Last Update: June 23,  2020

Course Objective:

Collaboration among partners to form a value network has become necessary as up-to-date information is so critical in a competitive market.  Sharing of information among a network of physical units on the shop floor and connecting internal manufacturing processes and business processes with external business processes allow a company to offer a core competence with flexible, responsive operations meeting the expectations of customers and the value network partners. This course aims to build students’ competence in collaboration in manufacturing from the board picture of collaborative manufacturing management down to collaboration on a shop floor. The students will learn from concepts, applications, and hands-on experience.

Learning Outcomes: The students on the completion of this course would be able to:

• • Recognize a potential collaborative manufacturing in a factory
  • Identify a value network for collaborative manufacturing for a business
  • Apply collaborative manufacturing management in practice
  • Manipulate collaborative robots for collaborative tasks
  • Manage manufacturing collaboration on a shop floor

Prerequisite:  None

 Course Outline:

Week	Topic	Workshop	Learning Materials	Teaching Materials	Note
1	Introduction to the course		Week 1 – Introduction	Week 1 – Introduction
	I. Collaborative Manufacturing Management
1	1. Evolution of Manufacturing Systems		MSIE-11-L-M1S1	MSIE-11-T-M1S1
2	2.Collaborative Manufacturing Management Model	Value network for collaborative manufacturing MSIE-11-L-M1S2-W01	MSIE-11-L-M1S2	MSIE-11-T-M1S2
3	3. Collaborative Manufacturing Management Fundamentals and Infrastructure	Plant simulation Tecnomatix Plant Simulation Modeling and Programming by Means of Examples (Steffen Bangsow, 2015)	MSIE-11-L-M1S3-part1	MSIE-11-T-M1S3-part1 Application of Tecnomatix Plant Simulation for modeling production and logistics processes
4	3. Collaborative Manufacturing Management Fundamentals and Infrastructure (cont.)		MSIE-11-L-M1S3-part2	MSIE-11-T-M1S3-part2
5	4. Ontology for Collaborative Manufacturing		MSIE-11-L-M1S4	MSIE-11-T-M1S4
	II Machines Collaboration on a Shop Floor
6	1. Distributed Manufacturing		MSIE-11-L-M2S1	MSIE-11-T-M2S1
7	2. Distributed Arrival Time Control for Real-Time Scheduling		MSIE-11-L-M2S2	MSIE-11-T-M2S2
8	3. Collaborative Material Handling System	Collaborative material handling system	MSIE-11-L-M2S3	MSIE-11-T-M2S3
9	4. Collaborative Manufacturing Processes	Collaborative machines MSIE-11-L-M2S4-W01	MSIE-11-L-M2S4-part1	MSIE-11-T-M2S4-part1
10	4. Collaborative Manufacturing Processes (cont.)		MSIE-11-L-M2S4-part2	MSIE-11-T-M2S4-part2
	III Man-Machine Collaboration on a Shop Floor
11	1. Evolution of Man-Machine Collaboration		MSIE-11-L-M3S1	MSIE-11-T-M3S1
12	2. Industrial human augmentation systems		MSIE-11-L-M3S2	MSIE-11-T-M3S2
13	3. Flexible Human-Robot Collaboration	Collaborative man-machine	MSIE-11-L-M3S3	MSIE-11-T-M3S3
14	4. Cyber-Human System		MSIE-11-L-M3S4-part1	MSIE-11-T-M3S4-part1
15	4. Cyber-Human System (cont.)		MSIE-11-L-M3S4-part2	MSIE-11-T-M3S4-part2

Laboratory Sessions:

• Laboratory on plant simulation : Simulate a manufacturing process based on local industries, identify a value network for collaborative manufacturing and apply collaborative manufacturing management.
• Laboratory on collaborative machines: Manipulate collaborative machines e.g. communication between 3D-printing machine and CNC milling machine
• Laboratory on collaborative robots: Manipulate collaborative robots e.g. machine-robot communication, human-robot collaboration
• Laboratory on collaborative material handling system : Manipulate collaborative material handling system on a shop floor e.g. automatic guided vehicle (AGV) and Automated storage and retrieval system (AS/RS)

Learning Resources:

Textbooks: No designated textbook, but class notes and handouts will be provided.

References:

1. Andre P. Calitz, Paul Poisat and Margaret Cullen, 2017, The future African workplace: The use of collaborative robots in manufacturing, SA Journal of Human Resource Management,pp. 1-11.
2. ARC Advisory group, 2001, Collaborative Manufacturing Management Strategies, ARCweb.com, pp.1-28
3. Eloise Matheson, Riccardo Minto, Emanuele G. G. Zampieri, Maurizio Faccio and Giulio Rosati, 2019, Human–Robot Collaboration in Manufacturing Applications: A Review, Robotics, Vol.8(100), pp. 1-25
4. Koomsap, P., Shaikh, I., Prabhu, V.V., 2005, Integrated process control and condition-based maintenance scheduler for distributed manufacturing control system, International Journal of Production Research, Vol. 43, No. 8, pp. 1625-1624.
5. Li, W. D., Ong, S. K., Nee, A. Y.C., McMahon, C. A. (Eds.), 2007, Collaborative Product design and manufacturing methodologies and applications. Springer Science & Business Media.
6. Luis M. Camarinha-Matos , Rosanna Fornasiero and Hamideh Afsarmanesh, 2017, Collaborative Networks as a Core Enabler of Industry 4.0 in Collaboration in a Data-Rich World. PRO-VE 2017. IFIP Advances in Information and Communication Technology, vol 506, pp 3-17.
7. Matthew Krugh and Laine Mears, 2018, A complementary Cyber-Human Systems framework for Industry 4.0 Cyber-Physical Systems, Manufacturing Letters, vol 15, pp. 89-92.
8. Melo, J. G., Fattori, C. C., Junqueira, F., & Miyagi, P. E., 2009,. Framework for collaborative manufacturing systems based in services. 20th International Congress of Mechanical Engineering (COBEM), Gramado, Brazil.
9. Mohammad Rizal Firmansyah and Yousef Amer, 2013, A Review of Collaborative Manufacturing Network Models, International Journal of Materials, Mechanics and Manufacturing, Vol. 1, No.1 pp. 6-12.
10. Roope Raisamo, Ismo Rakkolainen, Päivi Majaranta, Katri Salminen, Jussi Rantala and Ahmed Farooq, 2019, Human augmentation: Past, present and future, International Journal of Human-Computer Studies, Vol. 131, pp. 131-143.
11. Shirine El Zaatari, Mohamed Marei, Weidong Li and Zahid Usman, 2019, Cobot programming for collaborative industrial tasks: An overview, Robotics and Autonomous Systems, Vol. 116, pp.162–180.
12. Wit Grzesik, Hybrid additive and subtractive manufacturing processes and systems: A review (2018), Journal of Machine Engineering, Vol. 18, No. 4, pp. 5–24.

Journals and Magazines:

• Journal of Intelligent Manufacturing, Springer
• International Journal of Computer Integrated Manufacturing, Taylor & Francis
• Robotics and Autonomous Systems, Elsevier
• Robotics and Computer-Integrated Manufacturing, Elsevier
• International Journal of Human-Computer Studies, Elsevier
• Manufacturing Letters, Elsevier

Teaching and Learning Methods:

Each major topic of this course is approached using a three steps process: lecture together with class discussion and in-class group assignments, including case studies and practical exercises that students will work on and present at the end of the sessions. The students are expected to involve actively in-class activities. The students will also apply knowledge learned from class in laboratory sessions, which have been designed to match the learning topics. Besides, there will be a group project for the students to practice their knowledge, critical thinking, problem-solving, and decision-making skills as well as team management.

Time Distribution and Study Load:

Lectures: 30 hours

Workshop: 45 hours

Self-study: 45 hours

Evaluation Scheme: The final grade will be computed according to the following weight distribution: Class discussion and participation 5%, Peer assessment in class activities 5%, Assignments 10% Practical exercises 20%, Presentation 10% and Group project 50%.

An “A” would be awarded if a student shows a deep understanding of the knowledge learned through assignments, project works, and exam results.

A “B” would be awarded if a student shows an overall understanding of all topics.

A “C” would be given if a student meets below average expectation in understanding and application of basic knowledge.

A “D” would be given if a student does not meet expectations in both understanding and application of the given knowledge.

Developer: Kunlapat Thongkeaw (PSU), Thanate Ratanawilai (PSU), Wanida Rattanamanee (PSU), Wasawat Nakkiew (CMU) and Pisut Koomsap (AIT)