ROBOTICS TIP: Maintenance 2.0
Targeted plant optimization with digital engineering tools
Until a robotic system is fully up and running in continuous operation, many steps have to be performed during planning, setup, commissioning and ramp-up. Those aspects are usually taken into account sufficiently, however, the effort of maintenance during runtime is most often underestimated. Component and machine tolerances, mechanical wear, human error, general environmental influences, and the setup of new variants can create a significant amount of work. With the software Robot Programming Suite and the analysis tool Learning & Analytics for Robots, ArtiMinds offers a beneficial combination of manual adjustments and data-driven automatic corrections in the production plant, as well as preparatory work for major operations in the digital twin.
In quite a few cases, the maintenance department is responsible for over 50% of the plant costs over the entire life cycle. Therefore, optimized maintenance processes that reduce labour and downtime can lead to significant cost reductions.
Maintenance of the plant can be improved by two key measures:
a) Automation of previously manual work steps by the utilization of data
b) Good preparation of work steps in the production plant by using a linked digital twin
With the appropriate digital tools, these improvements can be achieved without additional effort and are well aligned to traditional maintenance instead of being in conflict with it. Therefore, digital tools allow choosing the optimal maintenance approach for each situation.
During the maintenance of robot cells, typical work include adjusting individual teach points or recalibrating parts of the robot program. The calibration of tools, once a hardware component is changed due to wear, is also carried out directly on the system. Usually, these steps are iterative and performed manually. In contrast, the adjustment of program flow and the program logic is usually considered to be a deeper intervention and is not done directly in the work cell.
MANUAL ADJUSTMENT OF TEACH POINTS
For manual correction of teach points, production has to be stopped so that the maintenance personnel can change the positioning of the robot directly in the production line. In flow production, the situation is further complicated by the fact that, in such a case, the production line, where the changes are made, comes to a standstill. The maintenance worker performs corrections in several rounds until the process is stable again. This can result in considerable work and long downtimes. If sensor technology is used in the robot system, data-driven automation of the optimization presents itself as the go-to option, especially if frequent variations due to recurring irregularities become necessary.
DATA-DRIVEN AUTOMATION OF MAINTENANCE CORRECTIONS
Adaptive robot systems use 2D cameras, 3D cameras, linear laser scanners or force-torque sensors to flexibly adapt to production conditions. Due to the availability of sensor data, these are particularly suitable for data-driven automation of maintenance corrections. During each run of a robot task, all relevant parameters of the movement are stored in a database at high frequency. This allows parameter changes to be algorithmically evaluated across different runs. Those evaluations can be used, for example, for the automatic correction of teach points. This involves analyzing the actual process behaviour represented in the collected data to calculate teach points. The data-driven optimization allows adaptive robotic task to be performed statistically faster and with less error handling.
Data-driven automation of maintenance corrections reduces labour time and also enables precise consideration of minor variations over many runs. It is important that the process can be combined with manual maintenance work at any time, as humans are able to address exceptions that no algorithm is prepared for. In this way, humans and algorithms can also play off their strengths in a complementary manner during the maintenance process.
However, if a change in the program logic is required, a manual, time-consuming adjustment is often necessary. Doing this directly at the system can be highly inefficient, inconvenient and error-prone. It is a good idea to prepare this work assignment in the best possible way. To do so, one simply transfers the most current program version – with all changes made by previous manual or data-driven automatic corrections – to a digital engineering environment. Then, one is able to rework the program without interfering with production before finally replaying the reworked program back to the line. Since the latest local corrections are permanently included, this creates a simple digital twin.
CONSISTENCY OF PROGRAM CHANGES
When linking the different methods, it is important to guarantee the consistency of the program changes. This means that repeated manual adjustments of teach points at the plant must be processed by the data-driven auto-correction and, vice versa, the results must be able to be manually adjusted again at the plant. This requires that both changes can flow smoothly into the digital twin and back again.
Program logic and data on different data processing systems such as robot controller, PLC, industrial PC at the production line, engineering PC in the office or shared engineering in the network are to be linked in a smooth way.
INTERACTION OF PROCESS OPERATION & OPTIMIZATION CALCULATION
With its Robot Programming Suite in combination with Learning & Analytics for Robots, ArtiMinds offers an integrated software package not only for no-code or low-code robot programming. Both software solutions ensure seamless handling, especially for the maintenance concepts described.
After a robot program has been initially transferred to the robot controller as native code, teach points in the native program can be manually readjusted on the system in the usual way. In addition, the worker has the option of also using the two more advanced methods.
For data-driven, automatic corrections, a PLC enables the interaction of process operation and optimization calculation – whether directly at the plant, at the production line or via network with a computer. From the robot controller, the data goes to the computer, which contains the database and related algorithmic calculation methods. There, corrections are continuously calculated and then made available to the robot program, backed up by the PLC.
With the combination of manual adjustments and data-driven automatic corrections at the system as well as the preparation of major interventions in the digital twin, the different strengths of the methods can be used in a complementary way.
This is because automatic correction saves labour and downtime, but it cannot handle every exceptional situation or changes in program logic. The preparation on the digital twin, which can be quickly synchronized with the real system, reduces downtime, unnecessary (human) mistakes, e.g. caused by high time pressure, and avoids long uncomfortable work directly at the production line. In addition, manual adjustments directly at the system remain possible in the usual way in order to address selective, unusual problems promptly.