Technical Scope

Our approach focused on quantifying process variation and identifying root causes to implement targeted solutions. The key technical aspects included:

• Chemical Composition Analysis: Examined variations in Silicon (Si), Iron (Fe), Copper (Cu), Manganese (Mn), Magnesium (Mg), Zinc (Zn), Titanium (Ti), Nickel (Ni), and Lead (Pb) using spectrometer data and control charts.

• Process Capability Assessment: Evaluated the Cp and Cpk values for key elements to determine whether the process could consistently meet specifications.

• Raw Material Supplier Evaluation: Compared incoming material quality to Niche’s specifications to determine whether variability originated from suppliers.

• Equipment and Process Variation Analysis: Assessed differences in furnace performance across multiple units to identify inconsistencies in melting conditions.

• Defect Analysis and Yield Optimization: Examined scrap rates, rework rates, and overall yield to evaluate process efficiency and pinpoint areas for improvement.

Design Process and Workflows

We followed a systematic approach to analyze and improve the foundry’s melting process:

1. Data Collection and KPI Identification

• Extracted spectrometer data from 388 melt samples taken throughout 2019.

• Identified chemical composition limits for each element based on industry standards.

• Evaluated additional process variables, including furnace temperatures, defect rates, and cycle times.

2. Process Control Chart Development and Analysis

• Generated X-bar and R-charts for each key element to assess process stability.

• Analyzed furnace-to-furnace variation to determine if differences in equipment performance contributed to inconsistencies.

• Conducted root cause analysis using the Ishikawa (fishbone) diagram to attribute variation to material, method, or machinery.

3. Capability Analysis and Performance Benchmarking

   • Computed process capability indices (Cp, Cpk) to quantify how well the process met specifications.

   • Compared supplier material quality against Niche’s internal standards to determine if incoming ingots contributed to variation.

4. Development of Improvement Strategies

   • Proposed real-time monitoring systems to continuously track temperature and composition data.

   • Recommended increasing sampling frequency to at least one sample per batch instead of daily testing.

   • Suggested operator training programs to ensure consistency in sampling, testing, and documentation.

   • Advocated for regular equipment calibration and standardized procedures to minimize process variability.

Project Outcomes

• Improved Process Stability: Identified key sources of variation and provided recommendations for reducing chemical composition inconsistencies.

• Enhanced Process Capability: Found that elements like Copper, Manganese, and Zinc had poor capability (Cp < 1.0), requiring better process control measures.

• Reduced Defect Rates: Estimated that 0.773% of parts produced in 2019 were out of specification, reinforcing the need for stricter process controls.

• Optimized Data Collection and Monitoring: Proposed real-time data tracking and control chart implementation to enable proactive corrective actions.

Reflections

This project demonstrated the critical role of Statistical Process Control (SPC) in manufacturing quality assurance. Through this analysis, I gained hands-on experience in:

• Interpreting process control charts and identifying out-of-control conditions.

• Assessing process capability indices (Cp, Cpk) to determine if a process meets specifications.

• Using data-driven decision-making to improve process stability and efficiency.

• Collaborating in a multidisciplinary team to analyze complex manufacturing challenges and propose feasible solutions.