Engel made a splash at K 2025 when it introduced the first autonomous injection molding cell, powered by Inject AI, Engel’s evolution of the now-ubiquitous Inject 4.0. The new AI system analyzes more than 1,000 parameters in real time, detects deviations and gives recommendations for corrective action.
“We are showcasing the world’s first industrial solution for an autonomous, self-regulating injection molding cell,” said Stefan Engleder, Engel Group CEO. “The machine autonomously produces high-quality parts with AI support.”
There is not much difference today in the injection molding machines, extruders, thermoformers and blow molders I saw when I first visited processing plants more than 35 years ago. The hardware looks strikingly similar.
But the software and control systems have seen huge changes and improvements. That is how machinery companies have differentiated themselves from competitors.
So adding AI to control systems is a next natural step in machinery evolution.
Machinery builders at the K show seemed to be thinking about AI for three processing functions: Training and capturing institutional knowledge from retiring employees; improving machinery maintenance programs; and troubleshooting processing problems. Most were not as ambitious as Engel and there was not much talk of AI-powered machines making autonomous decisions on the plant floor.
“Probably one of the bigger things that we’re going to see in our lifetime is AI troubleshooting, maintenance programs and things like that,” industry veteran Randy Wendling, director of aftermarket operations at Absolute Haitian, told Senior Reporter Karen Hanna for one of her stories on machinery maintenance in this issue.
Improving a maintenance program seems like low-hanging fruit for AI. It can analyze the data collected by machine sensors, compare it with baselines and in seconds tell an operator what is likely happening, when it will likely happen and what service is needed. And it can provide a parts list for the service.
Michael Duff, director of business development and aftermarket sales for auxiliary equipment maker ACS, said, “Our goal is practical application of these tools to simplify maintenance and improve responsiveness, not technology for its own sake.”
Forward thinking processors are already embracing AI as another tool to improve production efficiency.
Oxmaint AI, which provides industry-specific AI maintenance solutions, said AI-powered maintenance systems can reduce machine breakdowns by 70 percent, reduce overall maintenance costs a 25 percent and lead to faster repairs.
It is surprising that there is still a reluctance to embrace AI-powered solutions. Rockwell Automation, in its “2025 State of Smart Manufacturing” report, said that 95 percent of manufacturers surveyed said they have invested or plan to invest in AI machine learning, generative AI or casual AI in the next five years.
The report, which surveyed 1,500 manufacturing executives worldwide, found that introducing AI and automation were most often cited — about 41 percent for each — as part of their strategy to address their skills gap and labor shortages.
The plastics processing industry often lags behind other industries in adopting big changes. Five years ago, Rockwell said more than 80 percent of AI use cases focused on predictive maintenance. Now, the primary uses, particularly in large companies, focus on quality control and cybersecurity and on creating efficiency and streamlining processes.
It looks like the plastics industry is just coming out of the AI starting gate while some manufacturers are already going into the second turn.
Using all the best tools available seems to me to be part of the definition of smart, efficient manufacturing. How do you assess your operation?
If you roll your eyes and tune out when someone mentions AI, you are not doing any favors for your business.
Maintenance is a good starting point for your AI journey. The maintenance stories in this issue explain why that is true and how to get started. Give them a read.
