<p dir="ltr">This project aimed to develop an instrumentation and control system for a micro-drilling robot based on Industrial Internet of Things (IIoT) technologies. The automation system integrated IIoT technological tools to create a robust automation system capable of being used in drilling operations. The system incorporated industrial-grade sensors, which carried out direct measurements of the critical variables of the process. The indirect variables relevant to the control of the robot were calculated from the measured parameters. The system also considered the telemetry architecture necessary to reliably transmit data from the down-the-hole (DTH) robot to a receiver on the surface. Telemetry was based on wireless communication through long-range radio frequency (LoRa). The system developed had models based on Artificial Intelligence (AI) and Machine Learning (ML) for determining the mode of operation, detecting changes in the process, and changes in drilling variables in critical hydraulic components for the drilling process. Algorithms based on AI and ML models also allowed the user to make better decisions based on the variables' correlation to optimize the drilling process (e.g., dynamic change of flow, pressure, and RPMs based on automatic rock identification). A user interface (UI) was developed, and digital tools to perform data analysis were implemented. Safety assessment in all robot systems (e.g., electrical, hardware, software) was contemplated as a critical design component. The result of this research project provides innovative micro-drilling robots with the necessary technological tools to optimize the drilling process. The system made drilling more efficient, reliable, and safe, providing diagnostic and prognostic tools that allowed planning maintenance based on the actual health of the devices. The system that was developed was tested in a test bench under controlled conditions within a laboratory to characterize the system and collect data that allowed ML models' development, training, validation, and testing. The prototype of a micro-drilling robot installed on the test bench served as a case study to assess the implemented models' reliability and the proposed telemetry.</p>

The oil and gas industry faces a lack of compact drilling devices capable of performing horizontal drilling maneuvers in depleted or abandoned wells in order to enhance oil recovery. The purpose of this project was to design and develop a smart sensing system that can be later implemented in compact drilling devices used to perform horizontal drilling to enhance oil recovery in wells. A smart sensor is the combination of a sensing element (sensor) and a microprocessor. Hence, a smart sensing system is an arrangement that consists of different sensors, where one or more have smart capabilities. The sensing system was built and tested in a laboratory setting. For this, a test bench was used as a case study to simulate the operation from a micro-drilling device. The smart sensing system integrated the sensors essential for the direct operational measurements required for the robot. The focus was on selecting reliable and sturdy components that can handle the operation Down the Hole (DTH) on the final lateral micro-drilling robot. The sensing system's recorded data was sent to a microcontroller, where it was processed and then presented visually to the operator through a User Interface (UI) developed in a cloud-based framework. The information was filtered, processed, and sent to a controller that executed commands and sent signals to the test bench’s actuators. The smart sensing system included novel modules and sensors suitable for the operation in a harsh environment such as the one faced in the drilling process. Furthermore, it was designed as an independent, flexible module that can be implemented in test benches with different settings and early robotic prototypes. The outcome of this project was a sensing system able to provide robotic drilling devices with flexibility while providing accurate and reliable measurements during their operation.

<p>Multifunctional additive manufacturing has the immense potential of addressing present needs within structural health monitoring by enabling a new additive manufacturing paradigm that redefines what a sensor is, or what sensors should resemble. To achieve this, the properties of printed components must be precisely tailored to meet structure specific and application specific requirements. However due to the limited number of commercially available multifunctional filaments, this research investigates the in-house creation of adaptable piezoresistive multifunctional filaments and their potential within structural health monitoring applications based upon their characterized piezoresistive responses. To do so, a rigid polylactic acid based-filament and a flexible thermoplastic polyurethane based-filament were modified to impart piezoresistive properties using carbon nanofibers. The filaments were produced using different mixing techniques, nanoparticle concentrations, and optimally selected manufacturing parameters from a design of experiments approach. The resulting filaments exhibited consistent resistivity values which were found to be less variable under specific mixing techniques than commercially available multifunctional filaments. This improved consistency was found to be a key factor which held back currently available piezoresistive filaments from fulfilling needs within structural health monitoring. To demonstrate the ability to meet these needs, the piezoresistive responses of three dog-bone shaped sensor sizes were measured under monotonic and cyclic loading conditions for the optimally manufactured filaments. The characterized piezoresistive responses demonstrated high strain sensitivities under both tensile and compressive loads. These piezoresistive sensors demonstrated the greatest sensitivity in tension, where all three sensor sizes exhibited gauge factors over 30. Cyclic loading supported these results and further demonstrated the accuracy and reliability of the printed sensors within SHM applications.</p>