Ablation of Steel by Microsecond Pulse Trains

Laser micromachining is an important material processing technique used in industry and medicine to produce parts with high precision. Control of the material removal process is imperative to obtain the desired part with minimal thermal damage to the surrounding material. Longer pulsed lasers, with pulse durations of milli- and microseconds, are used primarily for laser through-cutting and welding. In this work, a two-pulse sequence using microsecond pulse durations is demonstrated to achieve consistent material removal during percussion drilling when the delay between the pulses is properly defined. The light-matter interaction moves from a regime of surface morphology changes to melt and vapour ejection. Inline coherent imaging (ICI), a broadband, spatially-coherent imaging technique, is used to monitor the ablation process. The pulse parameter space is explored and the key regimes are determined. Material removal is observed when the pulse delay is on the order of the pulse duration. ICI is also used to directly observe the ablation process. Melt dynamics are characterized by monitoring surface changes during and after laser processing at several positions in and around the interaction region. Ablation is enhanced when the melt has time to flow back into the hole before the interaction with the second pulse begins. A phenomenological model is developed to understand the relationship between material removal and pulse delay. Based on melt refilling the interaction region, described by logistic growth, and heat loss, described by exponential decay, the model is fit to several datasets. The fit parameters reflect the pulse energies and durations used in the ablation experiments. For pulse durations of 50 us with pulse energies of 7.32 mJ +/- 0.09 mJ, the logisitic growth component of the model reaches half maximum after 8.3 us +/- 1.1 us and the exponential decays with a rate of 64 us +/- 15 us. The phenomenological model offers an interpretation of the material removal process.

Towards Mapping of Rock Walls Using a UAV-Mounted 2D Laser Scanner in GPS Denied Environments

In geotechnical engineering, the stability of rock excavations and walls is estimated by using tools that include a map of the orientations of exposed rock faces. However, measuring these orientations by using conventional methods can be time consuming, sometimes dangerous, and is limited to regions of the exposed rock that are reachable by a human. This thesis introduces a 2D, simulated, quadcopter-based rock wall mapping algorithm for GPS denied environments such as underground mines or near high walls on surface. The proposed algorithm employs techniques from the field of robotics known as simultaneous localization and mapping (SLAM) and is a step towards 3D rock wall mapping. Not only are quadcopters agile, but they can hover. This is very useful for confined spaces such as underground or near rock walls. The quadcopter requires sensors to enable self localization and mapping in dark, confined and GPS denied environments. However, these sensors are limited by the quadcopter payload and power restrictions. Because of these restrictions, a light weight 2D laser scanner is proposed. As a first step towards a 3D mapping algorithm, this thesis proposes a simplified scenario in which a simulated 1D laser range finder and 2D IMU are mounted on a quadcopter that is moving on a plane. Because the 1D laser does not provide enough information to map the 2D world from a single measurement, many measurements are combined over the trajectory of the quadcopter. Least Squares Optimization (LSO) is used to optimize the estimated trajectory and rock face for all data collected over the length of a light. Simulation results show that the mapping algorithm developed is a good first step. It shows that by combining measurements over a trajectory, the scanned rock face can be estimated using a lower-dimensional range sensor. A swathing manoeuvre is introduced as a way to promote loop closures within a short time period, thus reducing accumulated error. Some suggestions on how to improve the algorithm are also provided.