Field
The present disclosure relates generally to methods for controlling automated manufacturing processes. More particularly, aspects of the present disclosure relate to systems and methods for optimizing performance of robotic assembly processes.
Description of the Related Art
Over the years, shifting manufacturing requirements to high flexibility, short production cycle time and high throughput have urged the emerging of intelligent manufacturing systems. Conventional industrial robots have high repeatability, but may lack adaptivity and flexibility. In manufacturing processes, the environment is constantly changing and parts/components to be processed could come from different batches and sometimes different suppliers. All of these variations will cause difficulty for conventional industrial robots to perform many manufacturing processes (for example, a converter assembly in the power train assembly process).
For example, in a typical assembly process, the clearance and geometry of parts from different batches, sometime different suppliers, are different. These variations will cause the increase of cycle time. Steps are sometimes taken to tune the assembly process parameters to adapt to the variations. However, it is difficult to tune all the parameters since the relationship between the parameters and system performance is not clear.
For example, installing a valve into a valve body is not always as easy as it looks. The radius of the valve is about 24.96 mm while the radius of the hole in the valve body is 25.00 mm with a clearance about 40 μm. Because of the fixture errors, the valve cannot be aligned with the holes on the valve body exactly. Therefore, the valve can be stuck at the surface of the valve body due to the positioning errors or jammed in the middle of the valve body due to the orientation errors. Thus several parameters are involved in this assembly process, such as search force, search speed, search radius and insertion force. The assembly process performance will decrease if these parameters are not tuned correctly to adapt to the variations.
Due to the demanding requirements of modern manufacturing and the limitations of conventional industrial robots, intensive human labors have been made in robot programming, teaching and parameter tuning/optimization etc. Several offline algorithms have been proposed to solve the assembly process parameter optimization problem. The Genetic algorithms (GA) are developed to randomly search for optimal parameters. To increase the efficiency of the GA based methods, Artificial Neural Network (ANN) may be utilized to model whether the parameters are “good” or “bad” to filter the candidate parameters first without performing any experiment. Design-of experiment (DOE) methods adopt a systematic way to optimize the parameters. After performing a series of experiments, the most sensitive parameters are chosen and tuned carefully. Even though these methods may be effective in offline parameter optimization, it may be unreasonable or unfeasible to use them online because of their low efficiency. Moreover, because the assembly processes typically have many stages and different control strategies such as hopping and searching, it may be difficult to construct a physical model to optimize the process parameters.
In many cases, the performance of a robotic assembly process is measured by the assembly cycle time and First-Time-Through (FTT) rate. Real-time assembly data may be used to construct an initial motion model. State-of-the-art robots may require an operator to manually modify parameters of the assembly process when they do not meet the requirements to satisfactorily complete the intended operation. In a typical process, the robot must be stopped (taken offline) and process parameter modifications made. This becomes a mitigated process with each failure to complete the assembly, updated again and again until satisfactorily completed. Subsequently, when the assembly model parameters have been defined, process cycle optimization and the FTT rate is again worked out by the human operator using the same iterative manual steps as before.
USPTO Backgrounds
USPTO Backgrounds