Festo
Canvas Category Machinery : Process Technology : Motion Control
Each and every day at Festo, we pursue the goal of making tomorrow’s working world more productive, simpler and more sustainable. That’s why we develop solutions for automation and technical education which prepare people, companies and organizations for the digital future in production, and for new technologies.
Assembly Line
PLCnext Technology: Festo and Phoenix Contact enter into strategic technology partnership
Festo, manufacturer of pneumatic and electrical automation technology, will use PLCnext Technology, the open ecosystem for modern automation from Phoenix Contact, in future intelligent devices. This will combine the innovative strength of both companies with the aim of taking industrial automation to a new level. The new product generation is expected to be launched at the end of the year. The integration of PLCnext Technology opens up a wide range of opportunities for Festo and its customers such as Openness and flexibility with PLCnext Technology is based on an open architecture that allows individual solutions to be developed and existing systems to be seamlessly integrated. Festo can therefore offer customized automation solutions for specific customer requirements.
The common goal of Festo and Phoenix Contact is to meet the requirements arising from the convergence of IT and OT (Information Technology and Operations Technology) in industry through open automation solutions.
Custom Machine Builder Develops Automation Solution That Increases Production Capacity Four-Fold
While the metal housing and circuit boards were made efficiently and cost-effectively in high numbers, adhering three plastic covers to each housing with a quality seal was slow and labor-intensive. When this electronic component was first introduced, low output was not a problem. As demand for the component increased, however, the bottleneck of plastic cover application became a concern. The manufacturer asked KAMP Automation to design an automated machine that would significantly expand capacity and ensure quality seals for the adhered plastic covers.
In the new automated process, the machine operator places two die-cast metal housings on fixtures and six plastic covers on a cover fixture (three covers for each housing). Vacuum holds the metal housings and plastic covers in place. Manually loading the machine made sense from a cost standpoint and only required seconds. Housings and covers in place, the operator starts the machine.
Deep Learning Boosts Robotic Picking Flexibility
Gripping and manipulating items of diverse shapes and sizes has long been one of the biggest challenges facing industrial robotics. The difficulty is perhaps best summed up by the Polanyi Paradox, which states that we “know more than we can tell.” In essence, while it may be easy to teach machines to exhibit a high level of performance on tasks that require abstract reasoning such as running computations, it is substantially harder to grant them the sensory-motor skills of even a small child in all but the most standardized and predictable environments.
Fetch.ai x Festo x University of Cambridge
We are incredibly excited to announce that we are collaborating with Festo and the Manufacturing Analytics Group at the University of Cambridge, Institute of Manufacturing (IfM), to provide research and recommendations to successfully develop a multi-agent system architecture for distributed manufacturing. With the use of our Fetch.ai technology stack, including the Autonomous Economic Agents framework and blockchain in synchronized harmony, our goal is to transform the existing manufacturing control systems, delivering a scalable solution for the 21st century and beyond.
Despite advancements in technology, the manufacturing industry remains rife with challenges and inefficiencies, lowering productivity, utilization, production variety. Distributed Manufacturing is a relatively new paradigm proposed to overcome some of these challenges. In Distributed Manufacturing, producers lease excess capacity for customized, low volume high variety orders. Whilst a promising approach to improve productivity and reduce wasted capacity, the take up of Distributed Manufacturing itself has been difficult. One of the issues is a lack of automated mechanisms to match suppliers and buyers. Firms need to spend manual effort to orchestrate matches, which are unlikely to outweigh the cost benefits obtained from a Distributed Manufacturing approach. Another issue has been the monopolization of economic transactions by platform providers, which results in suppliers having to succumb to pressure for reducing prices.
For years, multi-agent systems (MAS) architecture has been considered a possible solution to reducing the above issues associated with the conventional, centralized manufacturing orchestration. MAS offers a way to automatically allocate suppliers of services to buyers, without the associated manual transaction costs. It also allows for decentralized matchmaking, reducing the power of platform providers in suppliers. MAS take up has been slow due to a lack of suitable infrastructure. Ultimately, the missing link has been the application of cutting-edge research in AI and the connection with the blockchain technology that helps us understand the benefits that multi-agent systems can provide within the distributed manufacturing sector.
This collaboration will bridge these gaps, shedding light on the lack of current industry applications available to act as benchmarks to capitalize on the solutions multi-agent systems can provide to the distributing manufacturing sector.