How the Industrial Internet of Things (IIoT) Is Improving Injection Molding

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The evolution of industrial IoT (IIoT) technology continues to add to the highly efficient process of plastic injection molding in manufacturing by increasing productivity and potential business profits. Plastic injection molding can be used to make millions of nearly identical copies of products from a single mold.

With the increased use and development of IIoT sensors on industrial machines, plastic injection molding can further enhance business productivity and the quality of manufacturing.

Plastic injection molding uses pre-designed molds that are filled with heated plastic to create a part. The injection process is carried out by injection molding machines, and, while both the machine and the molds can be quite a hefty initial investment, there is a low cost of routine production. The novelty of injection molding is how fast parts can be made on such a large scale. Plastic injection molding also offers more consistent results with less leftover material.

However, the process isn't always perfect and errors in machinery can cost a business a lot of money and take valuable time away from production. Many defects and product failures can result from a machine supplying improper or inconsistent clamping force, injection pressure, shot volume, speed, and temperature.

But with the use of IIoT technology in injection molding machines, these issues can be quickly detected and even completely avoided to eliminate downtime and additional costs.

The IoT, or the Internet of Things, is a very complex and interconnected system of "things" that can include anything from devices to machines to people.

The first IoT device was created in the early 1980s. What makes these "things" a part of the IoT network are their embedded sensors and technologies that are connected to the internet. Sensors and software can collect data that will be sent, over the internet, to other devices within the IoT. The exchange and transference of data between devices allow for analysis and the device to take action based on the line of communication.

The IIoT, which was conceptualized after cloud technology entered the scene in 2002, has been referred to as the fourth wave of the industrial revolution, or Industry 4.0, due to its dramatic rise in application. For reference, in 2025, there is expected to be around 22 billion devices integrated into the IoT. This increase also coincides with the practicality of using and installing embedded sensors, as well as the evolution of its technology.

Though only around a couple of decades, this technology is evolving rapidly. Change first came with the reduction in sensor size. During the early stages of IoT integration in the '90s, these sensors were much larger and this made them quite impractical. Today, the sensors used in machinery are drastically smaller and much easier to house in most devices. With a fast rate of improvement, both in the shrinking of their size and cost, as well as a much quicker and easier installation process which made them more accessible and practical to manufacturers, the market for these sensors quickly grew.

Beyond the physicality of the sensors, IIoT technology has dramatically evolved as well. The presence of the cloud alone has made data more readily available to businesses, allowing fast data transfer that is more automated and doesn't require management. However, the most revolutionary addition in IIoT — for manufacturing machines specifically — comes with machine learning. This aspect is essential for the improvement and advancement of injection molding machines.

Microsensors are used on injection molding machines to collect important data, and, through data analysis, manufacturers can easily access insight into a machine's performance to improve the efficiency of the plastic injection process. The sensors can also aid in alerting manufacturers if there is an issue with the machine. In plastic injection molding, this can ensure that failed machines are off the production line and are not producing any items with defects. Not only does this support product quality but it can also save money.

After a failure has been alerted through the data transfer and analysis, a clear guide on how to address the failure can be provided. This makes maintenance both easier, quicker, and overall more efficient. With this advanced machine learning, these injection machines can foresee failures even before they occur and supply the needed information for regular upkeep to prevent future issues from occurring.

Thanks to these recent advancements in IIoT technology, sensors have become increasingly useful and practical for plastic injection molding manufacturers and provide the ability to maximize machine uptime and improve maintenance efficiency. These aspects, in addition to performance insights, can result in better quality products and leverage production efficiency to increase profits.

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How the Industrial Internet of Things (IIoT) Is Improving Injection Molding