Manual assembly procedures, and labor intensive tasks in production are particularly subject to human errors. These can lead to costly damages to equipment, undesired rework, quality issues at the product or even injury of the operators. Checklists and definition of standardized work procedures can partially mitigate the problem; however, these do not completely eliminate occurrence of undesired events. Hence, companies often need downstream quality control gates to address the problem, but these increase the complexity of the process chain, and also the costs.
Smart cameras offer the unprecedented possibility of sensing the physical world and automatically process information to generate meaningful insights in production. Thanks to state-of-the-art object recognition algorithms based on convolutional neural networks; smart cameras can be trained to detect the occurrence of particular events in production. The tightening of a screw, the correct application of labels or a specific sequence in assembly operations, can be automatically monitored. On the occurrence of undesired events, the operator can be instantly notified, and reminded to fix the issue right away.
In a practical implementation study, EMBRIO.tech developed a smart camera system to monitor the manual set-up procedure of heavy machining equipment. For this purpose, the system detects setup events and evaluates whether they occurred in the correct order. The events are detected based on the presence, position, and orientation of the relevant objects in consecutive images. The relevant objects are
The smart camera is able to automatically monitor the set-up procedure and verify whether the workpiece has been clamped correctly on the lathe, and whether it is safe or not to start production safely. Failure in clamping the part correctly can result in major damage to the equipment. The detection system comprise two individual smart cameras, for redundancy in case of an impaired view.
The smart camera algorithm has been trained with transfer learning using 650 distinct images from both cameras showing the tool and the part.
annual savings potential