37065 - Automatic Machines T

Learning outcomes

The course aims to provide students with the knowledge related to the architectures of automatic machines and their main subsystems including fluid actuation systems, with particular regard to pneumatic systems. Students are also given an introduction to additive manufacturing technologies and their applications for automatic machines. At the end of the course, the student: - has the ability to analyze the architectures of automatic machines. - can approach a functional design of an automatic machine - can manage some issues related to automatic machines in the perspective of industry 4.0 - can analyze and a pneumatic circuit - can design a simple pneumatic circuit - can apply the knowledge acquired in additive manufacturing in order to approach a mechanical components project.

Course contents

Recommended prerequisites

To engage effectively with the course activities, students should have a sound knowledge of basic mathematics, physics and mechanics, together with the ability to formulate equilibrium equations, read mechanical diagrams, use units of measurement correctly and interpret functional relationships. Necessary concepts are reviewed during the lectures, but these reviews do not replace mastery of the fundamental concepts acquired in previous courses.

Course structure

The course is divided into two modules, worth 6 and 3 ECTS credits respectively. The two modules are developed in parallel throughout the teaching period, linking theoretical aspects, the analysis of real machines and the solution of applied problems.

Module 1 - 6 ECTS credits (60 hours)

M1-1 Historical overview of industrial automation and automatic machines, with particular reference to the manufacturing context of Bologna.

M1-2 Review of structural analysis for mechatronic systems: free-body diagrams, support reactions, friction, concentrated and distributed loads, internal forces, imposed displacements, component deformability and preliminary sizing checks.

M1-3 Fundamentals of industrial automation: functional decomposition of machines, operating sequences, operating states, sensors, actuators, interconnections between subsystems, and monitoring and adaptation elements from an Industry 4.0 perspective.

M1-4 Fundamental parameters for productivity calculations: machine cycle, machine degrees, production rate, working and transfer times, bottlenecks, motion laws, velocity, acceleration and jerk.

M1-5 Main automatic-machine architectures: asynchronous intermittent-motion machines, synchronous intermittent-motion machines, continuous-motion machines and mixed-architecture machines.

M1-6 Probabilistic analysis for automatic machines and interpretation of the effects of parameter variability on operation and productivity.

Module 2 - 3 ECTS credits (30 hours)

M2-1 Introduction to fluid-power actuation systems for automatic machines.

M2-2 Analysis, selection and sizing of pneumatic systems and simple circuits for automatic-machine units.

M2-3 Application of fluid-power actuation systems to industrial cases and assessment of their interactions with mechanical, sensing and control subsystems.

M2-4 Introduction to additive manufacturing technologies, suitable materials, and criteria for the selection and design of mechanical components for automatic machines.

M2-5 Development of a group project, including functional analysis, selection of solutions, essential checks and discussion of results.

Readings/Bibliography

Required material

• Lecture notes, presentations, diagrams, videos, Excel spreadsheets, Wolfram Mathematica files and other materials made available by the lecturer on the Virtuale platform.

Recommended texts

• A. Zucchelli, Complementi per macchine automatiche, assembled using McGraw-Hill Create, McGraw-Hill Education, 27 August 2019, ISBN 978-1307474541.
• S. J. Derby, Design of Automatic Machinery, Marcel Dekker, 2005.
• G. Boothroyd, Assembly Automation and Product Design, CRC Press, Taylor & Francis Group, 2005.

Further reading

• A.A.V.V., Advances in Future Manufacturing Engineering, CRC Press, Taylor & Francis Group, 2015.
• A.A.V.V., Future Mechatronics and Automation, CRC Press, Taylor & Francis Group, 2015.
• B. Lotter, Manufacturing Assembly Handbook, Butterworths, 1986.
• M. Fortis, M. Carminati, The Automatic Packaging Machinery Sector in Italy and Germany, Springer, 2015.
• For research assignments and open-ended problems, students must also use reliable and verifiable technical sources, such as scientific papers, standards, manuals, catalogues and manufacturers' datasheets, including through the bibliographic services provided by the University.

Teaching methods

Course delivery

The course uses a range of teaching tools, integrating theoretical lectures, analysis of real machines and processes, exercises, research activities and the development of design solutions. In particular, the teaching activities include:

• presentation and discussion of lecture notes prepared by the lecturer using PowerPoint slides;
• ·videos illustrating the operation of automatic machines and their subsystems;
• analysis of images, drawings, functional diagrams, experimental data and industrial case studies, also organised in Excel spreadsheets;
• development of calculation and sizing exercises using Excel and Wolfram Mathematica;
• comparative discussion of different mechanical and architectural solutions;
• individual and group activities aimed at finding, organising and verifying technical information.

Exercises and open-ended problems

The exercises include both problems with a defined solution, intended to develop and consolidate calculation methods, and open-ended engineering problems. In open-ended problems, students are given a functional objective, geometric, time-related or trajectory constraints, and design data; they must propose the mechanical or architectural solution they consider most appropriate.

For these problems, assessment is not based solely on the final result. Students must make explicit:

• their interpretation of the problem and its functional decomposition;
• the assumptions introduced and any data obtained from external sources;
• the alternatives considered;
• the criteria used to select the solution;
• the models and calculations used for sizing;
• plausibility checks, limitations of the solution, and any trade-offs between performance, mass, inertia, overall dimensions, cost, reliability and ease of manufacture.

The formulation and solution of the exercises are the student's intellectual responsibility. The student must be able to reconstruct independently the interpretation of the problem, the assumptions adopted, the choice of models, the calculation steps, the checks performed and the engineering meaning of the results.

Technical research assignments

The research assignments concern systems and components required for the operation, handling and transport functions of automatic machines, sensors, and materials used in machine construction or processed to obtain finished products. The aim is to develop curiosity, independence in research, and the ability to transform heterogeneous information into verified and usable technical knowledge.

The selection of sources, organisation and comparison of information, interpretation of data and formulation of conclusions are the student's intellectual responsibility. The student must be able to justify the choices made, distinguish data from interpretations and independently reconstruct the process followed.

Optional use of generative artificial intelligence

The use of generative artificial intelligence is optional in study activities, exercises and research assignments. Students who do not use AI are not penalised. The use of AI does not alter the student's intellectual responsibility. The interpretation of the problem, formulation of assumptions, choice of models, selection of sources, design decisions, checks, interpretation of results and conclusions must be understood, justified and independently reconstructable by the student. AI-generated outputs must be treated as proposals to be understood, verified and, where necessary, corrected, not as authoritative answers.

If a student chooses to use AI tools, the work must comply with the following principles:

• before using AI: independently formulate the problem or research task, identifying objectives, constraints, data, unknowns, assumptions, the physical model or research structure, the relationships likely to be required and, where possible, an expected order of magnitude or qualitative outcome;
• while using AI: identify the interactions and contributions that actually influenced the work, distinguishing proposals received from decisions made personally;
• after using AI: verify units of measurement, assumptions, sources, physical plausibility, limiting cases and sensitivity to parameters, indicating the modifications, corrections and choices made personally.

If AI has been used substantially, meaning that its contribution has appreciably influenced the formulation, technical content, calculations, code, alternatives considered or conclusions, the submitted work must include the concise AI-use statement provided in the appendix. The statement is not required for occasional uses that do not alter the technical content of the work. Relevant interactions and outputs must be retained by the student and made available to the lecturer upon request.

An AI system is not a technical or bibliographic source. Factual information, numerical values, relationships, technical properties and statements used in the work must be verified against reliable and citable sources. Personal data, confidential material or content for which the necessary permissions are not held must not be uploaded to external services.

• Failure to declare substantial use of AI in the preparation of submitted work results in failure to pass the examination.

Individual and group activities

Exercises and research assignments may be carried out individually or in groups. For group work, the full names of all participants and each person's individual contribution must be stated. Every group member must be able to discuss the entire work and independently reconstruct at least the parts to which they contributed directly.

Material to be prepared for the examination

The exercises and research assignments set during the course must be collected in two portfolios, in digital and/or paper format:

• portfolio of exercises and design problems;
• portfolio of technical research assignments.

The portfolios must be well organised, clearly indexed and understandable without relying solely on oral explanations. When an activity uses Excel, Wolfram Mathematica or other calculation tools, the editable files must be available during the examination.

Required structure for exercises

1. problem statement, objective and constraints;
2. data, units of measurement, sources and any assumed data;
3. initial independent formulation, including the physical model, assumptions and fundamental relationships;
4. solution procedure and calculations;
5. for open-ended problems, alternatives considered and selection criteria;
6. dimensional and plausibility checks, limiting cases and sensitivity to parameters;
7. engineering interpretation of the result and limitations of the solution;

Required structure for research assignments

1. research question or objective;
2. criteria used to select sources;
3. complete and verifiable bibliographic and technical references;
4. critical organisation of information, distinguishing data, interpretations and personal evaluations;
5. properties, validity conditions and limitations of the technical data reported;
6. connection with components, processes or functions of an automatic machine;
7. any comparisons between alternative solutions;

Teaching support tools

• The Virtuale platform is used to distribute lecture notes and useful documentation.
• For large documents that cannot be uploaded to Virtuale, a dedicated OneDrive folder will be created and access will be granted to students enrolled in the course.

Assessment methods

Assessment principles

The assessment is intended to evaluate disciplinary mastery, independence in formulating problems, the ability to reconstruct the procedures followed, the quality and verification of sources, the plausibility of results, and the ability to critically rework the material prepared during the course.

The work prepared during the course provides material for discussion and does not replace personal understanding. The student must be able to justify the choices made and adapt models and procedures to modified conditions proposed during the oral examination.

Examination structure

The examination consists of an individual oral examination lasting approximately 30-40 minutes. The lecturer may ask up to seven questions, broadly following this structure:

• three questions on theoretical aspects;
• three questions on the exercises and design problems collected in the portfolio; at least one of these includes a variation, extension or limiting case that is not reproduced identically in the submitted work;
• ·one question on the research assignments, the sources used, the criteria for selecting information and the conclusions reached.

A substantial inability to explain, interpret and connect the theoretical topics demonstrates insufficient mastery of the course content and results in failure to pass the examination, irrespective of the performance on the other questions.

Consultation of materials

During the oral examination, students may consult the official course material and their own portfolios. Digital materials must be available offline. Internet access, the use of chatbots or artificial-intelligence assistants, semantic search and automated querying of documents are not permitted. Calculation tools may be used only when authorised by the lecturer.

Consultation of materials is permitted as support for reconstructing and exploring topics in greater depth, but it does not replace knowledge of the conceptual structure of the course. Before consulting the material, the student may be asked to indicate the module, chapter or area in which the topic in question is covered.

The student must be able to identify independently the disciplinary area of the question, locate the part of the material in which the relevant concepts, diagrams or models are addressed, and select the pertinent information. An inability to navigate the structure of the material, evidenced by random searching or failure to locate the relevant topic, demonstrates insufficient mastery of the course content and results in failure to pass the examination, irrespective of the performance on the other questions.

Discussion of exercises

For each exercise discussed, the student must demonstrate an understanding of the problem, assumptions, model adopted, logical steps, data sources, checks performed and engineering meaning of the result. For open-ended problems, the student must also justify the selected solution, discuss the alternatives considered and recognise the main design trade-offs.

The formal quality of the portfolio or solution cannot compensate for a lack of personal understanding. If, during the discussion of even one of the exercises selected for examination, the student is unable to reconstruct independently the formulation and essential steps, the examination is not passed, irrespective of the answers to the other questions.

The lecturer may propose variations to the exercise statements. The student must first make a qualitative prediction, identify the model or relationships that need to be modified, and discuss the effect on the results. A complete numerical solution will not necessarily be required unless otherwise specified.

Discussion of research assignments

The student must be able to identify and discuss the sources used, justify their selection, distinguish data, interpretations and personal evaluations, verify the key information in the research, and connect the results to a specific application in the field of automatic machines.

Assessment criteria

The overall assessment considers the following dimensions:

• mastery of technical knowledge and ability to navigate the structure of the subject;
• ability to formulate problems, make assumptions and select appropriate models;
• independence in discussion and in adapting solutions to modified conditions;
• ability to verify calculations, sources, models and results;
• ability to justify design choices and recognise limitations and trade-offs;
• appropriate use of technical language and clarity of presentation;
• transparency in documenting individual and group work.

Teaching tools

PowerPoint presentations

Audiovisual

Files for use in Excel

Files for use in Wolfram Mathematica

Office hours

See the website of Andrea Zucchelli

See the website of Gregorio Pisaneschi