KEY INSIGHTS

Autonomous mobile robots (AMRs) are popular because of their fl exibility and ease of deployment. However, even when outfi tted with robotic arms, they still have some limitations. While ideal for moving items across large plant fl oors or warehouses, AMRs may still struggle with tasks such as picking, particularly if items are of diverse shapes or sizes, or are arranged in a non-uniform manner.

One female student, who is now enrolled in an engineering program in college, recognized that young girls needed to see role models that looked like them in order to pursue engineering as an interest. So, once a month, she would create a lesson plan and teach middle school students di erent STEM topics. This experiment is now part of the high school program.

Condition-monitoring technologies have become popular because they attach to the machine and communicate operating data to the cloud, where it can be analyzed and used to monitor equipment health, triggering an alert if abnormal performance is detected. These types of tools use artifi cial intelligence to take the guesswork out of predicting maintenance issues and delivering alerts as required, instead of someone investigating logs.

Arburg, a manufacturer of plastics processing machinery, created a custom gripper for its MultiLift robotic system that can gently remove injection molded parts with complex geometries without marring their surface. Arburg accomplished this by combining a hard housing with a soft membrane, which is connected directly to the robot arm.

For manufacturers who have started down the road of digitization, we are seeing edge computing as a major enabler for other IIoT technologies. Specifi cally, since many digital initiatives come from the IT side, we’ve had to fi gure out a way to bring IIoT technology to the plant fl oor in the least disruptive way possible. One area where clients are seeing success is deploying network infrastructure with edge computing technology embedded at the network switch layer.