Training for smart manufacturing using a mobile robot-based production line

doi:10.1007/s11465-020-0625-z

Front. Mech. Eng.

2021, Vol. 16

Issue (2) : 249-270 https://doi.org/10.1007/s11465-020-0625-z

RESEARCH ARTICLE

Training for smart manufacturing using a mobile robot-based production line

Shuting WANG, Liquan JIANG, Jie MENG, Yuanlong XIE(

), Han DING

School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

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Abstract

Practice experimentation that integrates the manufacturing processes and cutting-edge technologies of smart manufacturing (SM) is essential for future academic and applied engineering personnel. The broadening efficacy of hands-on experience in SM engineering education has been manifested. In this regard, a reference practical system is proposed in this study for hands-on training in SM crucial advancements. The system constructs a mobile robot-based production line (MRPL) to increase participants’ interest in theoretical learning and professional skills. The MRPL-based reference system includes the comprehensive principles and processes involved in modern SM factories from warehousing to logistics, processing, and testing. With key features of modularity, integrability, customizability, and open architecture, this system has a threefold objective. First, it is an interdisciplinary subject that enables students to translate classroom learning into authentic practices, thus facilitating knowledge synthesis and training involvements. Second, it offers effective support to cultivate the attributions and behavioral competencies of SM talents, such as perseverance, adaptability, and cooperation. Third, it promotes students’ capacities for critical thinking and problem solving so that they can deal with the difficulties that physical systems have and motivates them to pursue careers with new syllabi, functions, and process techno-logies. The received positive evaluations and assessments confirm that this MRPL-based reference system is beneficial for modern SM talent training in higher engineering education.

Keywords smart manufacturing hands-on experience engineering education mobile robot-based production line

Corresponding Author(s): Yuanlong XIE

Just Accepted Date: 07 February 2021 Online First Date: 01 April 2021 Issue Date: 15 June 2021

Cite this article:

Shuting WANG,Liquan JIANG,Jie MENG, et al. Training for smart manufacturing using a mobile robot-based production line[J]. Front. Mech. Eng., 2021, 16(2): 249-270.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-020-0625-z
https://academic.hep.com.cn/fme/EN/Y2021/V16/I2/249

Fig.1 Developed serial mobile robot platform. (a) Mobile manipulators; (b) transporter.

Fig.2 Hardware of the mobile robot. 1: Manipulator; 2: Eye-in-hand vision system; 3: Industrial computer; 4: Controller; 5: Battery; 6: Steering/driving motors; 7: Suspension system; 8: Ultrasonic transducer; 9: LiDAR; 10: Anti-collision strip; 11: Storage rack.

Fig.3 Overall software architecture of the developed mobile robot platform.

Fig.4 Designed localization and re-localization processes.

Fig.5 Offset error calculation and compensation of the end-effector.

Fig.6 Integrated path planning process of the coordinated motion.

Fig.7 Mode-dependent switched sliding mode control framework.

Fig.8 Mobile robot-based modern smart manufacturing factory.

Fig.9 Structure of a modern smart manufacturing factory. MRPL: Mobile robot-based production line; SCADA: Supervisory control and data acquisition; PLC: Programmable logic controller; APS: Advanced planning and scheduling; ERP: Enterprise resource planning; PLM: Product lifecycle management; LES: Product lifecycle management; MES: Manufacturing execution system; IoT: Internet of Things.

Fig.10 Presented mobile robot-based “1+X” training framework: (a) Design principle and (b) implementation case of an automation production line.

Fig.11 Engineering students’ attributes in higher education.

Fig.12 Implementation example of the designed mobile robot-based production line. 1: Stereoscopic warehouse; 2: Positioning table; 3: Measuring equipment; 4: U-shaped conveyor chain; 5: Laser making machine; 6: Washing machine; 7: Robot arm; 8: Positioning table; 9: Numerical control (NC) lathe; 10: NC fraise machine.

Fig.13 Coordinated operation of multiple mobile robots in real scenarios.

Fig.14 Data and information processing flow of the constructed mobile robot-based production line platform.

Fig.15 Material distribution, information storage and collection unit.

Fig.16 Intelligent scheduling strategy and material transportation. (a) Loading and unloading process; (b) multi-robot scheduling.

Fig.17 Human–computer interaction and central control unit.

Fig.18 Intelligent processing unit.

Fig.19 Tool breakage detection program based on edge calculation. (a) Normal operation; (b) tool breakage.

Fig.20 Intelligent tool life management of the numerical control machine.

Fig.21 Cleaning and quality inspection unit for processed materials. (a) Washing machine; (b) machining quality analysis.

Fig.22 Customized creative pattern processing by laser. (a) Laser processing system; (b) laser processing.

Fig.23 Diverse sensor systems. (a) Sensor array for the stereoscopic warehouse; (b) sensor system for the positioning station; (c) inbound/outbound monitoring of the material tray; (d) multi-sensor fusion robot navigation.

Tab.1 Participant-centered discipline configuration

Fig.24 Typical designed Chinese zodiac relievo. (a) Chinese zodiac; (b) an example of the relievo.

Fig.25 Processing procedure of producing a personalized souvenir.

Fig.26 Interactive interfaces and information flow. MR: Mobile robot; VR: Virtual reality; AR: Augmented reality.

Fig.27 Mobile device interfaces of the developed interactive application.

Fig.28 Designed interactive operating software. (a) Task allocation and scheduling interface; (b) mapping interface.

Fig.29 Results of the course evaluation. (a) Project-based practice; (b) B1 course; (c) B2 course; (d) C1 course.

Tab.2 Questionnaire results of the MRPL-based raining outcomes

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