Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.

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Wire arc additive manufacturing can produce thin-walled components at much faster rates than conventional subtractive manufacturing processes. The as-built components have a poor surface finish, which requires post-processing via machining. However, tool reach limitations of cutting tools means that the desired component must be produced in sections, where the hybrid manufacturing process is conducted iteratively. A key aspect of this hybrid manufacturing process is the deposition of the first bead onto the previously machined component section, a thin-walled substrate. Since the deposition traverse speed and wire feed rate heavily impact geometry, and therefore the subsequent machining process, this work seeks to analyze the thin-wall substrate deposition geometry resulting from different deposition process parameters. Additionally, this work assesses the impact of the deposition geometry on subsequent machining by assessing the area of excess material which must be removed to achieve net shape geometry. Traverse speed was found to have the largest impact on bead morphology, with wire feed speed having an increased impact at higher traverse speeds. The penetration and remelting geometry of the samples was investigated and found to contribute to the relatively uniform bead height seen in the different samples.

Electroactive polymers (EAP) have shown promise in producing significant and controllable linear displacement in slim and lightweight packages. EAPs allow for seamless integration and multi-functionality since they are actuated by a driving voltage that could be controlled by a microprocessor. Polyacrylamide (PAAM)/Polyacrylic acid (PAA) hydrogel EAPs are commonly chosen due to their low driving voltage, significant amount of displacement, and rapid manufacturing capabilities, as these gels can be 3D printed. To effectively extrude these gels in 3D printers, their viscosity, gelation time, shear thinning, and self-wettability must be characterized. In this research, ungelled solutions of PAAM are prepared and then strain-tested at temperatures from 60C to 80C and with 1–2 drops of TEMED catalyst to determine the gelation time that is optimal for 3D printing. Strain testing of ungelled PAAM solutions is also used to determine the shear thinning propertie of the gel. All strain testing is conducted using a rheometer with 25 mm diameter plates and an oven enclosure. A prototype extrusion system is designed and fabricated to be used for self-wettability testing of the gel. The process data will then be used in the design of a modified 3D printer to manufacture and test different configurations of these hydrogel actuators.

Wire arc additive manufacturing (WAAM) is a metal additive process that allows for constructing diverse parts using an electric arc and metal wire feed stock. It provides higher deposition rates and larger build areas that make it an attractive technology for industry. An important element of WAAM process control is maintaining specific contact tip to work-piece distance (CTWD). Cumulative errors caused by modeling inaccuracies and thermal conditions can create an increase in CTWD during processing. In the present study, acoustic signals were investigated as a non-invasive form of monitoring this phenomenon and were compared with traditional current-based sensing approaches. Experiments were conducted across a range of controlled CTWD conditions. From the acoustic signals, 35 different statistical features were extracted and machine learning strategies were utilized to correlate them to the CTWD. Random forests composed of 50 trees each were chosen to classify the CTWD into three different levels of CTWD granularity. The ability of random forest algorithms to detect varying levels of CTWD was investigated and the models showed limited performance especially for high granularity CTWD predictions when compared to current sensing approaches. The implications for these results in rapid implementation for high-level process monitoring of process condition are briefly discussed.