Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

Abstract

The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of x- and y-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA’s remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs.

ANN-based prediction of ammonia nitrogen for wastewater discharge indicators under carbon neutral trend

“…ANNs have shown significant success in predicting NH3-N concentration due to their ability to learn and generalize from data. ANNs can be trained using both historical and real-time data.A study conducted by Huang et al (2019) used an ANN to predict NH3-N concentrations in an urban river. The ANN was trained using water quality data, meteorological data, and river flow data.…”

Automatic assembly with dual robotic arms based on mutual visual tracking and positioning

“…Combined with the control system and selection of appropriate power components and various circuit boards, as well as effective control methods, the optimal design and production of the robotic arm can be achieved. Most of the available robotic arms are single robotic arms with single charge-coupled device (CCD) [29][30][31] or two CCDs [32][33][34] for object tracking and positioning. Compared with double robotic arms, the operation is simpler and the work that is completed is less time than that with double robotic arms.…”