COURSE OVERVIEW:

Design of Experiments (DOE) is a sophisticated statistical methodology used to systematically determine the relationship between factors affecting a process and the output of 그 process. This course provides engineers and quality professionals with the tools to move beyond "trial and error" or "one-factor-at-a-time" testing, which are often inefficient and misleading. By using DOE, participants will learn how to conduct structured experiments that yield maximum information with the minimum number of trials, leading to rapid process breakthroughs.

The scope of this training covers the entire experimental workflow, from initial screening of potential factors to the final optimization of process settings. Attendees will explore various experimental designs, including Full Factorial, Fractional Factorial, and Response Surface Methodologies (RSM). The curriculum emphasizes the practical application of these techniques to identify significant interactions between variables that traditional testing methods often overlook, thereby solving complex quality issues and reducing manufacturing costs.

Coverage includes the statistical foundations of DOE, such as hypothesis testing, analysis of variance (ANOVA), and regression analysis. Participants will learn how to use statistical software to design experiments, analyze the resulting data, and interpret "main effect" and "interaction" plots. The course also addresses the practical challenges of conducting experiments in a live production environment, providing strategies for randomization, blocking, and replication to ensure that experimental results are both valid and reproducible.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

• Explain the statistical logic of DOE compared to traditional testing methods.
• Identify and prioritize factors (inputs) and responses (outputs) for experimentation.
• Utilize Full Factorial designs to study all possible combinations of factors.
• Apply Fractional Factorial designs to screen many factors with fewer resources.
• Perform Analysis of Variance (ANOVA) to determine statistical significance.
• Interpret Interaction Plots to understand how factors influence each other.
• Use Response Surface Methodology (RSM) to find optimal process settings.
• Implement Blocking and Randomization to eliminate environmental bias.
• Optimize multiple responses simultaneously using Desirability Functions.
• Conduct Robust Design experiments (Taguchi methods) to reduce sensitivity to noise.
• Verify experimental results through systematic confirmation runs.
• Communicate statistical findings and optimization recommendations to stakeholders.

TARGET AUDIENCE:

Process Engineers, Manufacturing Engineers, R&D Scientists, Six Sigma Black Belts, Quality Assurance Professionals, and Data Analysts in the manufacturing sectors.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximize the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.