| (*)1. Statistical Analysis and Application:
Students can apply statistical methods to analyze data from polymer processing, recall and correctly apply statistical concepts and calculations to data-driven problems, and make informed decisions. (k3, k5)
2. Process Optimization:
Students are able to understand, execute, and interpret optimization methods such as Design of Experiments (DOE) and Statistical Process Control (SPC) to improve polymer processes, identify process parameters that affect quality and efficiency, and develop optimization strategies. (k5, k6)
3. Advanced Method Adaptation:
Students can adapt advanced optimization methods, such as Taguchi Robustness, Gray Relational Analysis, and Fuzzy Systems, to specific challenges in polymer processing and use these methods to address complex optimization problems. (k6)
4. Software-Supported Process Analysis:
Students can effectively use Minitab software to analyze and optimize practical examples in polymer processing, conduct statistical modeling, and evaluate process performance. (k4)
5. Process Monitoring and Control:
Students can utilize statistical process control methods, such as SPC and Gage R&R, to evaluate and monitor process stability, ensuring consistent quality and efficiency in polymer processing. (k5)
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| (*)1. Recall and apply statistical fundamentals to data-driven challenges in polymer processing. (k3)
2. Conduct and interpret Design of Experiments (DOE) for process optimization. (k5)
3. Apply the Taguchi method to develop robust processes. (k5)
4. Utilize Gray Relational Analysis for decision-making in multi-objective scenarios. (k4)
5. Apply Fuzzy Systems to model and optimize uncertain and complex systems. (k5)
6. Perform Gage R&R analyses to evaluate measurement system capability. (k5)
7. Use Statistical Process Control (SPC) to monitor and optimize processes. (k5)
8. Employ Minitab for statistical analysis, process modeling, and data visualization. (k4)
9. Analyze real-world case studies and develop data-driven optimization solutions. (k6)
10. Document and present optimization results in a scientific and technical context. (k4)
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(*)1. Fundamentals of statistics, including distributions, mean, variance, and hypothesis testing. (k3)
2. Concept and applications of Design of Experiments (DOE) in process optimization. (k4)
3. Principles of the Taguchi method for developing robust processes. (k4)
4. Fundamentals and applications of Gray Relational Analysis for decision-making. (k4)
5. Concept of Fuzzy Systems and their applications in polymer processing. (k4)
6. Methods of Statistical Process Control (SPC), including process capability analysis. (k4)
7. Conducting and interpreting Gage R&R analyses for measurement system evaluation. (k4)
8. Use of Minitab for statistical analysis and data-driven optimization. (k3)
9. Practical applications and case studies for data-driven process optimization in polymer processing. (k4)
10. Interaction between statistical models and real-world processes to enhance process performance and quality. (k5)
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