Controllo Statistico Della Qualit Montgomery Download
Nolan Guyz
Dr. Massimo Pacella is an Associate Professor within the Department of Engineering for Innovation at the University of Salento, Italy. He holds an MSc in Computer Engineering from the University of Lecce, Italy and a PhD in Manufacturing and Production Systems from the Polytechnic of Milan, Italy. He has been awarded a Fulbright Fellowship and has additionally served as a research scholar at the Department of Industrial and Operations Engineering, University of Michigan, USA.
The primary focus of Dr. Pacella's research lies in the domain of functional data processing and profile monitoring, encompassing both applied and methodological aspects of machine learning and statistical modeling, in addition to engineering principles. These research interests are primarily applied in the manufacturing and automotive industries.
Throughout his career, Dr. Pacella has been involved in various Scientific Projects with broad applications in manufacturing and production system management. He has also been involved in various research projects funded by industrial companies.
Examination: oral. The exam consists in the presentation and discussion of the case-study assignment results by project groups. Case Study assignments should be completed in teams of 1 or 2. Teams of 3 may be allowed provided a request is made in advance to the instructor.
Autonomia di giudizio. Attraverso lo studio di approcci teorici e la valutazione critica delle diverse tecniche, lo studente potr migliorare la propria capacit di giudizio e di proposta in relazione al problema ingegneristico del controllo statistico di processo.
This course provides students with the analytical and management tools necessary to solve manufacturing quality problems and implement effective quality systems. Topics include quality systems and standards, the Six Sigma problem solving methodology, process capability analysis, measurement system analysis, gauge R & R, ANOVA, statistical process control, and geometric tolerances.
Knowledge and ability to understand. The course aims to provide useful knowledge on engineering techniques for statistical process control and their quantitative and qualitative characteristics. Specific attention will be devoted to the evolution of techniques related to the modern availability of measuring instruments.
Ability to apply knowledge and understanding. Through the analysis of recent scientific literature and quantitative data related to case studies in engineering, we will provide analysis tools and statistical techniques applicable in various engineering fields, particularly in manufacturing. After the course the student should be able to: i) know the techniques of statistical process control in manufacturing and process companies; ii) know the methods and techniques of experiment design and analysis of experimental data; iii) know the advanced techniques of modeling / monitoring of measurement data.
Autonomy of judgment. Through the study of theoretical approaches and the critical evaluation of different techniques, the student will be able to improve his judgment and proposal skills in relation to the engineering problem of statistical process control.
Communication skills. The presentation of the course topics will be carried out in such a way as to allow the acquisition of the mastery of a technical language and of an appropriate specialist terminology. The development of communication skills, both oral and written will also be stimulated through the drafting of a work project that will be presented and discussed in the classroom during the final exam.
Learning ability. The ability to learn will be stimulated through presentations and discussions in the classroom, aimed at verifying the effective understanding of the topics covered. The ability to learn will also be stimulated by the deepening of scientific articles related to research topics of statistical process control as well as case studies typical of management engineering.
The course consists of lectures based on the use of slides made available to students through this portal. Classes are aimed at achieving the training objectives through the presentation of theories, models and methods as well as the discussion of case studies in manufacturing field.
Quality planning. Quality assurance. Quality control and improvement. PDCA methodology (Plan-Do-Check-Act) and other fundamental quality management principles. Six Sigma overview. The DMAIC (Define-Measure-Analyze-Improve-Control) problem solving process. Quality standards (ISO 9000, ISO 9001, ISO 9004).
Modeling process quality: describing variation. Important continuous distributions. Probability plots. Some useful approximations. Control chart for variables: chance and assignable causes of quality variation. Statistical basis of the control chart. Implementing SPC in a control chart for Xbar and R. Control charts for Xbar and S. The control chart for individual measurements. Procedures for Xbar, R and S charts. Case studies: applications of variables control charts.
Process and measurement system capability analysis. Process capability analysis using a histogram or a probability plot. Process capability ratios. Estimating the natural tolerance limits of a process. Tolerance limits based on the normal distribution. Nonparametric tolerance limits. Gauge and measurement systems capability studies. Isolate the components of variability in the measurement system. Accuracy and precision of a measurement system. The ANOVA (Analysis of Variance) approach for analyzing measurement data.
Fundamentals of Dimensional and Geometrical Tolerances According to ISO, CSA (Canada), and ANSI (USA). Geometric Product Specification (GPS) standard covering ISO/TR 14638. Envelope requirement according to ISO 8015. Maximum material principle according to ISO 2692-1988. Form tolerances. Flatness tolerances. Straightness tolerance. Roundness. Cylindricity. Orientation tolerances. Parallelism (straight line/straight line). Parallelism plane/plane (plane/straight line) on CMM. Angularity. Positioning tolerances. Tolerance of single radial flap (radial runout). Tolerance of single axial flap (axial runout).
This course provides students with the analytical and management tools necessary to solve manufacturing quality problems and implement effective quality systems. Topics include quality systems and standards, the Six Sigma problem solving methodology, process capability analysis, measurement system analysis, gauge R & R, ANOVA and statistical process control.
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