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PORTALE DELLA DIDATTICA

Quality control techniques in Materials engineering

01DWJMZ

A.A. 2022/23

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Ingegneria Dei Materiali Per L'Industria 4.0 - Torino

Course structure
Teaching Hours
Lezioni 41
Esercitazioni in aula 3
Esercitazioni in laboratorio 36
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Genta Gianfranco Professore Associato ING-IND/16 21 0 0 0 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-IND/16
ING-IND/21
6
2
B - Caratterizzanti
B - Caratterizzanti
Ingegneria dei materiali
Ingegneria dei materiali
2022/23
The course aims to provide: - The basic concepts of probability and statistics used to manage and analyse the results of experiments. - The ability to understand general information about the features in the field of metrology tools and measuring system. - Knowledge concerning the elaboration and presentation of measurement and test results in materials engineering. - The concept of measurement uncertainty and its evaluation in materials engineering applications. - The main quality management and control methods used in industrial contexts.
The course aims to provide: - The basic concepts of probability and statistics used to manage and analyse the results of experiments. - The ability to understand general information about the features in the field of metrology tools and measuring system. - Knowledge concerning the elaboration and presentation of measurement and test results in materials engineering. - The concept of measurement uncertainty and its evaluation in materials engineering applications. - The main quality management and control methods used in industrial contexts.
It is expected the student will acquire knowledge in: - Regulations and standards for Quality Management Systems. - Common statistical data analysis techniques (descriptive statistics, outlier management, normality tests, regression, hypothesis test and ANOVA) for typical materials engineering measurements. - Metrological characteristics of instrument in materials engineering applications. - Measurement uncertainty evaluation. - Statistical Process Control: use of control charts, process capability analysis and sampling plans. It is expected the student will be able to: - Implement methods for the management and control of quality in measurements typical of materials engineering. - Gather data from laboratory experiments and process them to present test results. - Analyse the information derived from experiments and identify how to use such information in materials engineering design and control. - Design inspection procedures, which includes selecting appropriate measuring instruments and control strategies, and data analysis procedure for quality control in materials engineering. - Use common software to implement statistical data analysis for quality control.
It is expected the student will acquire knowledge in: - Regulations and standards for Quality Management Systems. - Common statistical data analysis techniques (descriptive statistics, outlier management, normality tests, regression, hypothesis test and ANOVA) for typical materials engineering measurements. - Metrological characteristics of instrument in materials engineering applications. - Measurement uncertainty evaluation. - Statistical Process Control: use of control charts, process capability analysis and sampling plans. It is expected the student will be able to: - Implement methods for the management and control of quality in measurements typical of materials engineering. - Gather data from laboratory experiments and process them to present test results. - Analyse the information derived from experiments and identify how to use such information in materials engineering design and control. - Design inspection procedures, which includes selecting appropriate measuring instruments and control strategies, and data analysis procedure for quality control in materials engineering. - Use common software to implement statistical data analysis for quality control.
Prior knowledge in mathematical analysis, physics and material science is required to understand better the content of the course. Also, base knowledge in spreadsheet use is required.
Prior knowledge in mathematical analysis, physics and material science is required to understand better the content of the course. Also, base knowledge in spreadsheet use is required.
1. Preliminary Concepts. Introduction to Quality standards, terms and definitions, historical notes on the introduction of quality control in industrial field for production and manufacturing, ISO 9000. Information contained in measurement data, the measuring management according to quality control, ISO 17025. 2. Basics statistic for quality control. Random variables. Statistical indexes of position and dispersion. Main statistical distributions (binomial, hypergeometric, Poisson, normal, t-Student, chi-squared, lognormal, F-Fisher). Confidence level and risk of error. Confidence intervals. 3. Tools for experimental data analysis: descriptive statistics and graphical representation (scatter plot, box-plot, histogram). Outlier management. Normality tests, regression, hypothesis testing, ANOVA. 4. Measurement uncertainty evaluation: main metrological characteristics of measuring instruments (resolution, accuracy, precision). Evaluation of uncertainty contribution: type A and type B. Law of uncertainty propagation. Expanded uncertainty. Table for uncertainty budget. 5. Statistical Process Control (SPC). Variability and natural tolerances of a process. Control charts for variable and attributes. Process capability analysis. Acceptance control: sampling plans.
1. Preliminary Concepts. Introduction to Quality standards, terms and definitions, historical notes on the introduction of quality control in industrial field for production and manufacturing, ISO 9000. Information contained in measurement data, the measuring management according to quality control, ISO 17025. 2. Basics statistic for quality control. Random variables. Statistical indexes of position and dispersion. Main statistical distributions (binomial, hypergeometric, Poisson, normal, t-Student, chi-squared, lognormal, F-Fisher). Confidence level and risk of error. Confidence intervals. 3. Tools for experimental data analysis: descriptive statistics and graphical representation (scatter plot, box-plot, histogram). Outlier management. Normality tests, regression, hypothesis testing, ANOVA. 4. Measurement uncertainty evaluation: main metrological characteristics of measuring instruments (resolution, accuracy, precision). Evaluation of uncertainty contribution: type A and type B. Law of uncertainty propagation. Expanded uncertainty. Table for uncertainty budget. 5. Statistical Process Control (SPC). Variability and natural tolerances of a process. Control charts for variable and attributes. Process capability analysis. Acceptance control: sampling plans.
The topics of the course will be illustrated during theoretical lectures. Additionally, practical classes delivered in computer labs to apply the concepts learned and discussed during theoretical lectures. Experimental laboratories will complement the course. During the experimental laboratories, typical material science measurements will be performed; those measurements will be analysed during devoted computer laboratory practical classes by exploiting the tools explained during the lectures. Information derived from this activity will be critically reviewed during discussion sessions with the teacher(s). The output of the experimental laboratory and relevant statistical analysis will serve to draft a technical report (individual or in small groups).
The topics of the course will be illustrated during theoretical lectures. Additionally, practical classes delivered in computer labs to apply the concepts learned and discussed during theoretical lectures. Experimental laboratories will complement the course. During the experimental laboratories, typical material science measurements will be performed; those measurements will be analysed during devoted computer laboratory practical classes by exploiting the tools explained during the lectures. Information derived from this activity will be critically reviewed during discussion sessions with the teacher(s). The output of the experimental laboratory and relevant statistical analysis will serve to draft a technical report (individual or in small groups).
- D. C. Montgomery, Introduction to Statistical Quality Control, 7th Ed., J. Wiley, New York, 2012. ISBN-13:978-1118146811. - D. C. Montgomery, Engineering Statistics, 5th Ed., J. Wiley, New York, 2010. ISBN-13: 978-0470631478. - G. Barbato, A. Germak, G. Genta, Measurements for Decision Making, 1st Ed., SocietÓ Editrice Esculapio, Bologna, 2013. ISBN-13: 978-8874885756.
- D. C. Montgomery, Introduction to Statistical Quality Control, 7th Ed., J. Wiley, New York, 2012. ISBN-13:978-1118146811. - D. C. Montgomery, Engineering Statistics, 5th Ed., J. Wiley, New York, 2010. ISBN-13: 978-0470631478. - G. Barbato, A. Germak, G. Genta, Measurements for Decision Making, 1st Ed., SocietÓ Editrice Esculapio, Bologna, 2013. ISBN-13: 978-8874885756.
ModalitÓ di esame: Test informatizzato in laboratorio; Elaborato scritto prodotto in gruppo;
Exam: Computer lab-based test; Group essay;
... The student to pass the exam will be required to have acquired the expected learning outcomes of the course. The exam will consist of two parts: - the evaluation of the technical report (individual or in small groups) drafted as an outcome of the experimental laboratories and related data analysis; - a written test. Both technical report's and written test's evaluation will weight 1/2 of the final score (i.e. 16 points each). To pass the exam, both parts, i.e., the technical report and the written test, must achieve a sufficient passing grade independently. The written test will consist of a set of exercises covering the topics of the course and some multiple answer questions about the theoretical section of the course. For the latter questions, the correct answers have a positive value, the wrong answers have a negative value and the not given answers a zero value. The written test exam will be delivered via a computer lab-based test using the Exam platform. During the written test, numerical tables and nomograms presented during the course may be exploited, while the use of books, notes or other supporting material of any kind is forbidden. The written test will last about 90 minutes, the exact duration being dependent on particular difficulty of the questions. The exam results will be published typically after about one week after the written test date. Students will be allowed to withdraw. Students will be allowed to see the written test on a date to be communicated upon the publishing of the exam results on the Portale della Didattica. The technical report on experimental laboratories activities will be submitted by students in a separate moment with respect to the written exam, though within the examination session. Once the assessment of the technical report will be defined by the teacher(s), in addition to the mark, feedback will be given to students about the quality of their work. If a positive mark will be obtained on this part of the exam, it will not be possible to withdraw the mark received for it.
Gli studenti e le studentesse con disabilitÓ o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'UnitÓ Special Needs, al fine di permettere al/la docente la declinazione pi¨ idonea in riferimento alla specifica tipologia di esame.
Exam: Computer lab-based test; Group essay;
The student to pass the exam will be required to have acquired the expected learning outcomes of the course. The exam will consist of two parts: - the evaluation of the technical report (individual or in small groups) drafted as an outcome of the experimental laboratories and related data analysis; - a written test. Both technical report's and written test's evaluation will weight 1/2 of the final score (i.e. 16 points each). To pass the exam, both parts, i.e., the technical report and the written test, must achieve a sufficient passing grade independently. The written test will consist of a set of exercises covering the topics of the course and some multiple answer questions about the theoretical section of the course. For the latter questions, the correct answers have a positive value, the wrong answers have a negative value and the not given answers a zero value. The written test exam will be delivered via a computer lab-based test using the Exam platform. During the written test, numerical tables and nomograms presented during the course may be exploited, while the use of books, notes or other supporting material of any kind is forbidden. The written test will last about 90 minutes, the exact duration being dependent on particular difficulty of the questions. The exam results will be published typically after about one week after the written test date. Students will be allowed to withdraw. Students will be allowed to see the written test on a date to be communicated upon the publishing of the exam results on the Portale della Didattica. The technical report on experimental laboratories activities will be submitted by students in a separate moment with respect to the written exam, though within the examination session. Once the assessment of the technical report will be defined by the teacher(s), in addition to the mark, feedback will be given to students about the quality of their work. If a positive mark will be obtained on this part of the exam, it will not be possible to withdraw the mark received for it.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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