The Industry 4.0 era of manufacturing depends so heavily on data-driven precision that artificial intelligence (AI) is playing an increasing role in harnessing that data to enhance the performance of machines — including injection molders.
AI in manufacturing encompasses an array of technologies that allow machines to perform with intelligence that emulates that of humans. Machine learning and natural language processing help machines approximate the human capacity to learn, make judgments, and solve problems. Data-enhanced efficiency keeps processes moving faster and more cost-effectively.
AI increasingly important as processors turn to automation
“AI is becoming increasingly important in mechanical engineering, not least because of the need to automate injection molding processes efficiently and flexibly despite ever smaller batch sizes and shorter product life cycles,” said Werner Faulhaber, Director of Research and Development at Arburg. “Application examples of AI include automatic programming of robotic systems, targeted malfunction remedying, and a spare parts system with ‘intelligent’ image processing. Arburg is working on making injection molding more intelligent, step by step — ensuring that the machine continuously learns, keeps itself stable, and can even optimize itself in the future.”
Arburg forms flexible — and controllable — production systems by combining machines, automation, and proprietary IT solutions. The company’s Gestica control system, with its intelligent assistant functions, is integral to those systems. “All Kuka six-axis robots, for example, have been equipped with the new Gestica user interface as standard,” Faulhaber noted. “This simplifies programming, as well as the monitoring, storage, and evaluation of process data.”
One application Arburg is working on is the automatic programming of its Multilift linear robotic systems. “The idea is that the operator simply enters the destination, as with a car navigation device, and the system automatically calculates the optimal route. For robotic systems, this means that the operator simply enters the desired start and end positions, and the control system takes care of the rest.”
Wittmann Battenfeld, which has fully embraced Industry 4.0 connectivity across its portfolio of injection molding and auxiliary machines over the past several years, employs AI with its robots to monitor cycle times and control robots’ speeds outside the molding machine.
The company’s machine-learning capabilities — HiQ Flow and CMS technology — will be on display at this year’s K show on Oct. 19 to 26 in Düsseldorf, Germany. The speed of ROI can be as short as a few cycles with HiQ Flow, and the software can often be retrofitted to older injection molding machines equipped with a B8 machine control. A CMS Pro version will be available at a later date.