Product quality evaluation by confidence intervals of process yield index

Area of Science:

• Industrial Engineering
• Statistical Quality Control
• Manufacturing Process Analysis

Background:

• Statistical techniques are crucial for managing process variability in manufacturing.
• Process Capability Indices (PCIs) offer a straightforward method for industrial application.
• The process yield index accurately measures process yield, derived from unilateral six sigma quality indices.

Purpose of the Study:

• To develop confidence intervals for the process yield index.
• To utilize joint confidence regions of unilateral six sigma quality indices for product quality characteristics.
• To enable statistical inferences for assessing and improving manufacturing process capability.

Main Methods:

• Development of confidence intervals for the process yield index.
• Application of joint confidence regions for two unilateral six sigma quality indices.
• Integration of confidence regions to determine product yield index confidence intervals.

Main Results:

• Established a method for calculating confidence intervals of the process yield index.
• Provided a framework for assessing process capability against required levels.
• Demonstrated application with an example of a driver integrated circuit.

Conclusions:

• The developed confidence intervals empower manufacturing industries to perform statistical inferences on process capability.
• This approach aids in evaluating if product quality meets specifications and identifies areas for enhancement.
• Facilitates data-driven decision-making for continuous improvement in manufacturing processes.