Motivation
The number of new disruptive technologies with expected global impact is growing on many fronts. Technologies such as Advanced Robotics, big data analysis, the Internet of things or 3D printing are listed as drivers that will change “life, business and global economy”[MaChBuDoBiMa2013]. Looking into the past and from a manufacturing point of view, the advent of such disruptive technologies, such as CAD/CAM or internet, only fulfils its potential when the overall management practices adjust to the new reality. Therefore, there is the need for an integrated development of these advanced manufacturing technologies with the development of new multi-level decision-making tools. This is the main focus of this project. Recent developments on Robotics are expected to change the landscape of the manufacturing processes. Robots are nowadays interacting closely with humans, are
Recent developments on Robotics are expected to change the landscape of the manufacturing processes. Robots are nowadays interacting closely with humans, are programmable-by demonstration, easily pluggable in any point of the shop-floor and capable of interacting in spaces designed for human use. This paradigm change has strong implications on the development of future manufacturing decision-making tools.
Objectives
The main objective of the DM4Manufacturing project is the integrated development of manufacturing decision-making tools aligned with the efficient use of advanced manufacturing technologies to address upcoming challenges in the high-mix high customization industry. To fulfil the potential of Advanced Robotics in these scenario there is the need for agile real-time decisions integrated in Adaptive Production Systems. The project will pursue the following multidisciplinary challenges:Challenge 1 Optimal automation levels
Challenge 1 Optimal automation levels
Plant managers and engineers in general struggle to adjust the level of automation of production lines to product variations and market demand: especially when robots are involved, updates are slow, complex and cost-intensive, and the loss of productivity is considerable due to a stopped manufacturing line. The most common solution is to keep the level of automation deliberately low to guarantee the fast updatability of the system. Another solution is to simply design the manufacturing lines from the start for the maximum estimated customer demand, but this means high initial investment and low equipment utilization during large parts of the product cycle and a huge difficulty to change the product.
Challenge 2 – Human-centered automation
The flexibility of the robotic systems to perform a variety of tasks is increasing but the major benefit from the use of collaborative robotics comes from the close cooperation with humans, exploring the best abilities of humans and robots. To maximize this potential, there is the need for decision making tools that can cope the robot/machine limitations with the human dimension in ergonomic, social and productivity terms.
Challenge 3 – Simulation and Optimization tools for adaptive production systems
Scheduling and controlling manufacturing activities in a dynamic environment, subject to a high level of uncertainty imposed by various unexpected events like machine breakdowns or frequent changes in orders quantity, mix and due dates, is a difficult task. Additionally, increased flexibility allowed by new manufacturing technologies, like robotics, has amplified, rather than reduced, planning, scheduling and control problems. Thus, novel optimization, simulation or simulation based optimization tools are required to, not only generate robust production schedules, but also to undertake real-time rescheduling to cope with the production environment uncertainty.