Industrial image processing
The term Industry 4.0 describes a future vision for the digitalisation of industrial production. In this process, mechanical industrial production is linked with modern information and communication technology in such a way that a largely self-organised production is created: people, machines, plants, logistics and products communicate directly with each other. The operating and production data collected in this way make it possible to optimise not only individual production steps but also entire value chains.
Machine vision has a key role to play here, because it allows individual production steps to be documented in detail and analysed in real time with the help of image recognition software. The data obtained in this way can be used for quality assurance, process control and process optimisation.
At its core, the use of industrial image processing is about combining the cognitive abilities and expertise of humans with the precision, endurance and speed of electronic data processing:
The human setter of a machine vision system contributes his or her cognitive skills and expertise by determining the parameters to be detected and defining the plant's response to the determined parameter values and storing this process data. In this way, the machine setter's expertise is digitised, preserved and made transferable by machine.
The hardware and software of an industrial image processing system can read in the digitised process data and execute the process described therein at high speed, always with consistent precision and for a virtually unlimited period of time.
The described concept of industrial image processing is ideally suited when image acquisition takes place under reproducible, constant ambient conditions (especially the lighting situation) and the measured parameter values trigger clearly defined reactions of the system.
It reaches its limits when environmental conditions can change or human intuition, association or (adaptive) discretion is required when interpreting the determined parameter values. For these cases, the human plant installer would have to be able to foresee every possible situation and determine the correct reaction of the plant for it. Where this is not possible, artificial intelligence methods can be used to train industrial image processing systems.