Fraunhofer Institute for Production Technology IPT
In modern manufacturing, the acquisition of machine data and the use of sensor technology provide the key to greater efficiency and control of cutting processes.
Machine data acquisition means the collection and recording of data generated in the numerical control (NC) or programmable logic controller (PLC) during the cutting process. These include position signals, motor currents of the drive axes, spindle power and tool information. They provide us with information about the performance, status and operation of the machines.
Trends and developments in machine data and sensor technology
Data standardization and integration: Interoperability between different machines, sensors and software solutions is becoming more important in order to seamlessly exchange and process data between systems. Standardization of the various data formats, which are often still proprietary today, is essential for merging the collected data.
Adaptability: As workpieces and requirements can vary, the ability to easily adapt sensors and data acquisition systems to different machines and processes is crucial for merging the data.
Intelligent sensor technology: Sensors provide a wealth of information, for example on temperature, vibration, pressure, tool wear and much more. Intelligent sensor technology can often carry out and filter initial analyses on site in the machine in order to transfer only relevant data for storage and further processing.
Artificial intelligence and machine learning: Analysis techniques based on artificial intelligence (AI) and machine learning are used to recognize patterns in the collected data, detect anomalies and make more precise predictions.
Cloud solutions: Cloud platforms collect data from different locations and make it available for analysis. This is particularly helpful when companies have several production sites in different locations that are networked with each other.
Real-time monitoring and analysis: Companies that use cutting technologies can use real-time data to continuously monitor the condition of machines, tools and processes. Real-time analyses make it possible to identify problems at an early stage, reduce downtimes and increase the efficiency of processes and internal workflows within the company.
Energy efficiency and sustainability: Collecting data on energy consumption and other environmental impacts of cutting processes helps companies to operate more sustainably and document compliance with environmental regulations, laws and ordinances.