Issue link: https://resources.mouser.com/i/1499865
Molex 2023 25 Collaborative (with Robots) The nature of collaboration is also evolving, with humans collaborating not just with other humans but also with robots. The goal of using machines and robotics in industrial environments has historically been to shift heavy, repetitive, and dangerous tasks away from humans to mechanical and automated systems. Collaborative robotics (cobots) expands these use cases, however, to include: • Skills that require years of training for humans to become proficient. Welding is one such skill that often requires more than 100 hours of classroom time and three to four years as an apprentice. Even then, human welders typically cannot match the quality and consistency of welds that robots can make. For example, even most highly skilled welders can weld a seam of only 60cm in one continuous motion, and the starts and stops on the seam can affect the overall quality of the weld. In contrast, say a robot in the factory can weld a 121cm seam in one continuous motion, producing a higher-quality weld. A collaborative approach in this scenario would have the robot completing steps that require skills and might affect quality. The human would do the tasks that are more intuitive to humans, such as setting up the process and handling exceptions. The idea here is to use the best of both worlds to achieve maximum efficiency. • Tasks where robots can do part of the work. Imagine a receiving and sorting department that handles components that are fragile, sensitive to handling, or require a clean room. In these instances, using a cobot to open boxes of various sizes and shapes would pose design challenges. Additionally, because the tasks are intuitive rather than calculated, using cobots may be less efficient than having humans perform the tasks. In contrast, a cobot could easily handle and sort the sensitive components after a human has opened the box—perhaps doing so faster and more consistently than humans. Resilient Resilience is about enabling systems to adapt quickly when variations in product manufacturing are needed. For example, at the start of the production run, someone sets up the system to manufacture a product. When variations of the product are needed—a different color, size, fabric pattern, module, or packaging—in less resilient systems, someone would need to manually change the setup to accommodate these variations, leading production downtime and higher labor cost. Resilient systems can withstand such unforeseen adversities and recover quickly. Ideally, resilient systems account for possible variations that the system might encounter, enabling the system to adapt with as little downtime and human intervention as possible. From a design engineering standpoint, it's easy to get trapped in the idea of developing complex systems that can do many things and account for even rare scenarios. The more complex the system, however, the more opportunities for failures. A simpler system that handles 80% of production scenarios might be better than a more complex system that handles 90% of scenarios. Designing for resilience is about finding a balance between simplicity that requires more human time in the loop and complexity that has more failure points and potentially affects other production areas.