Achenbach has developed a business model, a product, from the data: Achenbach OPTILINK – in the first instance a cockpit or analysis tool for customers worldwide. Customers can query the current status of their machines via a web interface. Achenbach provides the customer with a basic set of analysis tools, but customers can also create and perform analyses themselves.
However, Feist and his colleagues are still not satisfied. Artificial intelligence is the name of the game, and not just with the focus on ‘deep learning’ as strongly promoted by Google. Achenbach employs ‘unsupervised machine learning’ in many solutions. The idea behind this is that the rolling mill tries to identify patterns in the data that deviate from the unstructured noise. Ideally, this will enable a recommendation for action – such as ordering a spare part from Achenbach – to be given to the operator. Linking the OPTILINK system to an electronic spare parts catalogue was one of the first enhancements to the software package. In line with this OPTILINK can trigger defined work processes in the company’s ERP system via ‘tickets’, thus connecting the work of humans and computers.
To be able to maintain a high development speed for ML algorithms, the developers at Achenbach have integrated three tool packages into their portal. One component is based on ‘Matlab’ and observes production processes, while another was created with ‘Rapidminer’ and analyses incidents (such as strip tears) on the machines. For certain forecasting models, however, neural networks based on ‘TensorFlow’ were used. The next goal is to automate the decisions that are, in most cases, currently still made by humans on the basis of this information. Many of the ML algorithms have been known for years, but the correct data filtering and selection in relation to production data is still largely untested. “We’re working on it” is all that Feist is prepared to say on the matter. He doesn’t want any AI hype.