Tools You Can Use Today to Build the Plastics Plant of Tomorrow
Advanced technologies such as artificial intelligence (AI), Big Data and high-tech sensors are changing the dynamics of plastics processing by providing tools that learn from each other and make decisions to improve processes.
Plastic processing equipment with automated technology enables the equipment to communicate and process data with little or no human intervention. These embedded systems integrate operational technology and information technology to monitor and control physical processes.
A joint study by the Manufacturer’s Alliance for Productivity and Innovation and Deloitte published in September 2019 found that more than 85% of industrial manufacturers believe that smart factory initiatives using advanced technology will be the most important driver of manufacturing competitiveness in the next five years will be. IDTechEx Research estimates that companies will spend more than $250 million by 2032 for industrial applications of printed and flexible sensors.
Observation of material, process behavior
One example of advanced sensor applications in plastics processing is SensXPERT technology from Germany’s Netzsch Process Intelligence. The technology analyzes material behavior with in-mold sensors that enable dynamic, adaptive production by responding to material deviations, the company said. Customers can connect sensXPERT to existing manufacturing and control systems using standard interfaces, or use it as a cloud-based service.
Image courtesy of Netzsch Process Intelligence
|SensXPERT technology analyzes material behavior with in-mold sensors enabling dynamic, adaptive production.|
“The sensXPERT package is designed to handle each machine through modern industrial interfaces such as OPC-UA, PROFIBUS, PROFINET and – in the case of retrofitting older machines – analog and digital inputs/outputs,” SensXPERT Managing Director and CTO Dr. Alexander Chaloupka tells Plastic Today. “The hardware connection is usually the easiest process and the end-to-end communication must be set up within the software to enable sensXPERT and machines to talk to each other.”
The system includes several pieces of hardware, including two dielectric sensors installed in the mold and an Edge Device, which is hardware outside the press that collects data measured by the sensors inside the mold and processes parameters of the press itself.
Simulate, predict, analyze
The Edge device assesses hardware and software to produce models that capture small deviations in material and process. The resulting algorithms simulate, predict and analyze material behavior on individual machines. The algorithms’ ability to analyze large amounts of data flowing in from forms in real time helps the system get smarter over time. If the algorithms flag a set of data as different, machine learning software alerts the technician monitoring the press that something is changing. This allows the operator to decide if action is required.
Key parameters such as glass transition temperature, pressure and curing requirements “train” these process models, which are continuously refined. This data-driven model is designed to improve the quality and efficiency of manufacturing processes.
SensXPERT said its technology will work with a wide range of materials – including thermosets, thermoplastics and elastomers – and methods – injection, compression and transfer moulding; thermoforming; vacuum infusion; and autoclave curing. A web application allows users to access the system remotely.
“Historically, we’ve grown our business with thermoset,” Chaloupka said Plastic Today. “Due to the complex chemical reaction, there is a great demand for process transparency and automatic control to avoid scrap production and to work at the limits of the process. Within thermoplastic processing, similar to what we observe during the solidification of thermosets, we can see the crystallization and temperature behavior in the mold and use third-party sensors to also measure the pressure in the mold.
AI lets operators see into the future
Trying to manually identify a plastics processing problem requires a focused team effort that pulls data from many different sources, including oil analysis, vibration analysis, sensor data, and electrical testing. That data needs to be analyzed, which is a reactive exercise that is labor intensive, tedious and time consuming.
Using AI to collect and analyze the data in a multivariate analysis can reduce the time it takes to discover problems, according to Dominic Gallello, CEO of SymphonyAI Industrial. Instead of reacting to a problem, operators can examine the data in real time, which is more beneficial than looking at the data. AI’s ability to assess a variety of factors and provide a comprehensive overview of operations and processes also gives operators the vision they need to see into the future, he said.
A key aspect of the factory of the future is the increasing use of sensors that can measure every aspect of plastics processing, including temperature, vibration, speed, duration, pressure, proximity, smoke and humidity. “But data is not enough,” Gallello wrote in a recent Plastic Today article. “It requires AI to collect, analyze, match and assess the data to deliver meaningful and actionable insights. Together, AI and wireless sensors can collect and analyze enough data over days, weeks, and months to accurately predict hard-to-detect gear wear, early bearing wear, and other critical failures before they happen. They can make precise predictions using multi-dimensional models that far exceed what engineers can do with univariate models. And as advances in computer technology, sophisticated AI and machine learning provide more accurate results with consistent data, it also significantly reduces the burden on humans to act as detectives.”
Processors apply only about 2% of available data
Plastic processors are not exploiting approximately 98% of the data available to them about their operations, argues Gallello. Using sensors, AI, physics and embedded domain expertise, they can better understand their operations and processes.
While standard in-line sensors are built into most plastics manufacturing machines today, they are not always sufficient to achieve the highest levels of process optimization benefit, according to Prashant Srinivasan, Director of AI Products at SymphonyAI Industrial.
“As melt pressures exceed 150 MPa and temperatures are often above 300°C, in-mold pressure sensor elements are exposed to harsh conditions in a corrosive and abrasive medium,” said Srinivasan Plastic Today. “A number of new technologies, such as wireless thin-film piezoelectric sensors, are now available in the market. For temperature sensing, standard shielded thermocouples are subject to significant delay, making it desirable to consider installing IR-based temperature sensors. Similarly, for online quality measurements, advanced AI-based automated vision systems are now available and can be installed to achieve the highest quality control capability.”
No-code robot programming
Sepro Group, a leader in injection molding machine and robot integration, is now working on “no-code” programming, with a robot controller that uses AI to optimize trajectories and manage obstacles.
At K 2022 in Germany, Sepro allowed stand visitors to reposition a simulated mold and other peripherals and then challenge Sepro’s AI solution to calculate the best possible trajectory depending on which of three primary goals – maximum energy savings, minimum wear and tear or fastest cycle time – is selected. The system calculates ideal trajectories before the cycle starts, without any operator-written code.
Image courtesy of Sepro
|Interactivity was a key focus for Sepro at K 2022 in Düsseldorf, Germany, in October. Shown here is a demo cell that gave visitors a chance to choose the best human machine interface to steer a Sepro S5-15 Speed robot through a series of motions.|
The energy saving mode can reduce energy consumption by as much as 25% on certain tracks, which is ideal for processors looking to reduce their carbon footprint. The minimum wear mode reduces stress on system components, extending their life and reducing maintenance.
Also at K 2022, Sepro’s new centralized control software, Visual+, was at the center of a multi-step production process with a 110-ton Milacron injection molding machine and two Sepro robots. The process included assembly of toy sailboat components, inkjet printing, production data collection, dimensional checking, tray packaging and delivery of the finished boats to stall visitors using an autonomous mobile robot.
The new controller uses an open communication system that enables better synchronization of complex movements, integrated peripherals, data management and traceability. It can also seamlessly communicate with almost any brand of casting machine or secondary unit, the company said.
As an open system, it interfaces with the controls on molding machines and peripheral equipment using a single centralized and intuitive human-machine interface for more intuitive machine operation and an improved user experience, the company said. It can collect large amounts of data from all connected systems, which can be used for process optimization, traceability and analytics to calculate overall equipment efficiency and other metrics, locally or in the cloud.
Don’t be left behind
Using sensors, AI, multivariate analytics and machine learning, manufacturers can harness the power of large amounts of data to build models to predict product quality as a function of process conditions and settings. These models can be used to optimize and recommend settings for a given product to achieve the optimal quality and avoid rejections. They can also automatically learn from new data and adapt to aging machines and changes in operating conditions. Manufacturers who can effectively use these high-tech tools will achieve new levels of efficiency.