Industrial automation company, Beckhoff Automation, explains the importance of artificial intelligence (AI) powered tools in developing manufacturing solutions. The company’s TwinCAT 3 Machine Learning Creator TE3850 series is an example, integrating AI and machine learning into its software.
The machine learning process involves three key steps – data collection, model training, and integrating the model into control systems. The TwinCAT 3 Machine Learning Creator allows manufacturers to conduct this process in-house, without out-sourcing for data science and AI specialists.
Beckhoff Automation project manager, Ben Harrison, said that as OEMs, machine builders, or process engineers, companies have access to a wealth of data – from temperature, pressure, vibration and torque readings to production counts, downtime, and user interaction.
“All this data is readily available through Beckhoff’s control system and sets the stage for effective machine learning applications. You have the ability to own your data and process knowledge,” said Harrison.
“The tool accelerates project development through a transparent process that covers development, testing, and validation of the AI models. It supports the use of models in third-party environments via the ONNX open standard and facilitates automated report generation for auditing AI model creation.”
The TwinCAT features a no-code development platform, which enables non-AI experts to develop high-quality AI applications. It automates the process of AI development to make it more accessible for Beckhoff’s clients.
“Such features allow these companies to implement advanced technologies without the need for extensive resources or specialised knowledge,” said Harrison.
“The true potential of these advancements in machine learning can only be realised when they are in the hands of domain experts. Therefore, it is crucial that we reduce the complexity and the level of AI expertise required to leverage this technology effectively.”
The TwinCAT is also useful for AI experts, allowing for a streamlined workload and minimised errors. Potential applications include AI supported image processing for quality assurance or classification, or replacing traditional sensors with AI model outputs.
