The course is divided in three parts: 1- basic principles of design of experiments (1 CFU); 2- quality through experimental designs (4 CFU); laboratory: case-studies carried out by the students on real data with teacher's assistance (1 CFU).
Lecture notes available on the Teacher's web-site: https://local.disia.unifi.it/berni/
Cornell J.A, 1993, Come applicare la metodologia delle superfici di risposta, Vol.8, Editoriale Itaca per ASQC.
Levi R., Lombardo A., Vicario G., 2006, progettazione e analisi degli esperimenti, McGraw-Hill libri capp.: 4-6-7-8.
Learning Objectives
The knowledge and the comprehension (ability) to make quality in a firm, by considering the technological and productive issues, through off-line statistical quality control methods, e.g. experimental designs and response surface methodology for carrying out a robust process optimization.
Prerequisites
Basic principles of statistics and statistic inference; in particular estimation methods and hypothesis tests.
Teaching Methods
1- Theory; 2- Application of the theoretical elements through real examples (case studies); 3- excercises performed by students with teacher's check.
Further information
The course will be in Florence, at the DISIA Department (Viale Morgagni), for futher details see the timetable published before the beginning of the course.
Type of Assessment
oral examination.
More precisely, questions will cover all the specific topics of the course, by considering the major arguments outlined through the chapters.
Particular attention is payed to the critical and constructive student's abilities to perform a robust process optimization, e.g. to be able to achieve and to determine the optimal setting for a product/process.
Course program
1-Brief introduction to the experimental design: trial, replication, randomization, source of variability, factor role (experimental factor and sub-experimental factor); random effects and fixed level factors; experimental planning.
2-Complete factorial design; interaction; fractional factorial design at two levels: resolution, confounding effects, alias pattern, building of the fractional factorials. Fractional factorial design and robust design, also considering the Taguchi’s philosophy. Response Surface Methodology- 1st and 2nd order: factorial designs, Central Composite Design, optimization methods (I and II order), polynomial models.