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）反应的系统建立一套实验设计、分析和质量控制方法。具有QQ响应的系统通常在各种制造和生物医学应用中遇到。例如，在热喷涂涂层过程中，通常通过定量的表面粗糙度和定性的涂层失效指标来测量涂层质量。另一个例子可以在器官移植过程中找到，其中定量活检测试分数和医生，定性评价是用来评估器官的健康状况。新方法基于对QQ回复的联合建模，揭示了两类回复之间的隐藏关系，因此比单独对两类回复建模的传统方法更具优势。这些方法将在工业合作者提供的实际案例研究中进行测试和实施。该教育计划将促进来自弱势群体的本科生和研究生在各种制造和生物医学应用中获得严格的质量控制培训。研究结果将用于实际工业实践中提高产品质量。如果成功，本研究将对具有QQ响应的系统进行有效可靠的建模、实验设计、监测和控制。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、缺失值、多重响应和非线性质量过程关系。针对实验设计的需要，提出了QQ响应的贝叶斯优化设计方法，并将其推广到序列设计和鲁棒系统设计中。基于建模方法，面向模型的流程监控将检测流程更改，并从预测器或系统本身识别根本原因。考虑到QQ回复之间的隐藏关系，通过优化目标来实现质量提升。这些方法将为扩大生产规模和提高生物医学质量提供一套强有力的工具。此外，该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。真实案例研究将用于实验课程和本科毕业项目。该研究将使学生具备应用统计、制造和生物医学系统的批判性思维和动手技能。研究成果也将通过出版物、会议和讲习班向学术界和工业界传播。将建立一个网站，分享数据、案例研究和先进的工具，以促进研究合作。

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.