• Production Engineering, Semester 3, position 3

The aim of the course is to develop students' ability for conceptual design and implementation of intelligent manufacturing systems and processes by using the design theory, machine learning and evolutiveness, based on paradigms of artificial intelligence (AI). After he/she becomes familiar with the structure of intelligent manufacturing system based on multi-agent methodology (agents: robot, machine tool, machine learning, process planning, optimization, software, etc.) using laboratory equipment like reconfigurable mobile robots with sensors and laboratory model of designed manufacturing system as well as simulation by applying specialized software tools, the student will acquire knowledge necessary for the development of advanced production technologies.

Students' learning outcomes of this course are: • Implementation of developed software tools (e.g. TRIZ, Flexy) for modelling and analysis of intelligent manufacturing systems and processes. • Selection of methods based on the application of artificial neural networks (by using software packages MATLAB, Python, BPnet, ART Simulator) and other computational intelligence techniques in designing and building intelligence of artifacts (autonomous mobile robots can thus be observed interacting with their manufacturing environment) as well as scheduling of manufacturing entities. • Advanced utilization of the software for discrete event simulation (AnyLogic, Flexy) with analysis and presentation of the experimental results obtained. • Understanding the interaction of soft and hard real-time subsystems of autonomous mobile robot through reconfiguration and advanced programming in MATLAB. • Capability for team work.

Introduction to knowledge and machine learning-based intelligent systems. Machine learning models; deduction, induction and analogy. Machine learning as a basis of intelligent systems and processes. Paradigms of AI; decision tree induction, artificial neural networks, genetic algorithms, case-based reasoning-CBR (learning from experience), etc. Evolutiveness and intelligent systems based on Multi-agent Systems Engineering (MaSE) methodology. Agents work autonomously; basic concepts and importance. Autonomous mobile robots; target cognitive capabilities of mobile robots including perception processing, collision avoidance, anticipation, path planning, complex motor coordination, reasoning about other agents, etc. Mobile robot localization and navigation (pose estimation) as well as characteristic objects detection in robotic exploration within the manufacturing environment. The design theory and development of intelligent manufacturing systems. Scheduling of manufacturing entities. Software tools for modelling and analysis of intelligent manufacturing systems. Conceptual design of typical FMS lay-out configurations (FMS-Flexible Manufacturing System). Examples of developed Intelligent Manufacturing Systems (IMS).

Modelling and analysis of intelligent manufacturing systems and processes (laboratory work). Exemplified application of developed intelligent systems (laboratory work). Software for simulation of artificial neural networks (laboratory work). Software architectures for machine learning of intelligent systems. Intelligent behaviour of manufacturing system agents based on empirical control algorithm. Subsumption architecture for intelligent control based on achieving increasing pre-specified levels of competence in an intelligent robotic system (intelligent behaviour design of an autonomous mobile robot interacting with detected objects - programming in MATLAB). Scheduling plans optimization using genetic algorithms (programming in MATLAB). Software tools for conceptual design of FMS lay-out configurations (laboratory work). Project design (Material handling; Intelligent control of autonomous mobile robot; Scheduling of indoor transportation equipment).

Defined by Curriculum.

[1] Z. Miljković, M.M. Petrović, INTELLIGENT MANUFACTURING SYSTEMS – with excerpts from robotics and artificial intelligence, Textbook, XXVIII+409 p., UB - Faculty of Mechanical Engineering, 2021 (I edition), 18.1 /In Serbian/ [2] Z. Miljković, D. Aleksendrić, ARTIFICIAL NEURAL NETWORKS – solved examples with theoretical background (2nd ed.), Textbook, UB - Faculty of Mechanical Engineering, 2018, 18.1 /In Serbian/ [3] M. Kalajdžić (editor), Lj. Tanović, B. Babić, M. Glavonjić, Z. Miljković, et al., CUTTING TECHNOLOGY (9th ed.), Handbook, UB - Faculty of Mechanical Engineering, 2021, 18.1 /In Serbian/ [4] Z. Miljković, Systems of artificial neural networks in production technologies, Monograph book within the Series Intelligent Manufacturing Systems, Vol. 8, UB - Faculty of Mechanical Engineering, 2003, 18.1 /In Serbian/ [5] Z. Miljković, M.M. Petrović, Handouts, UB - Faculty of Mechanical Engineering, 2022, 18.1 /In Serbian/ [6] Z. Miljković, M.M. Petrović, Software "Moodle" for distance learning (http://147.91.26.15/moodle/), UB - Faculty of Mechanical Engineering, 2022, 18.13 [7] Z. Miljković, M.M. Petrović, Website for IMS (http://cent.mas.bg.ac.rs/), UB - Faculty of Mechanical Engineering, 2022, 18.13 [8] Laboratory mobile robots (PAL-TIAGo - Mobile Manipulator Robot with stereo vision system; K-Team's Khepera II mobile robot with gripper and camera; LEGO Mindstorms NXT and LEGO Mindstorms EV3 Sets of reconfigurable mobile robots equipped with sensors and micro-controllers; RAICO & DOMINO - prototypes of own development mobile robots), Laboratory CeNT, UB - Faculty of Mechanical Engineering, 18.12 [9] Laboratory model of designed manufacturing system, Laboratory CeNT, UB - Faculty of Mechanical Engineering, 18.12 [10] Software packages (MATLAB, Python 3.14.0rc2 and 3.13.7, BPnet, ART Simulator, AnyLogic, TRIZ, Flexy), Laboratory CeNT, UB - Faculty of Mechanical Engineering, 18.13

Total assigned hours: 75

New material: 20
Elaboration and examples (recapitulation): 10

Auditory exercises: 0
Laboratory exercises: 15
Calculation tasks: 0
Seminar paper: 0
Project: 15
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 0
Review and grading of the project: 4
Test: 2
Test: 4
Final exam: 5

Activity during lectures: 5
Test/test: 20
Laboratory practice: 10
Calculation tasks: 0
Seminar paper: 0
Project: 35
Final exam: 30
Requirement for taking the exam (required number of points): 30

Z.Miljković, M.M.Petrović, (2021) INTELLIGENT MANUFACTURING SYSTEMS – with excerpts from robotics and AI, Textbook (In Serbian), XXVIII+409 pp. (ISBN 978-86-6060-071-6), UB-Faculty of Mechanical Eng.; R. Siegwart, I.R. Nourbakhsh, D. Scaramuzza, (2011) INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS, 472 pp. (ISBN 9780262015356), The MIT Press, Cambridge, MA 02142.; N.P. Suh, (2001) AXIOMATIC DESIGN - ADVANCES AND APPLICATIONS, MIT-Pappalardo Series in Mechanical Engineering, 528 pp. (ISBN 9780195134667), Oxford University Press.; E. Alpaydin, (2010) INTRODUCTION TO MACHINE LEARNING, 2nd Edition, 400 pp. (ISBN 9780262012119), The MIT Press, Cambridge, England.; R.R. Murphy, (2019) INTRODUCTION TO AI ROBOTICS, 2nd Edition, 648 pp. (ISBN 9780262038485), The MIT Press, Cambridge, England.