"With the Internet of Things and a multitude of networked machines, more and more data is available. However, this data is worthless if it cannot be used in decision-making processes. It must therefore be processed and intelligently evaluated by AI applications as quickly as possible. Artificial intelligence can also draw comparisons with other processes, systems and their data and, by learning from experience, independently solve future tasks, avoid errors and optimize processes," explains Martin Heinz, board member of the iTAC Software AG.
The IoT structure therefore needs artificial intelligence, which in turn needs the Internet of Things as a source of data. iTAC also relies on this symbiosis and enables streaming and batch analytics on a central, scalable platform with out-of-the-box access to MOM data for predictable production. The iTAC.MOM.Suite takes advantage of the latest IIoT technology. The iTAC.IIoT.Edge software is a part of the MOM system (Manufacturing Operations Management System), but can also be used as a stand-alone solution and enables significant improvements in manufacturing processes in a short time. It can combine IIoT data with MES data to form flat data structures and analyze this data in real time. The data packages can also be transferred to other analysis or ML/AI tools used by the customer and ML models created on other platforms can be integrated.
"Machine learning and artificial intelligence-based applications in the field of analytics enable more sophisticated and higher-quality analyses than conventional technologies and algorithms. Artificial intelligence can, for example, find complex patterns in the data, draw conclusions and thus make predictions," explains Martin Heinz.
By using iTAC's edge solution, numerous ML/AI use cases can be developed for advanced and digitalized manufacturing, for example in the area of prediction. The corresponding monitoring of machine and sensor data makes it possible, for instance, to predict machine failures. Unplanned machine failures can thus be reduced by up to 70 percent. Another use case is the reduction of test efforts. Most SMT lines with AOI struggle with a high rate of false calls. AI can be used to accurately differentiate between real defects and false calls. This reduces the need for manual verification by operators by up to 60 percent, with associated time and costs. The result is higher throughput while supporting zero-defect production.
The possibilities are numerous. iTAC offers a use case library that enables customers to implement applications quickly and easily.
