IN-SITU CHARACTERIZATION OF SURFACE QUALITY IN γ-TiAl AEROSPACE ALLOY MACHINING

The functional performance of critical aerospace components such as low-pressure turbine blades is highly dependent on both the material property and machining induced surface integrity. Many resources have been invested in developing novel metallic, ceramic, and composite materials, such as gamma-titanium aluminide (γ-TiAl), capable of improved product and process performance. However, while γ-TiAl is known for its excellent performance in high-temperature operating environments, it lacks the manufacturing science necessary to process them efficiently under manufacturing-specific thermomechanical regimes. Current finish machining efforts have resulted in poor surface integrity of the machined component with defects such as surface cracks, deformed lamellae, and strain hardening. This study adopted a novel in-situ high-speed characterization testbed to investigate the finish machining of titanium aluminide alloys under a dry cutting condition to address these challenges. The research findings provided insight into material response, good cutting parameter boundaries, process physics, crack initiation, and crack propagation mechanism. The workpiece sub-surface deformations were observed using a high-speed camera and optical microscope setup, providing insights into chip formation and surface morphology. Post-mortem analysis of the surface cracking modes and fracture depths estimation were recorded with the use of an upright microscope and scanning white light interferometry, In addition, a non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN) was developed to achieve a real-time total volume crack monitoring capability. This approach showed good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, thereby establishing the significant potential for real-time quality monitoring in manufacturing processes. The findings from this present study set the tone for creating a digital process twin (DPT) framework capable of obtaining more aggressive yet reliable manufacturing parameters and monitoring techniques for processing turbine alloys and improving industry manufacturing performance and energy efficiency

Computer Numerical Controlled (CNC) machining for Rapid Manufacturing Processes

The trends of rapid manufacturing (RM) have influenced numerous developments of technologies mainly in additive processes. However, the material compatibility and accuracy problems of additive techniques have limited the ability to manufacture end-user products. More established manufacturing methods such as Computer Numerical Controlled (CNC) machining can be adapted for RM under some circumstances. The use of a 3-axis CNC milling machine with an indexing device increases tool accessibility and overcomes most of the process constraints. However, more work is required to enhance the application of CNC for RM, and this thesis focuses on the improvement of roughing and finishing operations and the integration of cutting tools in CNC machining to make it viable for RM applications. The purpose of this research is to further adapt CNC machining to rapid manufacturing, and it is believed that implementing the suggested approaches will speed up production, enhance part quality and make the process more suitable for RM. A feasible approach to improving roughing operations is investigated through the adoption of different cutting orientations. Simulation analyses are performed to manipulate the values of the orientations and to generate estimated cutting times. An orientations set with minimum machining time is selected to execute roughing processes. Further development is carried out to integrate different tool geometries; flat and ball nose end mill in the finishing processes. A surface classification method is formulated to assist the integration and to define the cutting regions. To realise a rapid machining system, the advancement of Computer Aided Manufacturing (CAM) is exploited. This allows CNC process planning to be handled through customised programming codes. The findings from simulation studies are supported by the machining experiment results. First, roughing through four independent orientations minimized the cutting time and prevents any susceptibility to tool failure. Secondly, the integration of end mill tools improves surface quality of the machined parts. Lastly, the process planning programs manage to control the simulation analyses and construct machining operations effectively

Process planning methodology and evaluation of tool life for micromilling with an application to the fabrication of thin wall structure

Ph. D. Thesis.The scaling down effect on feature geometries and tools used in micromilling results in low feature stiffness and excessive tool wear. To achieve the required costs and tolerances, optimisation of the machining processes and their associated parameters are necessary which requires a thorough understanding of machining characteristics. Furthermore, the compensation must be sought for downscaling issues that arise at the process planning stage. Hence, the effect of the characteristics of the cutting tool, workpiece material and machining parameters are investigated in this research through a critical review of the literature followed by a numerical and experimental study of the impact of process variables. The research findings are used in the development of a process planning methodology for micromilling of components with application to high aspect ratio structures, to assist machine operators and to fill the gap between industrial and academic machining knowledge. From the investigation of machining sequences, the study of machining layer strategy considering the sequence of removal of excess material using numerical simulation, strategic planning of machining layers in relation to feature stiffness is required, in particular to the machining of high aspect ratio features. The results from numerical simulation recommend an improved layer strategy for micromilling of thin wall structures, which were then experimentally validated in relation to machining time and geometrical and surface accuracy. The importance of planning tool entry and exit position in relation to feature rigidity was highlighted. The increase in depth of cut shows to improve the tool engagement reducing the thin wall deflection by 168 μm and appearance of the burr along the wall edge indicated by up to 200% drop in burr width. The investigation of tool paths showed the suitability of strategies for machining of circular and linear geometries. Also, the experimental findings emphasise on considering the feature geometry type in the selection of tool paths to achieve a balance between high-performance machining and improved productivity. This study also investigates tool life, associated with flank wear rate, surface roughness, volumetric tool loss and the degradation of the cutting edge radius for micro endmills where a direct correlation between cutting speed and tool wear rate has been found. The new procedure for tool life prediction in conjunction with clear tool rejection criteria for the micro end mill is recommended. Along with standard procedure for the evaluation of tool change intervals to avoid tool failure and consequential defects in parts produced. In addition to the findings in the literature on machine process planning and findings from the study of machining sequence on the thin wall structure and tool life investigation conducted, a new process planning methodology for micromilling has been proposed. The process planning methodology includes four distinct modules i.e. feature recognition, tool selection, machining parameter selection and machining sequence planning. The feature recognition module proposes a new approach to identify key feature faces and their corresponding machining attributes required for tasks in process planning. In the tool selection module, a new methodology for the evaluation of the machinability index and the tool replacement strategy for micro endmills are proposed to guide the operator in the task of tool selection and estimating tool replacement intervals. The machining parameter module provides a systematic approach for the selection spindle speed, feedrate and depth of cut. The machine sequence planning module assists the operator in selecting a suitable tool path and tool layer strategy along with a compensate technique for tool path errors. An artefact with thin wall features has been fabricated using the methodology proposed and the conventional process planning method. The results show the part processed using the proposed methodology achieved better geometrical tolerance, and improved repeatability. It also show a 17% improvement in mean surface roughness, which demonstrates the effectiveness of the proposed methodology

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

DESIGN AND EVALUATION OF AN ELEVATED TEMPERATURE CUTTING FORCE DYNAMOMETER FOR HYBRID MANUFACTURING

Cutting force coefficients, workpiece dynamics, and uncut chip area all change as a function of temperature during machining processes at elevated temperatures. In a traditional milling process, these parameters are nearly constant. The bulk workpiece temperature remains well below the working limit of the material, generally near room temperature. However, workpiece temperatures in hybrid manufacturing, where additive deposition precedes machining, are spatially and temporally variable. A milling force model that does not incorporate temperature effects will generally overestimate the cutting force and workpiece dynamic stiffness and underestimate the chip area. Milling parameters that are stable at room temperature may be unstable at higher temperatures, leading to unexpected chatter and poor part quality. A cutting force dynamometer was designed, constructed, and tested to measure cutting force during milling with the workpiece heated to a constant bulk temperature of up to 500°C. The measured cutting forces were used to determine temperature dependent cutting force coefficients. These coefficients and workpiece dynamics were integrated into a time domain milling simulation to predict cutting force and stability under elevated temperature conditions. The outcome is improved modeling capabilities for hybrid manufacturing processes

Development Of Generative Computer-Aided Process Planning System For Lathe Machining

Computer Aided Process Planning (CAPP) is the bridge between computer-aided design (CAD) and computer-aided manufacturing (CAM). CAPP functions as the recognizer of the geometric input from CAD and analyse it into specific function for manufacturing purpose in CAM. These functions always create irregular data descriptions in current CAD and CAM system supply and demand. This study attempts to solve this problem by recognizing the part model’s features via its geometrical based and produce sub-delta volumes that can later be used to generate manufacturing feature-based data for CAM in a single system via generations of algorithm through open source 3D CAD modeller. To map the generated sub-delta volume and respective machining process, part model complexity (PMC) is introduced. Errors of the overall delta volume (ΔODV) were calculated and verification of the proposed PMC is done. Furthermore, to minimize unit production cost, machining parameters including cutting speed (CS), feed rate (f) and depth of cut (d) were optimized for regular form surfaces by using firefly algorithm (FA). These parameters were then useful for tooling selections and tool-path planning. The results from the automatic feature recognitions show less than 0.02% of error in comparison of algorithm overall delta volume, (ODValg) and the manual calculation ODV, (ODVmanual). To validate the generated tool-path, G-codes generated in media package file (MPF) file format and verified through CNC lathe machine. Indeed, the developed algorithm was able to determine the minimum unit production cost of lathe machining part model. Therefore, a single automatic system that able to transfer CAD data into machining readable data through CAM data had been developed

Ultra-high precision machining of contact lens polymers

Contact lens manufacture requires a high level of accuracy and surface integrity in the range of a few nanometres. Amidst numerous optical manufacturing techniques, single-point diamond turning is widely employed in the making of contact lenses due to its capability of producing optical surfaces of complex shapes and nanometric accuracy. For process optimisation, it is ideal to assess the effects of various conditions and also establish their relationships with the surface finish. Presently, there is little information available on the performance of single point diamond turning when machining contact lens polymers. Therefore, the research work undertaken herewith is aimed at testing known facts in contact lens diamond turning and investigating the performance of ultra-high precision manufacturing of contact lens polymers. Experimental tests were conducted on Roflufocon E, which is a commercially available contact lens polymer and on Precitech Nanoform Ultra-grind 250 precision machining. Tests were performed at varying cutting feeds, speed and depth of cut. Initial experimental tests investigated the influence of process factors affecting surface finish in the UHPM of lenses. The acquired data were statistically analysed using Response Surface Method (RSM) to create a model of the process. Subsequently, a model which uses Runge-Kutta’s fourth order non-linear finite series scheme was developed and adapted to deduce the force occurring at the tool tip. These forces were also statistically analysed and modelled to also predict the effects process factors have on cutting force. Further experimental tests were aimed at establishing the presence of the triboelectric wear phenomena occurring during polymer machining and identifying the most influential process factors. Results indicate that feed rate is a significant factor in the generation of high optical surface quality. In addition, the depth of cut was identified as a significant factor in the generation of low surface roughness in lenses. The influence some of these process factors had was notably linked to triboelectric effects. This tribological effect was generated from the continuous rubbing action of magnetised chips on the cutting tool. This further stresses the presence of high static charging during cutting. Moderately humid cutting conditions presented an adequate means for static charge control and displayed improved surface finishes. In all experimental tests, the feed rate was identified as the most significant factor within the range of cutting parameters employed. Hence, the results validated the fact that feed rate had a high influence in polymer machining. The work also established the relationship on how surface roughness of an optical lens responded to monitoring signals and parameters such as force, feed, speed and depth of cut during machining and it generated models for prediction of surface finishes and appropriate selection of parameters. Furthermore, the study provides a molecular simulation analysis for validating observed conditions occurring at the nanometric scale in polymer machining. This is novel in molecular polymer modelling. The outcome of this research has contributed significantly to the body of knowledge and has provided basic information in the area of precision manufacturing of optical components of high surface integrity such as contact lenses. The application of the research findings presented here cuts across various fields such as medicine, semi-conductors, aerospace, defence, telecom, lasers, instrumentation and life sciences