domain Specific Data in Developing AI Applications for Manufacturing

QualiSense VP Explains Importance of Domain Specific Data in AI Applications for Manufacturing

AI tools like ChatGPT have revolutionized the way we approach data analysis and decision-making processes. However, what happens when your AI application requires domain-specific data that is locked away behind a wall of secrecy? Miron Shtiglitz, VP Product and Delivery at visual inspection company QualiSense, sheds light on the importance of domain-specific data in developing AI applications for manufacturing.

In the world of deep learning models, the availability of labeled data is crucial for their efficacy. While acquiring such data has become easier for various applications, developing AI-driven systems for production environments, such as quality inspection systems, presents a significant challenge.

Pre-trained deep learning models have gained popularity for their ability to expedite development across multiple applications. These models come with a foundational understanding of essential features, making them adept at distinguishing and comprehending complex information.

However, the availability of vast image datasets online has not translated seamlessly into the industrial space. Image datasets for industrial processes are often restricted, making it challenging to develop models for this domain.

In the realm of industrial quality inspection, using pre-trained models can offer some advantages, but the benefits diminish as the application moves away from the dataset’s domain. For example, using a pre-trained model designed for color images in a grayscale or hyperspectral image application may extract irrelevant features, hindering the desired outcome.

QualiSense has tackled this challenge by partnering with Johnson Electric, a global leader in the automotive industry, to access a diverse range of production line images. This unique data repository has enabled QualiSense to build an AI model with a generic understanding of the production domain, adaptable to specific production environments.

The scarcity of accessible proprietary data in manufacturing poses a formidable challenge for AI-driven quality inspection. While pre-trained models have their place, the key to success lies in domain-specific data that reflects the intricacies of the production environment.

For more insights on the technology challenges facing AI-driven quality inspection, visit the QualiSense blog at qualisense.ai/blog.