Dual-driving systems have been widely adopted in advanced manufacturing machine. Synchronization of the dual-servo systems is crucial for tracking reference trajectories. This article proposes a novel cross-coupled fuzzy logic sliding mode control for the synchronous control of dual-driving feed system. The dynamic model of dual-driving system has been established and the individual proportional-proportional-integral controller is built based on the dynamic model. To increase the synchronous performance, a cross-coupled sliding mode controller is provided based on dual-driving system model. In addition, a continuous saturation function is adopted to reduce chattering. Lyapunov stability criterion is used to analyze the stability of the cross-coupled sliding mode control. Moreover, a fuzzy logic saturation gain control approach is proposed to overcome the low robustness and poor dynamic synchronous performance in the normal cross-coupled control scheme. The adaptive saturation function is designed to eliminate synchronous deviation caused by the process of two axes following each other. Finally, an industrial application of dual-driving system is utilized to prove the effectiveness of the proposed scheme. The proposed cross-coupled fuzzy logic sliding mode control scheme has been effectively demonstrated to improve synchronous performance and tracking accuracy.
Finding a reliable quality inspection system of resistance spot welding (RSW) has become a very important issue in the automobile industry. In this study, improvement in the quality estimation of the weld nugget’s surface on the car underbody is introduced using image processing methods and training a fuzzy inference system. Image segmentation, mathematical morphology (dilation and erosion), flood fill operation, least-squares fitting curve and some other new techniques such as location and value based selection of pixels are used to extract new geometrical characteristics from the weld nugget’s surface such as size and location, shape, and the numbers and areas of all side expulsions, peaks and troughs inside and outside the fusion zone. Topography of the weld nugget’s surface is created and shown as a 3D model based on the extracted geometrical characteristics from each spot. Extracted data is used to define input fuzzy functions for training a fuzzy logic inference system. Fuzzy logic rules are adopted based on knowledge database. The experiments are conducted on a 6 degree of freedom (DOF) robotic arm with a charge-coupled device (CCD) camera to collect pictures of various RSW locations on car underbodies. The results conclude that the estimation of the 3D model of the weld’s surface and weld’s quality can reach higher accuracy based on our proposed methods.
The current nondestructive testing methods such as ultrasonic, magnetic, or eddy current signals, and even the existing image processing methods, present certain challenges and show a lack of flexibility in building an effective and real-time quality estimation system of the resistance spot welding (RSW). This paper provides a significant improvement in the theory and practices for designing a robotized inspection station for RSW at the car manufacturing plants using image processing and fuzzy support vector machine (FSVM). The weld nuggets’ positions on each of the used car underbody models are detected mathematically. Then, to collect perfect pictures of the weld nuggets on each of these models, the required end-effector path is planned in real-time by establishing the Denavit-Hartenberg (D-H) model and solving the forward and inverse kinematics models of the used six-degrees of freedom (6-DOF) robotic arm. After that, the most frequent resistance spot-welding failure modes are reviewed. Improved image processing methods are employed to extract new features from the elliptical-shaped weld nugget’s surface and obtain a three-dimensional (3D) reconstruction model of the weld’s surface. The extracted artificial data of thousands of samples of the weld nuggets are divided into three groups. Then, the FSVM learning algorithm is formed by applying the fuzzy membership functions to each group. The improved image processing with the proposed FSVM method shows good performance in classifying the failure modes and dealing with the image noise. The experimental results show that the improvement of comprehensive automatic real-time quality evaluation of RSW surfaces is meaningful: the quality estimation could be processed within 0.5 s in very high accuracy.
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