Inicio  /  Applied Sciences  /  Vol: 13 Par: 23 (2023)  /  Artículo
ARTÍCULO
TITULO

An Intelligent Detection System for Surface Shape Error of Shaft Workpieces Based on Multi-Sensor Combination

Xiaoyan Guan    
Ying Tang    
Baojiang Dong    
Guochao Li    
Yanling Fu and Chongshun Tian    

Resumen

As the main components of mechanical products and important transmission components of mechanical motion, shaft workpieces (SW) need to undergo high-speed motion while also withstanding high torque motion, which has high processing requirements. At the same time, the processing quality of the workpieces determines the success of the entire processing process, and the quality-inspection methods and the accuracy of the technology directly affect the evaluation of the product. This paper designs an intelligent detection system for the surface shape error (SSE) of SW that combines multiple sensors. Based on the principle of sensor use and specific experimental status, the overall scheme of the detection system is designed, followed by research on the spatial positioning algorithm and surface measurement algorithm of the workpiece to be tested. We then compensate and correct the errors with the algorithm. The effectiveness of the system is verified by measuring the surface size of the workpiece. Finally, the radial circular runout error is taken as an example to verify the detection system. The results show that the measurement error is less than 5%, and the accuracy of the system is high.

 Artículos similares

       
 
Sonia Díaz-Santos, Óscar Cigala-Álvarez, Ester Gonzalez-Sosa, Pino Caballero-Gil and Cándido Caballero-Gil    
This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineate... ver más
Revista: Applied Sciences

 
Youngkwang Kim, Woochan Kim, Jungwoo Yoon, Sangkug Chung and Daegeun Kim    
This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it... ver más
Revista: Information

 
Yiming Mo, Lei Wang, Wenqing Hong, Congzhen Chu, Peigen Li and Haiting Xia    
The intrusion of foreign objects on airport runways during aircraft takeoff and landing poses a significant safety threat to air transportation. Small-scale Foreign Object Debris (FOD) cannot be ruled out on time by traditional manual inspection, and the... ver más
Revista: Applied Sciences

 
Qiuyue Li, Hao Sheng, Mingxue Sheng and Honglin Wan    
Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents... ver más
Revista: Applied Sciences

 
Marco Guerrieri, Giuseppe Parla, Masoud Khanmohamadi and Larysa Neduzha    
Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments. This... ver más
Revista: Infrastructures