This paper presents the objective metric study on Design Of Experiments (DOE)-based robotic force control parameter optimization in transmission torque converter assembly. Based on a real-world assembly production process, investigation and analysis are performed on the optimization metrics of assembly cycle time mean (MEAN), its mean plus three times of standard deviation (MEAN+3*STDEV), and First Time Through (FTT) rate. Simulations have been conducted to illustrate and explain the findings in the parameter optimization practice. Practical metric criteria have been proposed and discussed. An on-pendant robotic assembly parameter optimization tool with the objective metric concept is introduced. And automatic parameter optimization or online robot learning feature is also mentioned in terms of the objective metrics for the particular robot assembly parameter optimization tasks.Finally conclusions are drawn and discussion and further investigation is proposed.
Robotic assembly self learning is accomplished through the use of a parameter optimization technique in the midst of running production on a real-world torque converter assembly process. The robotic performance metrics optimized in the manufacturing process are First Time Through (FTT) percentage and cycle time. The data generated is automatically gathered and analyzed by two robot program modules – RAPID motion program module and C# analyzing module. The optimization tool applies Taguchi full factorial Design of Experiments (DOE) that is running on parts during the production process. These results are subjected to automatic statistical analysis to discover the optimal parameter found based on FTT and cycle time performance. The efficacy of this method has been proved in several Ford Motor Company Powertrain assembly plants. The optimization program continues to run iteratively until no further improvement of the process is discovered or an engineering limit set on the parameter range is reached. The test results based on real world data are presented and analyzed in this paper.
This paper examines the structure and performance of three control strategies for a regenerative life support system constrained by mass balance equations. A novel agent-based control strategy derived from economic models of markets is compared to two standard control strategies, proportional feedback and optimal control. The control systems require different amounts of knowledge about the underlying system dynamics, utilize different amounts of information about the current state of the system, and differ in their ability to achieve system-wide performance goals. Simulations illustrate the dynamic behavior of the life support system after it is perturbed away from its equilibrium state or nominal operating point under the three different control strategies. The performance of these strategies is discussed in the context of system-wide performance goals such as efficiency and robustness.
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