|Politecnico di Torino|
|Academic Year 2015/16|
Innovation of product/Process innovation
Master of science-level of the Bologna process in Industrial Production And Technological Innovation Engineering - Torino
The course aims to present the methodologies used in the processes of innovation and improvement of products and processes. In particular will be dealt with the methodologies used to design and manage production specification in the engineering of innovative products, time compression techniques and statistical based methodologies to asses the quality and reliability of products, improve the quality of products and processes, for the introduction and evaluation of innovative products and processes.
Expected learning outcomes
Knowledge of the techniques of time compression for the product / process development;
Knowledge-of statistical-based methodologies to asses the quality and reliability of products and to improve the quality of products and processes; knowledge-of statistical-based methodologies for the introduction and evaluation of innovative products and processes (robust design, six sigma etc.).
Ability to adapt production to functional specification of product and to evaluate their acceptance, especially in the phases of introduction of products / processes.
Ability to provide statistical methodological support to specialists involved in the activities of R & D, and to coordinate the evaluation activities.
Prerequisites / Assumed knowledge
Knowledge of basic technologies
Basic knowledge of probability and descriptive statistics
Background and integration of basic statistic: probability; population and sample parameters. Distribution: normal, lognormal, uniform, t-student, chi2, Weibull, exponential, Bernoulli, Poisson. Normal algebra.
Statistical Inference: confidence bounds. Mean and variance inference; inference of Bernoulli distribution.
Regression: Least squares method, correlation index; maximum likelihood method; confidence bounds.
Probability plots. Outliers.
Estimation of the distribution parameters and regression by the maximum likelihood method confidence bounds.
Reliability: definition. Static probabilistic design, Time depending reliability: failure rate function, MTTF, MTBF
System reliability: series, parallel, , stand-by, mixed and k out of n configurations.
Reliability assessment methodology; Pareto diagram, FTA, FMEA, FMECA.
Reliability evaluation test: type of data (complete, censored, truncated, incomplete): Weibull analysis; failure rate analysis; mixed Weibull analysis. Sudden Death test. Success run test
Qualitative and quantitative accelerated tests; elephant test, rate acceleration, overstress. Arrhenius, Eyring, IPL, T-NT models. Test with reduced sample; staircase.
Design of experiments: Hypothesis test; Comparison between two sample (parametric and non-parametric tests); 1 way ANOVA, 2 way ANOVA without and with replication. Classic and factorial experimental plans. Mathematical model and evaluation of effects. Factorial experimental plans; Yates tables. Orthogonality. Fractional plans. Confounding; Design and analysis. Block experimental design.
Quality: Definition. Process Capability Index, Quality Loss Function, Tolerances, economical safety factor. Quality improvement, P Diagram, Concept, parameter e tollerance design, Robust design. ANOM, sensitivity analysis. Control experiment
Basic ideas on six sigma methodology
Operations and processes
Processes improvement and innovation.
- Process performance objective
- Process mapping
- Process task and capacity
- Process variability
Methodology for diagnostic and improvements of manufacturing system
- Absolute benchmarking and value stream mapping.
- Integration of value stream mapping and discrete events simulation (descriptive analysis data , queuing theory)
- Measures of Manufacturing performance
- Push and pull production systems
- Lean synchronisation
Practical examples will be provided to the students in the course web page (didactical portal) ; these examples will be discussed during the tutorials.
During the course the students will attend lectures and practical exercise in classroom and Informatic labs. Groups of students which regularly attend theoretical and practical lectures can work on a case study and produce a technical report that can be evaluated in the final examination.
Texts, readings, handouts and other learning resources
Lecture notes in the course website will be provided.
Reference textbooks (Product Innovation):
G. Belingardi, "Strumenti statistici per la meccanica sperimentale e l’affidabilità"- Levrotto & Bella, 1997
C. Lipson, N. Sheth, "Statistical Design and Analysis of Engineering Experiments", McGraw-Hill, 1973
W.J. Dixon., F.J. Massey, "Introduction to statistical analysis", Int. Student Edition, McGraw-Hill, 1985
M. Vigier, "Pratique des plans d’expériences", Les éditions d’organisation, Paris, 1988
R. Levi, "Elementi di statistica sperimentale", RTM, Vico Canavese, 1972
M.S. Phadke, "Quality engineering using Robust Design", Prentice Hall, New Jersey 1989
Genichi Taguchi, "Taguchi on robust technology development: bringing quality engineering upstream", ASME Press, New York, 1993
Genichi Taguchi, "Introduction to quality engineering: designing quality into products and processes", Unipub, Dearborn, American Supplier Institute, White Plains, 1986
Genichi Taguchi, "System of experimental design: engineering methods to optimize quality and minimize costs",: Unipub Kraus ; Dearborne : American Supplier Institute, New York, 1987
W. Nelson, Accelerated testing" John Willey & Son, New York, 1990
Carter A.D.S. - "Mechanical Reliability", Macmillan 1986
Kampur K.C., Lamberson L.R. - "Reliability in engineering design", John Wiley, 1977
Manuale di statistica: www.dsa.unipr.it/soliani/soliani.html
www.weibull.com (life data analysis, accelerated life testing, system analysis)
Reference textbooks (Process Innovation):
Factory Physics -Wallace J.Hopp, Mark L. Spearman – McGraw Hill International Edition.
Operations and Process management , principles and practice for strategic impact – Slack, Brando-Jones, Johnston, Betts- Pearson
Gestione delle operations e dei processi- Slack, Brandon-Jones, Johnston, Betts, Vinelli, Romano, Danese -Pearson
Lean organization from the tools of Toyota Production System to Lean Office -Springer
Tecnomatix Plant Simulation 10 Step-by-Step Help – Siemens
Assessment and grading criteria
The exam consists of two parts, an written test of "Process innovation" and a subsequent oral of "Product innovation".
The written test of Process innovation assesses the knowledge and skills acquired through exercises and theoretical questions. The written test may be partially replaced, only by students who have attended at least 70% of lectures and tutorials, with the presentation and discussion of the technical report carried out as part of the course (maximum score 16/30). The technical report must be submitted by the date set by the teacher (approximately 10 days before the first exam session); if this deadline is not met, the student will have to take the entire written test.
The oral part of the Product Innovation assesses the knowledge of statistical tools for assessing the quality and reliability of products and for the improvement of products and processes. To sit for the oral of Product Innovation is necessary to have passed (min. 18/30) the part of Process Innovation
The final score will be the mean of the two parts.
If one part is failed the student must repeat the total exam (written and oral)
The students can conserve the vote of Process Innovation only during the ongoing examination session. In case the student does not succeed in the exam of Product Innovation within the same session, he will have to repeat both exams in the next exam session.
Programma definitivo per l'A.A.2015/16