Ingegneria Meccanica | PROGRAMMAZIONE E CONTROLLO DELLA PRODUZIONE

cod. 0622300010

PROGRAMMAZIONE E CONTROLLO DELLA PRODUZIONE

	0622300010
	DIPARTIMENTO DI INGEGNERIA INDUSTRIALE
	EQF7
	MECHANICAL ENGINEERING
	2018/2019

PERCORSO COMUNE Cohort 2017/2018

	YEAR OF COURSE 2
	YEAR OF DIDACTIC SYSTEM 2016
	PRIMO SEMESTRE

TEACHING UNITS

	SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
	ING-IND/16	6	60	LESSONS	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS

	Objectives
	THE COURSE IS AIMED TO PROVIDE BOTH THE THEORETICAL AND SCIENTIFIC BASES AND FUNDAMENTAL METHODOLOGICAL TOOLS FOR THE UNDERSTANDING OF NON-DETERMINISTIC, NATURAL PHENOMENA AS WELL AS STOCHASTIC SIMULATION MODELS THROUGH THE STUDY OF THE EXPERIMENTAL DATA. THE STUDENT IS THEREFORE LED TO MATURE THE PRINCIPLES OF STATISTICAL ANALYSIS, THE FUNDAMENTAL RELATIONSHIPS BETWEEN VARIABLES AND RANDOM FUNCTIONS. THE AIM IS THEREFORE TO STRENGTHEN THE BASIC KNOWLEDGE OF DESCRIPTIVE AND INFERENTIAL STATISTICS BY PRESENTING THEORETICAL MODELS OF RANDOM VARIABLES OF MAJOR INTEREST IN INDUSTRIAL APPLICATIONS. IN ADDITION, TEACHING IS AIMED TO LAY THE BASES TO UNDERSTAND THE INTERACTIONS BETWEEN TWO OR MORE VARIABLES AFFECTING A MANUFACTURING SYSTEM. EVENTUALLY, IT IS AIMED TO PROVIDE THE GUIDELINES TO CONDUCT A CRITICAL CHOICE TO BE USED IN THE DESIGN-OF-EXPERIMENTS (DOE) APPROACH IN ORDER TO EXTRACT AND ANALYZE WITH REASONABLE VARIABILITY THE PROPER RESPONSE VARIABLES OF A MANUFACTURING SYSTEM.

THE COURSE IS AIMED TO PROVIDE BOTH THE THEORETICAL AND SCIENTIFIC BASES AND FUNDAMENTAL METHODOLOGICAL TOOLS FOR THE UNDERSTANDING OF NON-DETERMINISTIC, NATURAL PHENOMENA AS WELL AS STOCHASTIC SIMULATION MODELS THROUGH THE STUDY OF THE EXPERIMENTAL DATA. THE STUDENT IS THEREFORE LED TO MATURE THE PRINCIPLES OF STATISTICAL ANALYSIS, THE FUNDAMENTAL RELATIONSHIPS BETWEEN VARIABLES AND RANDOM FUNCTIONS.
THE AIM IS THEREFORE TO STRENGTHEN THE BASIC KNOWLEDGE OF DESCRIPTIVE AND INFERENTIAL STATISTICS BY PRESENTING THEORETICAL MODELS OF RANDOM VARIABLES OF MAJOR INTEREST IN INDUSTRIAL APPLICATIONS. IN ADDITION, TEACHING IS AIMED TO LAY THE BASES TO UNDERSTAND THE INTERACTIONS BETWEEN TWO OR MORE VARIABLES AFFECTING A MANUFACTURING SYSTEM.
EVENTUALLY, IT IS AIMED TO PROVIDE THE GUIDELINES TO CONDUCT A CRITICAL CHOICE TO BE USED IN THE DESIGN-OF-EXPERIMENTS (DOE) APPROACH IN ORDER TO EXTRACT AND ANALYZE WITH REASONABLE VARIABILITY THE PROPER RESPONSE VARIABLES OF A MANUFACTURING SYSTEM.

	Prerequisites
	MANUFACTURING SYSTEMS AND DEPENDANCE ON THE GOVERNING PROCESSING VARIABLES IN CONVENTIONAL SUBTRACTIVE MACHINING BASES OF PROBABILITY AND STATISTICS

	Contents
	FREQUENCY DISTRIBUTIONS (4 H THEORY, 3 H EXERCISE) CLASSICAL PROBABILITY, CONDITIONAL PROBABILITY AND BAYES THEOREM, COMBINATORIAL ANALYSIS, DISCRETE AND CONTINUOUS VARIABLES. HISTOGRAMS AND POLYGONS OF FREQUENCY. POSITION AND VARIABILITY INDICES. MOMENTS OF A RANDOM VARIABLE. JOINT DISTRIBUTIONS. ELEMENTARY SAMPLING THEORY (15 H THEORY, 5 H EXERCISE) GENERATING PSEUDO-RANDOM NUMBERS AND RANDOM VARIABLES, SAMPLING WITH AND WITHOUT REPETITION, DISTRIBUTION OF X-BAR, DISTRIBUTION OF SUMS AND DIFFERENCES, STUDENT DISTRIBUTION, FISHER DISTRIBUTION. DECISION THEORY: CORRECT AND EFFICIENT PARAMETER ESTIMATES, CONFIDENCE INTERVALS, HYPOTHESIS TESTS, SIGNIFICANCE, COMPARISON OF AVERAGES, COMPARISON OF VARIANCES. EXPERIMENT PLANNING (10 H THEORY, 4 H EXERCISE) ONE-FACTOR EXPERIMENTS, FULL RANDOM DESIGN, ONE-WAY VARIANCE ANALYSIS, RESIDUE ANALYSIS, FACTORIAL DESIGN, FRACTIONAL DESIGN, TWO-WAY VARIANCE ANALYSIS, FACTOR INTERACTION ANALYSIS, RESPONSE SURFACES AND METAMODELLING. PROCESS CAPACITY ANALYSIS (5 H THEORY, 3 H EXERCISE) CAPACITY INDICES, CAPACITY ANALYSIS WITH HISTOGRAMS AND PROBABILITY CARDS, CAPACITY ANALYSIS WITH PLANNED EXPERIMENTS, ESTIMATION OF THE NATURAL TOLERANCE LIMITS OF A PROCESS. CONTROL CHARTS (6 H THEORY, 5 H EXERCISE) CHARTS FOR VARIABLES AND ATTRIBUTES, CONTROL LIMITS.

Contents

FREQUENCY DISTRIBUTIONS (4 H THEORY, 3 H EXERCISE)
CLASSICAL PROBABILITY, CONDITIONAL PROBABILITY AND BAYES THEOREM, COMBINATORIAL ANALYSIS, DISCRETE AND CONTINUOUS VARIABLES. HISTOGRAMS AND POLYGONS OF FREQUENCY. POSITION AND VARIABILITY INDICES. MOMENTS OF A RANDOM VARIABLE. JOINT DISTRIBUTIONS.

ELEMENTARY SAMPLING THEORY (15 H THEORY, 5 H EXERCISE)
GENERATING PSEUDO-RANDOM NUMBERS AND RANDOM VARIABLES, SAMPLING WITH AND WITHOUT REPETITION, DISTRIBUTION OF X-BAR, DISTRIBUTION OF SUMS AND DIFFERENCES, STUDENT DISTRIBUTION, FISHER DISTRIBUTION. DECISION THEORY: CORRECT AND EFFICIENT PARAMETER ESTIMATES, CONFIDENCE INTERVALS, HYPOTHESIS TESTS, SIGNIFICANCE, COMPARISON OF AVERAGES, COMPARISON OF VARIANCES.

EXPERIMENT PLANNING (10 H THEORY, 4 H EXERCISE)
ONE-FACTOR EXPERIMENTS, FULL RANDOM DESIGN, ONE-WAY VARIANCE ANALYSIS, RESIDUE ANALYSIS, FACTORIAL DESIGN, FRACTIONAL DESIGN, TWO-WAY VARIANCE ANALYSIS, FACTOR INTERACTION ANALYSIS, RESPONSE SURFACES AND METAMODELLING.

PROCESS CAPACITY ANALYSIS (5 H THEORY, 3 H EXERCISE)
CAPACITY INDICES, CAPACITY ANALYSIS WITH HISTOGRAMS AND PROBABILITY CARDS, CAPACITY ANALYSIS WITH PLANNED EXPERIMENTS, ESTIMATION OF THE NATURAL TOLERANCE LIMITS OF A PROCESS.

CONTROL CHARTS (6 H THEORY, 5 H EXERCISE)
CHARTS FOR VARIABLES AND ATTRIBUTES, CONTROL LIMITS.

	Teaching Methods
	TEACHING INCLUDES THEORETICAL CLASSES AND NUMERICAL EXERCISES, EVEN WITH EXCEL. PRESENTATIONS OF THE CONTENTS OF THE COURSE AND EXCEL SHEETS ARE AVAILABLE ONLINE AT HTTP://ELEARNING.DIMEC.UNISA.IT. THE PROBLEMS ARE INTENDED TO BE DISCUSSED IN COOPERATION BEFORE THE EVENTUAL SOLUTION BY THE PROFESSOR, SO THAT EACH STUDENT CAN PROCEED TO SELF-EVALUATION OF THE PREPARATION.

	Verification of learning
	EVALUATION CONSISTS OF THE ORAL EXAMINATION, AIMING TO EVALUATE THE SKILLS IN DESIGNING A PROPER EXPERIMENTAL PLAN, DISCUSSING THE RESPONSES AND SELECTING THE BEST SOLUTION AMONG SEVERAL POSSIBILITIES.