DOE: Design of Experiments for Process Optimization with Zero Capital Investment (QIT103)

25 April 2017 - 26 April 2017

OBJECTIVES
Upon successful completion of training, participants will be able to:
• Appreciate the Power of DOE in process optimization
• Select the optimum operating condition for a process
• Apply the effects of main factors and interactions to improve products Quality, productivity and Reduce Cost of production
• Determine the most critical process parameters to control in SPC charts or other means of control
• Perform the most efficient and effective experiment to study the behavior of a process and in the appropriate sequence

COURSE OUTLINE

MODULE 1: GENERAL INTRODUCTION
• Introduction to the Statistical Concepts on Process Variation
• Some basic concepts on Statistical Inferences e.g. Hypothesis Testing, Confidence level of estimation, methods of comparing 2 distributions
• Model Building for a process from the observed data
• Determination of Factors affecting a process and Coding of Factors
• One Factor at One Time Experiments - Weakness and Pitfalls
• Effects of Factors (Differentiation between Dummy and Critical Factors)
• Cause and Effect Relationship of Process, Products and Robust Designs
• Screening Experiments versus Fine Tuning Experiments
• Correlation Studies and Auto-correlation application in process data analysis
• Deciding the appropriate parameters for optimization
• Exercise: Optimization for a process & sub-process with reference to process flow chart

MODULE 2: FACTORIAL DESIGNS
• Comparison between One Factor at One Time Experiments and Factorial Experiments
• Factorial Designs -- Method to construct the Experiment and its powers
• Estimation of effects for main factors and interactions between factors
• Application of Yates Algorithm to determine the effects of main factors and interactions
• Interpretation of experiment data and set up optimum condition (Low Cost/High Quality) for the process
• Deciding effects of centre points
• The physical constraints and limitations to run Factorial Experiments
• The appropriate procedures to run a Factorial Experiment
• Real life Ffactorial Experiment (Molding Process Optimization) exercise
• Interpretation of experiment results and optimization of process performance using Capability Index (Cpk)
• Case studies on DOE for Electronic Assembly Process

MODULE 3: FRACTIONAL FACTORIAL DESIGNS
• General Introduction to Types of Fractional Factorial Experiments, E.g. Taguchi, Plackett and BurmanDesign Etc.
• Method to Construct Fractional Factorial Experiments from Factorial Experiments
• The Confounding of Effects of Factors in Fractional Factorial Experiments
• Method of Extracting Information from the Fractional Factorial Experimental data
• Fold Over and De-confounding of the Fractional Factorial Experiments
• Determining the Critical process parameters
• Set up the optimum operations (Low Cost/High Quality) condition for the process
• Real Life Application of Fractional Factorial Experiments
• Comparison between Factorial and Fractional Factorial Experiments, the Strength and weakness
• Interpretation of Experiment Results and Optimization of Process Performance using Capability Index (Cpk)
• Case studies: Chemical reaction process optimization

MODULE 4: CONCLUSION
• The Sequential Approach of Experimentation
• The Economical Consideration of Experiments
• The need of blocking and randomization in experimental runs
• Important points to take note during any experimentation and data collection
• Checklist -- Proper Procedures to start experimentation

WHO SHOULD ATTEND
Engineers, managers and technical personnel who will be involved in designing experiments for process improvement

DURATION
2 days @ 9am – 5pm

COURSE FEE
PSDC Member: RM 690
Non-Member: RM 750
*All prices listed above are subjected to GST effective 1st April 2015

HRDF
SBL Khas Scheme