The total installed base of AI-enabled devices in industrial manufacturing is forecast to reach 15.4 million within five years, with a CAGR of 64.8% from 2019 to 2024.
It’s claimed that Artificial Intelligence (AI) will revolutionise the industrial manufacturing space, and in many respects that transformation has already begun.
AI is already delivering generative design in product development, production forecasting in inventory management, and machine vision, defect inspection, production optimisation, and predictive maintenance in the production phase.
“AI in industrial manufacturing is a story of edge implementation,” says Lian Jye Su, principal analyst at global tech market advisory firm, ABI Research.
“Since manufacturers are not comfortable having their data transferred to a public cloud, nearly all industrial AI training and inference workloads happen at the edge, namely on device, gateways and on-premise servers.”
To facilitate this, AI chipset manufacturers and server vendors have designed AI-enabled servers specifically for industrial manufacturing. More and more industrial infrastructure is equipped with AI software or dedicated AI chipsets to perform AI inference.
Despite these solutions and the wealth of manufacturing data being generated, the implementation of AI in industrial environments hasn’t been as seamless or as rapid as expected.
Among all the use cases, predictive maintenance and equipment monitoring are the most commercially implemented so far, due to the maturity of associated AI models.
The total installed base for these two use cases alone is expected to reach 9.8 million and 6.7 million, respectively, by 2024, according to ABI research. Though it’s worth noting that many of these AI-enabled industrial devices support multiple use cases on the same device due to advancements in AI chipsets.
Another commercial use case currently gaining momentum is defect inspection. The total installed base for this use case is expected to grow from 300,000 in 2019 to more than 3.7 million by 2024.
This is a use case that is extremely popular in electronic and semiconductor manufacturing, where major manufacturers, such as Samsung, LG and Foxconn, have been partnering with AI chipset vendors and software providers to develop AI-based machine vision to perform surface, leak and component-level defect detection, microparticle detection, geometric measurement, and classification.
Conventional machine vision technology remains popular in the manufacturing factory, due to its proven repeatability, reliability, and stability.
However, the emergence of deep learning technologies opens the possibility of expanded capabilities and flexibility. These algorithms can pick up unexpected product abnormalities or defects, go beyond existing issues and uncover valuable new insights for manufacturers.