A dimensional tolerancing knowledge management system using Nested Ripple Down Rules (NRDR)

This paper proposes to use a knowledge acquisition (KA) approach based on Nested Ripple Down Rules(NRDR) to assist in mechanical design focusing on dimensional tolerancing. A knowledge approach to incrementally model expert design processes is implemented. The knowledge is acquired in the context of its use, which substantially supports the KA process. The knowledge is captured which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards in order to demonstrate the presented approach. The developed dimensional tolerancing knowledge management system would help mechanical designers become more effective in the time-consuming tolerancing process of theirdesigns in the future

A Machine Learning Approach for Global Steering Control Moment Gyroscope Clusters

This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and the desired maneuver—inputs—to a number of null space motions the gimbals have to execute—output. Two ML techniques—Deep Neural Network (DNN) and Random Forest Classifier (RFC)—are utilized to predict the required null motion for trajectories that are not included in the training set. The principal advantage of this approach is the exploitation of global information gath-ered from the whole maneuver compared to conventional steering laws that consider only some local information, near the current gimbal configuration for optimization and are prone to local extrema. The data-set generation and the predictions of the ML systems can be made offline, so no further calculations are needed on board, providing the possibility to inspect the way the system responds to any commanded maneuver before its execution. The RFC technique demonstrates enhanced accuracy for the test data compared to the DNN, validating that it is possible to correctly predict the null motion even for maneuvers that are not included in the training data. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Using Nested Ripple Down Rules (NRDR) to Aid in Mechanical Dimensional Tolerancing

A dimensional tolerancing knowledge management system using Nested Ripple down Rules (NRDR) targeted towards incrementally capturing expert design processes is presented. In order to demonstrate the presented approach, the knowledge is captured which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards. The developed dimensional tolerancing knowledge management system, DesignAssistant, would help mechanical designers become more effective in the time-consuming tolerancing process of their designs in the future