Collaborative Research: Experimental Design and Analysis of Quantitative-Qualitative Responses in Manufacturing and Biomedical Systems

该奖项的研究目标是为具有定量和定性(QQ)响应的系统建立一套实验设计、分析和质量控制方法。具有QQ响应的系统在各种制造和生物医学应用中经常遇到。例如，在热喷涂过程中，涂层质量通常通过定量的表面粗糙度和定性的涂层失效指示器来衡量。另一个例子是在器官移植过程中，使用定量活组织检查分数和医生的定性评估来评估器官的健康状况。新的方法基于对QQ响应的联合建模，以揭示两种类型响应之间的隐藏关系，因此比传统的分别对两种类型的响应建模的方法更具优势。这些方法将在行业合作者提供的真实案例研究中进行测试和实施。该教育计划将促进来自代表性不足群体的本科生和研究生在各种制造和生物医学应用方面获得严格的质量控制培训。研究成果将用于提高实际工业实践中的质量。如果成功，这项研究将导致有效和可靠的建模、实验设计、监测和控制具有QQ响应的系统。联合建模方法考虑了联合变量选择的约束似然估计。它将进一步适应各种复杂的场景，包括功能预测值、缺失值、多个响应和非线性质量-过程关系。QQ响应的贝叶斯优化设计是为了满足实验设计的需要，可以扩展到序贯设计和鲁棒系统的设计。基于建模方法，面向模型的过程监控将检测过程变化，并从预测者或系统本身识别根本原因。质量的提高将通过优化目标来实现，这些目标考虑了QQ回复之间的隐藏关系。这些方法将为生产放大和生物医学质量改进提供一套强大的工具。此外，教育计划将为本科生和研究生提供各种课程模块和研究机会。真实的案例研究将在实验室课程和本科生顶峰项目中使用。这项研究将使学生在应用统计学、制造业和生物医学系统方面具备批判性思维和动手技能。研究成果还将通过出版物、会议和研讨会向学术界和工业界传播。将建立一个网站，以共享数据、案例研究和促进研究合作的先进工具。

The research objective of this award is to establish a set of experimental design, analysis, and quality control methodologies for systems with both quantitative and qualitative (QQ) responses. Systems with QQ responses are commonly encountered in various manufacturing and biomedical applications. For example, in a thermal spray coating process, the coating quality is often measured by quantitative surface roughness and a qualitative coating failure indicator. Another example can be found in an organ transplant process, where the quantitative biopsy testing scores and doctors, qualitative evaluation are used to evaluate the health condition of the organ. The new methodologies are based on a joint modeling of the QQ responses to unveil the hidden relationship between the two types of the responses, and thus are more advantageous than the traditional methods that model the two types of responses separately. These methodologies will be tested and implemented in real case studies provided by industrial collaborators. The education plan will promote undergraduate and graduate students from the underrepresented groups to obtain the rigorous quality control training in various manufacturing and biomedical applications. The research results will be used to improve the quality in real industrial practices. If successful, this research will lead to effective and reliable modeling, experimental design, monitoring and control of the systems with QQ responses. The joint modeling method considers constrained likelihood estimation with joint variable selection. It will further accommodate various complex scenarios including functional predictors, missing values, multiple responses, and nonlinear quality-process relationship. A Bayesian optimal design of QQ responses is planned to address the experimental design needs, which can be extended to sequential designs and designs for robust systems. Based on the modeling method, the model-oriented process monitoring will detect process changes, and identify root causes from the predictors or from the system itself. The quality improvement will be fulfilled by optimizing objectives accounting for the hidden relationship between the QQ responses. These methodologies will serve a set of powerful tools for manufacturing scale-up and biomedical quality improvement. Moreover, the educational plan will provide undergraduate and graduate students with various course modules and research opportunities. Real case studies will be used in lab sessions and undergraduate capstone projects. The research will equip students with critical thinking and hands-on skills in applied statistics, manufacturing, and biomedical systems. Research results will also be disseminated to academia and industry through publications, conferences, and workshops. A website will be established to share data, case studies, and advanced tools for promoting research collaboration.