<p>An on-line quality assessment system in the industry is essential to prevent artifacts and guide manufacturing processes. Some well-developed systems can diagnose problems and help control the output qualities. However, some of the conventional methods are limited in time consumption and cost of expensive human labor. So, more efficient solutions are needed to guide future decisions and improve productivity. This thesis focuses on developing two image-based machine learning systems to accelerate the manufacturing process: one is to benefit nitrate sensor fabrication, and the other is to help image quality control for inkjet printers.</p> <p><br></p> <p>In the first work, we propose a system for predicting the nitrate sensor's performance based on non-contact images. Nitrate sensors are commonly used to reflect the nitrate levels of soil conditions in agriculture. In a roll-to-roll system, for manufacturing thin-film nitrate sensors, varying characteristics of the ion-selective membrane on screen-printed electrodes are inevitable and affect sensor performance. It is essential to monitor the sensor performance in real-time to guarantee the quality of the sensor. We also develop a system for predicting the sensor performance in on-line scenarios and making the neural networks efficiently adapt to the new data.</p> <p><br></p> <p>Streaks are the number one image quality problem in inkjet printers. In the second work, we focus on developing an efficient method to model and predict missing jets, which is the main contributor to streaks. In inkjet printing, the missing jets typically increase over printing time, and the print head needs to be purged frequently to recover missing jets and maintain print quality. We leverage machine learning techniques for developing spatio-temporal models to predict when and where the missing jets are likely to occur. The prediction system helps the inkjet printers make more intelligent decisions during customer jobs. In addition, we propose another system that will automatically identify missing jet patterns from a large-scale database that can be used in a diagnostic system to identify potential failures.</p>

<p dir="ltr">The present era of industrial growth, along with the rise in big data, has led to an increase in the demand for data-savvy professionals employing visualization techniques and software to fully leverage the value of this data. Since data visualization is an expansive and intricate field, it leads to challenges for novice learners as they seek to understand it. The Data Visualization Capacity (DVC) Tool is an online learning platform designed to enhance data visualization literacy amongst learners. The DVC Tool encompasses fundamental principles and techniques essential for proficient data visualization, by including external resources, quizzes, and tutorials in a distance-based modular format.</p><p dir="ltr">This study investigates the usability of the DVC Tool using a mixed-methods approach combining quantitative analysis of Google Analytics data, System Usability Scale (SUS) questionnaires, and qualitative insights from usability testing sessions and interviews. The research aims to assess the effectiveness of the DVC Tool across diverse user profiles and identify strategies for optimizing user experience. User studies were conducted with participants from various backgrounds and experience in data visualization to gain insight into the strengths and weaknesses of the DVC Tool, as well as gain recommendations for effective learning strategies and user experience design. The findings reveal a high overall usability rating for the DVC Tool, with users from various educational backgrounds and levels of expertise expressing satisfaction with its functionality and organization. The SUS usability scores indicate a mean usability score of 81.8, highlighting the tool's effectiveness in providing a user-friendly learning experience for all users across diverse profiles. Interviews also give insight into the importance of clear organization, visual aids, and custom learning plans to enhance the learning experience of the student.</p><p dir="ltr">In general, this research contributes to the advancement of data visualization education by providing insights into effective instructional strategies and components of digital learning platforms. The findings offer practical implications for educators and developers looking to enhance data visualization literacy among learners, while also addressing theoretical gaps in usability research within the field.</p>