Plastics machinery makers are incorporating more smart manufacturing and AI technologies into their offerings, helping processors with everything from troubleshooting equipment and optimizing processes to automatically adjusting molding parameters and detecting potential problems before they lead to downtime or scrap.

The improvements, many of which are still under development, rely on accurate process data collection.

“One thing that we have learned about AI is that it is all about data, but what do we do with that data?” said Jason Long, vice president of sales for Wittmann USA.

Smart technology can optimize machinery operations

Given the shortage of skilled workers, AI is increasingly being called upon to make continuous, automatic adjustments to the molding process, reducing the need for human intervention.

“I’m sure you hear the same as I hear from a lot of molders in the USA — one of the biggest issues that keeps them up at night is a skills shortage,” said Martin Baumann, president Americas and CEO of Arburg Inc. “This is a challenge as well as an opportunity for machinery makers like Arburg.”

With less skilled labor, many injection molding operations might not be able to fully take advantage of the capabilities built into the machinery they purchase, Baumann said.

“The opportunity for us is to make the utilization and accessibility for day-to-day operations as easy as possible,” Baumann said. “By making the machinery and associated auxiliary equipment easier to integrate, exchange data and operate, we can address that issue.”

Arburg's machinery has long collected extensive process data but making that information accessible and easy for operators to act upon has been challenging.

Arburg plans to highlight its smart offerings during Fakuma 2026, which will be held Oct. 12-16 in Friedrichshafen, Germany.

Haitian, with the introduction of its fifth-generation injection molding machines (IMMs), has made a suite of artificial intelligence (AI) software a standard feature.

“It does a litany of things to help molders keep the process stable without any human interaction,” said Ben Hartigan, Absolute Haitian marketing manager. “It also helps with energy savings and overall helps the adaptability of the machine with different materials, and if there’s a change in the actual resin quality, it can make slight adjustments to maintain injection stability.”

KraussMaffei’s APCPlus, which the company calls smart machine technology, compensates for external influences on molded part quality, such as viscosity changes, according to the company. It automatically adjusts the IMM to achieve consistently high-quality parts. It can also reduce material use, energy consumption and cycle time.

Ranjith Pola, KraussMaffei director of digital solutions and controls, called APCPlus “cruise control for the injection molding machine process.” APC Plus maintains process stability by automatically adjusting based on tolerances set by the user.

Wittmann Group is partnering with AI vision system maker Krevera on a robot that picks up a molded part and allows a camera to inspect it. The results are communicated to the IMM, which can autonomously adjust its operating parameters to correct a defect. It plans a demonstration at NPE2027 in Orlando, said Jason Long, vice president of sales for Wittmann USA.

Vision systems pair with AI for quality control

Wittmann is not the only primary equipment manufacturer pursuing such a strategy for inspection.

Uniloy has developed Uniloy Eye, a camera system that integrates with a blow molding machine’s programmable logic controller (PLC). It can incorporate cameras monitoring the mold area, parison/extrusion area, blow pin/nozzles, take-out area, accessory equipment area, scrap area, and any other area specified by a customer.

“It is available on new machines from Italy (shuttle, injection blow, and industrial machines), and it’s also going to roll out for machines built in the U.S.,” said Chuck Flammer, Uniloy VP of machine sales.

The cameras, coupled with AI in the near future, will increasingly help operators monitor the diameter and shape of the parison, as well as the temperature and consistency of cutting. The cameras can detect a bottle stuck inside a clamp, a leading cause of mold damage, said Daniel Horecica-Csiki, Uniloy shuttle product line manager.

Uniloy has partnered with an industrial AI software company to develop the technology.

Predictive, preventive maintenance develop rapidly

Several companies are using smart technology to identify maintenance needs before a breakdown happens.

KraussMaffei’s liveCare continuously monitors wear on components such as screws, helping plastics processors implement condition-based maintenance strategies by determining the optimum service life of parts. The company’s flagship smart factory platform, socialProduction, is a production monitoring tool. It can measure and log energy consumption, looking for anomalies. SocialProduction can also alert customers when a machine stops, an alarm is triggered, or operating parameters move outside normal limits.

Uniloy hopes to offer preventive maintenance options in the near future. It is working with the previously mentioned AI software company to develop preventive maintenance models that would trigger machine intervention or operator actions to correct processes that are drifting from optimal, Flammer said.

“Since it is an AI package, the programming is minimal, but to determine which variations need to be corrected is what we are looking into,” Flammer said. “How to automate the correction is our next step, so this is a continuous process.”

Operator assistance, training right on the machine

With plastics processors finding fewer experienced equipment operators, several companies are building AI tools that make expert knowledge readily available.

Arburg has introduced its “Ask Arburg” AI chatbot that searches the company’s knowledge database to produce answers that match or nearly match a user’s specific question about the operation of its equipment, saving time in troubleshooting problems.

Wittmann is developing Aim4Help, an AI knowledge database to help customers troubleshoot equipment.

“Our customers will be able to scan a QR code when they have an issue with a machine or robot or another piece of equipment,” Long said. “It will show them the alarms and have suggestions on how to fix them.”

The tool is still in development, but Wittmann’s own service staff use it extensively and select customers can access it.

At K 2025, Reifenhäuser launched its NEXT system, which integrates a chatbot to help inexperienced line operators quickly solve complicated tasks. It combines AI technology with Reifenhäuser’s expertise and live production data.

In addition to the chatbot, NEXT.AI, the system includes NEXT.Learning, a combination of on-site training and a digital learning platform that helps users retain staff expertise and make it available to new employees through AI avatars; and NEXT.Data, which automatically aggregates production data and displays it on dashboards designed for the plastics extrusion and packaging industries.

Reifenhäuser said studies show that the use of industrial AI has the potential to improve OEE by up to 15 percent and reduce waste and downtime by up to 20 percent.

Bay Plastics Machinery (BPM), which builds pelletizing systems, incorporated a training function into its S.M.A.R.T. Control.

The control stores machine maintenance and troubleshooting documentation, including training videos and drawings. A 3D model of the specific machine is stored on the machine and can be used to identify components and part numbers, potentially making it easier to order replacement parts.

BPM’s S.M.A.R.T. control also monitors and analyzes system performance and stores recipes.

Housed on BPM’s pelletizers, its recipe storage reduces the skill needed to run a process, since operators can save and recall settings instead of manually adjusting them each time.

“Rather than having to turn a bunch of manual knobs and dialing it every time you’re on the process, you can pull that information up and run it exactly the same way every time,” said Eric Misiak, electrical controls engineer at BPM.

BPM’s S.M.A.R.T. (System, Monitoring, Analysis, Recipes and Training) Control not only controls the pelletizer, but it also controls downstream auxiliary equipment. Depending on the configuration, that can include water baths, air knives, dryers, water slides, blower skids, conveyors and pump skids.

Haitian’s HT-Xtend AI software on its IMMs is designed to help less experienced machine operators. The AI-driven controls automatically adjust molding processes to improve stability and reduce the need for worker intervention.

“The management side is asking for this sort of technology because a lot of these guys just don’t know,” Hartigan said. “They don’t have 20 processing guys running around their plant for babysitting each of these machines.”

While many customers are asking for AI assistance, plastics processors have the option to disable it.

“If you have some old-school guys who don’t want to do all this and they just want to set their exact parameters themselves, that is totally fine,” Hartigan said.

Production monitoring delivers insights

Smart and AI-enabled software gives processors broader visibility into what’s happening across their plant or plants.

Arburg ALS, a manufacturing execution system developed by the company for injection molding production, centrally stores the data records of all machines. A major advantage of ALS is that the data can be recorded and analyzed in real time. Key performance indicators, metrics, and production reports help processors adapt quickly in response to changing conditions, according to the company.

KraussMaffei’s dataXplorer is a high-resolution data recording and analysis tool for injection molding machines and extruders.

“With dataXplorer, you can get thousands of data points from the machine, and at a higher frequency,” Pola said. “With this data, what customers can do is amazing.”

Process engineers can use the data to optimize equipment performance. While dataXplorer does not perform predictive maintenance analysis itself, it supplies the high-resolution data needed by third-party predictive maintenance software, Pola said.

Haitian’s Go Factory MES integrates with IMMs across a company’s operations and provides real-time monitoring, alerts, production data and downtime tracking.

“It’s pretty much a mobile app or a computer program, and it fully monitors your machine, your process,” Hartigan said. “It tracks all your molds, everything you’d want. You can get alerts on your phone. You can see when the machines are running, when they’re not running. You can track downtime, uptime, part counts, everything you could think you’d want to track in your factory.”

Energy optimization cuts usage

KraussMaffei’s socialProduction includes a module that measures and logs energy consumption and looks for anomalies. Its APCPlus system reduces energy use by tightening process control, according to the company.

Wittmann’s Imago module for its TEMI+ MES software offers detailed analysis of a customer’s energy consumption and monitors usage in real time across the production floor. It helps processors identify where operations could be more efficient.

Haitian’s HT Energy feature provides a power meter that details electric usage on hydraulic and electric machines. Its HT OptiForce feature, available on electric machines, minimizes power usage by avoiding excess clamping pressure.

As AI quality evolves, plastics processing operations are moving toward adapting to fewer workers and smarter machines.