Quality Control of Frame Structures of Robotic Systems by Express Nondestructive Methods

The problem of quality control of parts of robotic systems is an essential task for modern technology's effective functioning. The article discusses the fundamental principles of ensuring the quality of parts using express nondestructive methods of impact indentation of conical indenters using artificial intelligence algorithms. The quality of parts is considered from the standpoint of a set of mechanical properties that determine strength, hardness and deformability. In practice, the strength characteristics must be known during production and must be quickly and accurately controlled during operation. The characteristics of the strength and deformability of steels are of a stochastic nature. To implement the proposed approach, a device for impact indentation of an indenter has been developed, and a method based on the probabilistic nature of strength properties has been proposed.

[1]  Serdar Yazyev,et al.  Optimization of thick-walled spherical shells at thermal and power influences , 2017 .

[2]  Zheng Li,et al.  Statistischer Maßstabseffekt und seine Bedeutung für die Zuverlässigkeit im Stahlbau , 2020, Bautechnik.

[3]  I. Bogdanova,et al.  Production quality and safety management in the corporate-type integrated structures in the agro-industrial complex , 2020, E3S Web of Conferences.

[4]  Horacio González-Vélez,et al.  Multi-service model for blockchain networks , 2021, Inf. Process. Manag..

[5]  S. Dubey Hyper‐efficient estimator of the location parameter of the weibull laws , 1966 .

[6]  A. Rybak,et al.  Stand for carrying out life tests of plunger hydraulic cylinders with energy recovery , 2020, IOP Conference Series: Materials Science and Engineering.

[7]  Ray Y. Zhong,et al.  Industrial Blockchain: A state-of-the-art Survey , 2021, Robotics Comput. Integr. Manuf..

[8]  Shreyes N. Melkote,et al.  Hybrid statistical modelling of the frequency response function of industrial robots , 2021, Robotics Comput. Integr. Manuf..

[9]  Albert Jones,et al.  A New Architectural Approach to Monitoring and Controlling AM Processes , 2020, Applied Sciences.

[10]  Viktor B. Rykov,et al.  Agricultural machine parts quality control by dynamic non-destructive methods , 2018 .

[11]  Kun‐Cheng Ke,et al.  Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning , 2021, Polymers.

[12]  K. Shileev,et al.  Analysis of hydraulic resistance in a rotating junction of the cooling system of an active phased array antenna of a circular view , 2020, IOP Conference Series: Materials Science and Engineering.

[13]  Hubert Anysz,et al.  Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests , 2020, Materials.

[14]  Chenhui Shao,et al.  Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning , 2021 .

[15]  Zheng Li,et al.  Statistischer Maßstabseffekt und seine Bedeutung für die Zuverlässigkeit im Stahlbau , 2020, Bautechnik.

[16]  A D Lukyanov,et al.  Development of methods for analyzing patterns of current consumption in a system for wireless monitoring the effectiveness of metalworking production , 2020 .

[17]  James Gao,et al.  An automatic and accurate method for tool wear inspection using grayscale image probability algorithm based on bayesian inference , 2021, Robotics Comput. Integr. Manuf..

[18]  Jianrong Tan,et al.  An assembly precision analysis method based on a general part digital twin model , 2021, Robotics Comput. Integr. Manuf..