Academic Year 2023/2024 - Teacher: ANTONIO COSTA

Expected Learning Outcomes

The primary goal of this course is to impart to the student an understanding of advanced processes for the production of mechanical components using the latest technologies and methods on the one hand, and of the production planning and control related techniques on the other hand. Therefore, the course will be structured by two major sections, namely Advanced Manufacturing Processes (AMP) and Advanced Manufacturing Systems (AMS). At the end of the course, students will be able to approach the optimization problem concerning both bulk and metal forming processes. At the same time, they will be able to solve the decision-making problem related to the mix of products to be manufactured in a flexible production system or the one related to the workload balancing into an assembly line context.

Course Structure

Beyond the frontal lessons, an intensive practical aspect characterizes the course of Advanced Manufacturing. Indeed, students will be involved in developing a series of project works properly supported by the supervision of the lecturer.

The course entails the following activities:

- Frontal lessons

- Numerical exercises on the different topics;

- Project works (numerical simulation, design of experiments, mathematical modelling, optimization).

Required Prerequisites

Knowledge regarding Mechanical Technologies are preliminary requirements to attend the course. Mechanical Technology is a formal prerequisite. In addition, it is worth pointing out that some development environments usually adopted in the university context are largely employed during the course, namely MS Excel and Matlab.

Attendance of Lessons

Course attendace is mandatory

Detailed Course Content

The contents of the course can be divided into three distinct sections, as follows:

Section I: Advanced Manufacturing Processes

  • General introduction on Manufacturing
  • Fundamental of Materials: behavior and manufacturing properties
  • Testing and notes on physical and manufacturing properties of materials
  • Notes on the heat treatments of metal alloys
  • Metal rolling processes and equipment
  • Metal forming processes and equipment
  • Extrusion and drawing processes and equipment
  • Sheet metal forming
  • Rapid Prototyping processes and operations
  • Computer aided manufacturing
  • Computer integrated manufacturing systems

Section II: Advanced Manufacturing Systems

  • Introduction to scheduling and sequencing
  • Singla machine scheduling
  • Parallel machine scheduling
  • Flow shop scheduling
  • Single/mixed model assembly line balancing and sequencing

Transversal contents:

>Design of Experiments

  • Response Surface Methodology (RSM)
  • Factorial and fractional designs

> Machine learning and Artificial Neural Networks

>Optimization algorithms

  • Optimization using advanced optimization techniques
  • Nature-inspired evolutionary algorithms.

More in detail, he former section (Advanced Manufacturing Processes) deals with the main metal forming manufacturing processes which, in some cases, will be in-depth investigated by applying both analytical methods and numerical simulations. Optimization of manufacturing processes is a key issue in the modern advanced manufacturing context. To this end, the design of experiments (DOE)-based statistical techniques will be employed to construct the mathematical model able to faithfully predict the behavior of a certain manufacturing process. Subsequently, a series of optimization procedures based on meta-heuristic algorithms will be studied with the aim of selecting the process parameters able to optimize a certain objective function. The latter section (Advanced Manufacturing Systems) is dedicated to the study of the most advanced techniques on production planning and control of manufacturing systems. Medium-term and short-term planning strategies will be investigated for solving both balancing and scheduling problems associated to different kinds of manufacturing systems. Besides, metaheuristic algorithms will be adopted to generate near optimal solutions of the problem under investigation.

Textbook Information

[1]. Kalpakjian S., Schmid S.R., Manufacturing Engineering & Technology, Pearson College,  ISBN-10 : 0133128741, ISBN-13 : 978-0133128741.

[2]. Kenneth R. Baker, Dan Trietsch, Principles of Sequencing and Scheduling, John Wiley & Sons Inc. ISBN-10 : 047039165, ISBN-13 : 978-047039

Some contents will be also derived from:

[3]. Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook, “Response surface methodology”, Wiley.

[4]. Kalpakjian S., Schmid S.R., Manufacturing Processes for Engineering Materials, Pearson College, ISBN-10 : 0132272717, ISBN-13 : 978-0132272711.

[5]. Kapil Gupta e Munish Kumar Gupta “Optimization of Manufacturing Processes”, Springer-Verlag, ISBN-10 : 3030196372, ISBN-13 : 978-3030196370.

Course Planning

 SubjectsText References
1Metal forming1), 3) and 4)
3Process Optimization5)

Learning Assessment

Learning Assessment Procedures

The course entails the following activities:

-       Frontal lessons;

-       Numerical exercises on the different topics;

-       Project works (numerical simulation, design of experiments, mathematical modelling, optimization).

 Student assessment criteria:

-       Evaluation of project work (student can be arranged in groups)     50%

-       Final oral examination      50%

To guarantee equal opportunities and in compliance with current laws, the Interested students can request a personal interview in order to schedule any compensatory and/or dispensatory measures, based on educational objectives and specific needs. It is also possible to contact the CInAP reference teacher (Centre for Active Integration and Participated - Services for Disabilities and/or DSA) of your Department. 

Examples of frequently asked questions and / or exercises

Q1: How many energy components characterize the extrusion process? 

Q2: What's the difference between open die forging and closed die forging? 

Q3: What is the response surface methodology? 

Q3: How many control parameters have to be set for a praticle swarm optimization?