DTMA: Visual Object Inspection and Mechanism for Digital Twin with Robotic Arm

Author:

Venkataraman Hrishikesh1,Nidamanuri Jaswanth2,Dittakavi Aditya V3,Y Raja vara prasad1,Trestian Ramona4,H Nguyen4

Affiliation:

1. Indian Institute of Information Technology

2. School of Technology, Woxsen University

3. Indiana University – Purdue University Indianapolis

4. Middlesex University

Abstract

Abstract Over the recent years, the manufacturing sector has been rapidly changing the operational mechanism using advanced technologies such as Artificial Intelli- gence, Computer Vision, etc. The availability of various categories of sensors like IoT, IMU, obstacle detecting sensors, actuators embedded in the system helps to visually monitor the manufacturing process. However, there are many challenges that are yet to be addressed in Cyber-Physical Systems (CPS) and industry 4.0. This includes reliability, precision, adaptability and multi-sensor informa- tion fusion and analysis. Hence, this work proposes an advanced and integrated framework Digital Twin Manipulator Arm (DTMA) that establishes the primary base for industrial level analysis of Digital Twins, including the required concepts of anomaly detection. The main objective of the proposed DTMA framework is three-fold. The first objective is to design a digital twin of robotic arm. The second is to design the visual inspection module for object inspection in man- ufacturing process. The third objective is to integrate both the modules with automated correction using robotic arm in to the entire process. The applica- tion of DTMA framework is gauged from two different use-cases discussing the heuristic based block level functioning of the proposed framework. The proposed framework will enable the development of mechanisms for key advantages such as predictive maintenance, reduced operation cost, visual inspection with higher processing rates and improved productivity.

Publisher

Research Square Platform LLC

Reference29 articles.

1. “A Novel Imple- mentation Framework of Digital Twins for Intelligent Manufacturing Based on Container Technology and Cloud Manufacturing Services;Hung M-H;IEEE Transactions on Automation Science and Engineering,2022

2. Trestian et. al., ”Digital Twin for 5G and Beyond,” in IEEE Communication Magazine, vol. 59, no. 2, pp. 10–15, Feb. 2021.

3. ”Artificial Intelligence-Based Sensors for Next Generation IoT Applications: A Review;Mukhopadhyay SC;in IEEE Sensors Journal,2021

4. S. Mihai et. al., ”A Digital Twin Framework for Predictive Maintenance in Industry 4.0.” (2021).

5. K. Renuka, N. Bhuvanesh, and J. Reena Catherine. ”Kinematic and Dynamic Modelling and PID Control of Three Degree-of-Freedom Robotic Arm,” Advances in Materials Research. Springer, Singapore, 2021. 867–882.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3