INTEGRATED PRODUCTION SYSTEMS | Università degli studi di Bergamo - Didattica e Rubrica


Modulo Generico
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Annualità Singola
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Ingegneria gestionale

It is recommeded to have followed the course of "Manufactung Technology"

Educational goals

Educational objectives
Knowledge of the basic and advanced methodologies for the industrialization of the products, with particular reference to integrated manufacturing systems that allow companies to create innovative and quality products, in shorter times and at competitive costs.
At the end of the course, student will be able to:
- Forecast the optimal automation level as a function of specific production systems
- Set-up an integrated production system considering the economical aspects and involving also simulation techniques
- Understand the machine tools and robot programming methods (e.g. ISO programming)
- Know the main types of automation in production systems used in production, handling, transport and storage
The course contributes to the educational objectives of the master's degree course in "Management Engineering" in relation to the technological-industrial area, with particular reference to the problems solving in the field of manufacturing and automation.

Course content

1. Introduction to integrated production systems, CIM and Industry 4.0
2. CNC machines
a. Main characteristics
b. Position and speed transducers
c. Adaptive control (e.g. systems for measuring and compensating for thermal drift, elastic deformations and vibrations)
d. Automatic programming of processing and CNC programming
3. Industrial robots & Material Handling
a. Characteristics and structure
b. Applications
c. Integration with the environment
4. Non-conventional process
a. EDM
b. WJ - AWJ
c. Laser
d. Micromanufacturing and ultraprecise processes
5. Additive Manufacturing
a. Characteristics
b. CAD integration
c. Technical and economical aspects
6. Process and product simulation (software, algorithms)
7. Digital Twin
8. Dimensional and morphological qualification of the products and quality aspects
a. Advanced Quality controls (machine vision, image analysis)
b. Measuring machines (CMM, optical and digital microscopes)
c. Reverse engineering (laser 3D scanning)
9. Artificial Intelligence
a. Artificial intelligence-Machine Learning-Deep Learning
b. Simulation and forecast methodology

Teaching methods

Frontal lectures, Simulation exercises, Laboratory demonstrations

Assessment and Evaluation

The exam is written defined by 4/5 open questions for evaluating the theoretical student knowledge (90 minutes). For each question, a score out of 30 will be assigned taking into account the completeness and correctness of the answer, the overall score is the arithmetic mean of the scores obtained in the individual questions.
For attending students there is the possibility of presenting a simulation model generated in small groups (max. 3 people). The delivery of the simulation model and a report describing the modelling and data analysis will allow you to have one less question in the written exam and the project score will replace that of the additional question (just in case of posiive results of the written part).