White Paper: Manufacturing & Prototyping

Deploying Hybrid AI to Reduce Inspection Costs


Artificial intelligence (AI) brings promises of reduced costs and increased efficiencies for inspection applications, equally balanced by concerns over complexity and usability among other factors. This case study discusses the basics around AI and machine learning, and common deployment concerns for organizations. It then introduces the concept of hybrid AI, which aims to ease deployment by simplifying algorithm development and training while allowing designers to introduce machine learning capabilities within existing infrastructure and processes. To close, it highlights a real-world case study of a manufacturer evaluating AI for a pharmaceutical inspection application.

AI is one of the most hyped technologies of recent years, and while it promises new cost and process benefits for inspection applications, deployment remains a challenge.

Part of the technology trepidation stems from uncertainty around the terms and definitions of “AI” and “machine learning.” Organizations are also unsure how to deploy new AI capabilities alongside existing infrastructure and processes. This is especially true in inspection systems, where there are significant investments in cameras, specialized sensors, and analysis software with well-established processes for end-users. The cost and complexity of algorithm training is also a concern for businesses evaluating AI.

A hybrid approach to AI is designed to work with existing inspection hardware and software, with integrated plug-in skills, and a user-friendly approach to training and custom integration.

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