### Title: Optimization and Quality Control of Cylinder Head Casting in Automotive Manufacturing Using Statistical Process Control and Machine Learning

### Abstract
The cylinder head is a critical component in automotive engines, significantly influencing engine performance, efficiency, and durability. This thesis explores the integration of Statistical Process Control (SPC) and Machine Learning (ML) techniques to optimize and enhance the quality control processes in cylinder head casting within automotive manufacturing. The study aims to identify key process parameters, implement real-time monitoring, and predict defects to achieve higher production efficiency and product quality.

### Chapter 1: Introduction
1.1 Background
- Importance of cylinder heads in automotive engines
- Challenges in cylinder head casting
- Need for optimization and quality control

1.2 Objectives
- To optimize the casting process parameters
- To implement SPC for real-time monitoring
- To utilize ML for defect prediction and process optimization

1.3 Scope and Limitations
- Focus on cylinder head casting in automotive manufacturing
- Use of SPC and ML techniques
- Consideration of practical constraints in industrial settings

### Chapter 2: Literature Review
2.1 Cylinder Head Casting Process
- Overview of the casting process
- Common defects and their causes

2.2 Statistical Process Control (SPC)
- Principles of SPC
- Application of SPC in manufacturing
- Case studies in automotive manufacturing

2.3 Machine Learning in Manufacturing
- Overview of ML techniques
- Application of ML in quality control and process optimization
- Case studies in automotive manufacturing

### Chapter 3: Methodology
3.1 Data Collection
- Identification of process parameters
- Data sources and collection methods

3.2 Statistical Process Control Implementation
- Selection of control charts
- Establishing control limits
- Real-time monitoring and feedback mechanisms

3.3 Machine Learning Model Development
- Data preprocessing
- Feature selection and engineering
- Model selection (e.g., Random Forest, Neural Networks)
- Training, validation, and testing of ML models

3.4 Integration of SPC and ML
- Synergy between SPC and ML
- Real-time defect prediction
- Continuous process optimization

### Chapter 4: Results and Discussion
4.1 SPC Analysis
- Control chart results
- Identification of special causes of variation
- Process capability analysis

4.2 Machine Learning Model Performance
- Accuracy, precision, recall, and F1-score of ML models
- Comparison of different ML algorithms
- Case studies of defect prediction

4.3 Integrated Approach Outcomes
- Improvement in process parameters
- Reduction in defect rates
- Enhanced production efficiency

### Chapter 5: Conclusion and Recommendations
5.1 Summary of Findings
- Key insights from SPC and ML implementation
- Impact on cylinder head casting quality

5.2 Recommendations for Industry
- Best practices for SPC and ML integration
- Continuous improvement strategies

5.3 Future Work
- Advanced ML techniques (e.g., deep learning)
- Expansion to other manufacturing processes
- Integration with Industry 4.0 technologies

### References
- Comprehensive list of academic papers, industry reports, and books referenced throughout the thesis.

### Appendices
- Detailed data sets
- Additional charts and graphs
- Code snippets for ML models

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### Summary
This thesis demonstrates the effective integration of Statistical Process Control and Machine Learning to optimize and enhance the quality control of cylinder head casting in automotive manufacturing. By leveraging real-time monitoring and predictive analytics, significant improvements in process efficiency and product quality can be achieved, ultimately leading to higher customer satisfaction and reduced manufacturing costs.