Effective quality improvement in the manufacturing industry is continually pursued. There is an increasing demand for real-time fault detection, and avoidance of destructive post-process testing. Therefore, it is desirable to employ sensors for in-process monitoring, allowing for real-time quality assurance. Chapter 3 describes the application of sensor based monitoring to additive manufacturing, in which sensors are attached to a desktop model fused deposition modeling machine, to collect data during the manufacturing process. A design of experiments plan is conducted to provide insight into the process, particularly the occurrence of process failure. Subsequently, machine learning classification techniques are applied to detect such failure, and successfully demonstrate the future potential of this platform and methodology. Chapter 4 relates the application of online, image-based quantification of the surface quality of workpieces produced by cylindrical turning. Representative samples of cylindrical shafts, machined by turning under various conditions, are utilized, and an apparatus is constructed for acquiring images while the part remains mounted on a lathe. The surface quality of these specimens is analyzed, employing an algebraic graph theoretic approach, and preliminary regression modeling displays an average surface roughness (Ra) prediction error of less than 8%. Prediction occurs in less than 2 seconds, showing the capability for future application in a real-time, quality control setting. Both of these cases, in additive manufacturing and in turning, are validated using real experimental data and analysis, showing application of sensor-based online process monitoring in multiple manufacturing areas. / Master of Science / Effective quality improvement in the manufacturing industry is continually pursued, and there is an increasing demand for real-time quality monitoring. Therefore, it is desirable to employ sensors for in-process monitoring, allowing for real-time quality assurance. This is explored in two manufacturing areas. The first section of this work is in the area of additive manufacturing (“3D printing”), in which sensors are attached to a desktop model machine, to collect data during the printing process. Experiments are conducted to provide insight into how the process behaves, particularly the occurrence of printing failure. Machine learning classification techniques are then applied to detect such failure, and successfully demonstrate the future potential of this platform and methodology, for real-time monitoring of the process. The second section of this work relates to the conventional machining process of turning, and describes the application of image-based measurement of surface roughness. An apparatus is constructed for acquiring images, while the cylindrically turned shaft remains mounted on the lathe. The surface roughness is measured, and preliminary modeling displays an average surface roughness prediction error of less than 8%. This prediction occurs in less than 2 seconds, showing the capability for future application in a real-time, quality control setting. Both of these cases, in additive manufacturing and in turning, show the application of sensor-based monitoring in various manufacturing areas. This work provides a basis for future research and application, demonstrating how this sensor-based monitoring approach may be used for real-time quality monitoring in manufacturing.