Bleck spent five years developing MTMS technology, which he describes as a “repeatable, safe, fast, accurate, and low-cost alternative to current methodologies.” It is now available to plastics processors. For more information, visit schmitprototypes.

Oracle’s IoT cloud service can automatically pull Noble Plastics’ production data from its molding machines, and then use anomaly-detection algorithms to help monitor machine health, maintenance requirements, and parts quality.

Noble Plastics uses Oracle Mobile Cloud Service to extract information from Oracle IoT Asset Monitoring Cloud and send an alert to the appropriate engineer or line operator in the event of a problem. If there’s oil on a pump that’s getting too hot, for example, the production manager doesn’t need to know about it, Rogers says, “but the maintenance tech does.”

Don’t make the mistake of thinking the temperatures shown on the controller screens are actual, he cautions. “Those thermocouples are reading steel temperatures along the barrel, not melt temperatures.”

The MTMS method is based on an insulator cup that keeps the material in a molten state during the measurement process. It also precisely positions the thermocouple probe in the effective flow path. The process is shown in the embedded video. In addition to its low cost and ease of use, Bleck cites the following advantages compared with conventional technologies:

The company’s 10-year-old asset-monitoring system was supposed to text somebody if a machine went down, but every once in awhile that system stopped working. “There would be a power glitch or our servers would lock up,” says Noble Plastics technology director Scott Rogers. When the system failed, Rogers says, his team was flying blind, risking excessive damage to equipment and large batches of unusable parts. “These events were having a negative effect on our business, causing excessive waste and production delays,” he says.

Today, the Oracle IoT cloud service can automatically pull Noble Plastics’ production data from its robots and molding machines, and then use anomaly-detection algorithms to help keep tabs on machine health, maintenance requirements, and parts quality.

The importance of complying with resin supplier recommendations for melt or process temperatures, which MTMS facilitates, cannot be overstated, according to Bleck. If the temperature is too high, the polymer will degrade; too low, and the part will be difficult to fill, pack out, and maintain a good appearance, explains Bleck. “Melt temperature influences the plastics viscosity or resistance to flow (thick or thin) and is critical in obtaining optimal part dimensional control. Consistent viscosity allows for repeatable filling of the mold with consistent cavity pressures. This correlates to less shrinkage variation and a tighter bell curve on normal production variations. Hence, molding with the same melt temperature generates higher quality parts from lot to lot and press to press,” says Bleck.

The company’s engineers now spend more time analyzing problems and designing new products, he says. “If life is better at Noble, it’s easier to keep employees and retain customers,” Rogers says. “And what could be better than that?”

Before moving to the cloud, Rogers’ team had to actually make a bad part to determine that there’s a problem, then fix the problem and rebuild the production schedules. “It created nothing but chaos for everyone,” he says.

Using infrared cameras to measure melt temperature can achieve good results but requires sophisticated equipment and adherence to a strict procedure. It also can be expensive and only measures the surface, adds Bleck.

The impact was particularly severe during the night shift, when the company’s engineers and machine operators were at home and the robots ran the shop floor. Problems with the old monitoring system kept surfacing. “I just didn’t trust it anymore,” Rogers says.

Productivity gains made possible through industrial automation and the so-called Fourth Industrial Revolution have given small manufacturers such as Noble Plastics an opportunity to compete on a massive scale.

Various methods are currently used to measure melt temperature, but all of them have drawbacks, says Bleck. “There is the standard purging of resin into a puddle and inserting a thermocouple probe, stirring the puddle, taking three shots, and calculating the average,” he explains. “This method has inconsistencies due to skinning of the polymer on the probe and human measurement errors. It also takes a significant amount of time, which fouls up a steady state process.”

“I was having a particularly challenging day when I got a call from Oracle,” says Rogers, who notes he’d received several calls from the sales rep before but hadn’t been receptive. “This time I was more open-minded: Maybe Oracle had something that could help.”

“We had no experience with Oracle,” Rogers says. “But my impression was that Oracle was too big, too expensive for us.” But its pay-as-you-go cloud service is affordable, he says, and Oracle has committed a lot of time and resources to “listening and assisting us through the testing and initial use of the IoT cloud product.”

The Oracle saleswoman explained that Oracle Internet of Things Insight Asset Monitoring Application was helping other small and midsize businesses monitor their industrial equipment remotely, across multiple facilities, without requiring any onsite machine operators or servers.

Editor in chief of PlasticsToday since 2015, Norbert Sparrow has more than 30 years of editorial experience in business-to-business media. He studied journalism at the Centre Universitaire d'Etudes du Journalisme in Strasbourg, France, where he earned a master's degree.

Oracle Mobile Cloud’s routing feature is important because it helps reduce organizational stress. “I don’t need another alarm,” Rogers says. “If I had an alarm for everything that didn’t go quite right, I’d be responding to alarms all day.”

“These KPIs are critical,” Rogers says. “The power of machine learning is being able to analyze millions of product and machinery characteristics, predict if a machine or process is going to break down, and then help us avoid making bad parts.”

Not wanting to put stress on the organization by making people work the graveyard shifts (and not wanting to hire and train more people), the company turned to the cloud.

The Louisiana-based contract manufacturer of injection molding products has long been aware that simply automating its processes without integrating the systems that monitor them can result in defective parts and expensive rework.

Fill speed, pack pressure, cooling rate, and melt temperature are the four critical process variables of injection molding that control the quality and consistency of molded parts. The first three variables are relatively easy to duplicate when lots or colors are changed or machines are switched. Not so with melt temperature, which can remain a mystery that few processors have the time, knowledge, or equipment to solve, according to Rich Bleck, a 48-year veteran of the plastics industry. He has developed a proprietary system that precisely measures the melt or purge temperature during the injection molding process. His company, Procon Training & Consulting LLC, has partnered with Schmit Prototypes to manufacture and market the Melt Temperature Measurement System (MTMS).

“Other methods use an insulated cup to catch the purge and insert a thermocouple probe. This method has significant issues, such as the size of the thermocouple and the technique of the operator,” says Bleck. “It may require multiple shots if it is a small shot size. Therefore, rpm, back pressure, and residence time are not factored into the melt temperature. Moreover, all three of these methods are very difficult to use on small shot sizes,” says Bleck.

Current methods for measuring melt temperature during injection molding are often inconsistent or expensive. The Melt Temperature Measurement System promises to deliver precise, repeatable results in a low-cost package.