Manufacturing automation standards for smart fabrication using robot in kinematics control system with machine learning model

被引:0
|
作者
Huyan, Yongjiang [1 ]
机构
[1] Henan Mech & Elect Vocat Coll, Sch Mech & Elect Engn, Zhengzhou 451191, Peoples R China
关键词
Manufacturing industry; Smart fabrication; Robot; Kinematics; Control system; Automation monitoring;
D O I
10.1007/s00170-023-12902-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mechanical hardware has been assuming a focal part since the proposition of brilliant assembling. Starting from the start of the initial mix of modern robots into creation lines, modern robots have improved efficiency and assuage people from weighty jobs essentially. Robotics and software are utilized in industrial automation to control machinery and processes in several sectors. IoT, machine learning, and other methods are integrated into many applications to offer clever features that enhance the user experience. This research proposes novel technique in manufacturing industry smart fabrication using robot in kinematics-based control system with automation monitoring using machine learning techniques. Here the smart fabrication is carried out using kinematics-based control system by robots, and the automation monitoring is carried out using reinforcement belief spatio neural network. The experimental analysis is carried out for various automation monitoring data in terms of average accuracy, mean average precision, recall, and integral absolute error (IAE). Proposed technique attained average accuracy of 89%, mean average precision of 92%, recall of 94%, and IAE of 89%.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Smart Energy Management System Using Machine Learning
    Akram, Ali Sheraz
    Abbas, Sagheer
    Khan, Muhammad Adnan
    Athar, Atifa
    Ghazal, Taher M.
    Al Hamadi, Hussam
    CMC-COMPUTERS MATERIALS & CONTINUA, 2024, 78 (01): : 959 - 973
  • [22] Smart Attendance System Using Machine Learning Algorithms
    Chowdary, M. Nitin
    Sujana, V
    Satvika, K.
    Srinivas, K. Lakshmi
    Suhasini, P. S.
    MACHINE LEARNING AND AUTONOMOUS SYSTEMS, 2022, 269 : 99 - 115
  • [23] Iterative learning control for integrated system of robot and machine tool
    Thanh-Quan Ta
    Chen, Shyh-Leh
    ASIAN JOURNAL OF CONTROL, 2023, 25 (02) : 807 - 823
  • [24] Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph
    Patel, Dhaval
    Shrivastava, Shrey
    Gifford, Wesley
    Siegel, Stuart
    Kalagnanam, Jayant
    Reddy, Chandra
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 1604 - 1613
  • [25] Smart Air Pollution Monitoring System with Smog Prediction Model using Machine Learning
    Siddiqui, Salman Ahmad
    Fatima, Neda
    Ahmad, Anwar
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (08) : 401 - 409
  • [26] Safety analysis via forward kinematics of delta parallel robot using machine learning
    Liu, Cheng
    Cao, Guohua
    Qu, Yongyin
    SAFETY SCIENCE, 2019, 117 : 243 - 249
  • [27] Automation of Waste Treatment on the Washer Machine Based on PLC Control System in the Manufacturing Industry
    Ardi, Syahril
    Tommy, Muhammad Imam
    Afianto, Afianto
    2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2018), 2018, : 649 - 653
  • [28] Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things
    Al Shahrani, Ali M. M.
    Alomar, Madani Abdu
    Alqahtani, Khaled N. N.
    Basingab, Mohammed Salem
    Sharma, Bhisham
    Rizwan, Ali
    SENSORS, 2023, 23 (01)
  • [29] Monitoring and control of biological additive manufacturing using machine learning
    Gerdes, Samuel
    Gaikwad, Aniruddha
    Ramesh, Srikanthan
    Rivero, Iris V.
    Tamayol, Ali
    Rao, Prahalada
    JOURNAL OF INTELLIGENT MANUFACTURING, 2024, 35 (03) : 1055 - 1077
  • [30] Development of manufacturing control strategies using unsupervised machine learning
    Bowden, R
    Bullington, SF
    IIE TRANSACTIONS, 1996, 28 (04) : 319 - 331