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Results for “machining processes”
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In-pixel integration of signal processing and machine learning based data filtering for particle tracking detectors
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An Enhanced Earthquake Catalog for the 2020 Monte Cristo Range Sequence Derived from Machine Learning Processing of a Dense Aftershock Deployment
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The development of machine technology processing for earth resource survey
The following technologies are considered for automatic processing of earth resources data: (1) registration of multispectral and multitemporal images, (2) digital image display systems, (3) data system parameter effects on satellite remote sensing systems, and (4) data compression techniques based on spectral redundancy. The importance of proper spectral band and compression algorithm selections is pointed out.
Stochastic Signal Processing and Machine Learning Approaches for Exoplanet Transit Surveys
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High Throughput Testing for Frequent AM Machine and Process Characterization.
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Low-cost sensors and analytics for a digital factory
ORNL Manufacturing Demonstration Facility and Perisense worked on a low-cost solution for machining process monitoring to enable a digital factory. Enabling machine connectivity for digital factors is expensive for many small to medium manufacturers. The hypothesis for the research project is that a lowcost sensor suite, which can be retrofitted to a legacy machine tool or a new machine tool, can effectively provide real-time, in-situ sensing for manufacturing processes, which can enable improvements in process performance and efficiency. The Perisense sensor node was installed on the Haas TM1 machine with a current sensor and an accelerometer. Cutting tests were performed at different process parameters and sensor data was recorded. Results show a correlation of the current sensor to the spindle load. The accelerometer, mounted on the machine base, showed a correlation to the spindle speed as well as the spindle load. The sensor data can be used to monitor the machining process and provide insights on the machine spindle load utilization.
Technique for Machining Glass
Process for machining glass with conventional carbide tools requires a small quantity of a lubricant for aluminum applied to area of glass to be machined. A carbide tool is then placed against workpiece with light pressure. Tool is raised periodically to clear work of glass dust and particles. Additional lubricant is applied as it is displaced.
Electromagnetic processes in an ideal, induction MHD machine
Electromagnetic processes in conduction band of traveling magnetic field of flat inductor in magnetohydrodynamic machines
AI-enabled Dynamic Finish Machining Optimization for Sustained Surface Integrity
While machining processes are typically leveraged to establish geometric features, many functional characteristics of advanced materials are directly determined by their machining-induced surface integrity (SI). Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters. We present a novel integrated approach based on model-informed artificial intelligence (AI), which optimizes ‘dynamic’ process parameters in real-time. Here, by maximizing the useful life of a cutting tool over which a required set of SI parameters can be maintained, our paradigm will enable significantly more efficient processing of next-generation materials and components.
Machining of Thin-Walled Structures From Stiffness-Driven Additively Manufactured Preform Geometry
Additive manufacturing provides the means to build component preforms with reduced excess material to create functional parts. In the case of aero-structural and aero-engine components, additive manufacturing technologies offer the possibility to substantially reduce the volume of material to be removed by machining operations. To achieve this objective, the preform must be built with the minimum material necessary to contain the final geometry and simultaneously provide enough stiffness to withstand the magnitude of the machining forces. This work describes a computationally efficient method to calculate the geometry required from the preform to reliably manufacture typical thin-walled structures via finish machining processes. This is achieved by defining the preform with near constant static stiffness across the width of the preform, in combination with a prescribed magnitude of stiffness at the top edge of the preform. The prescribed static stiffness is the function of the machining force magnitude, a direct consequence of the preselected cutting conditions. In conclusion, this article illustrates the application of the method to a straight single boundary thin-walled structure as an introduction case and for ease of description.
Methods of sheet metal production and sheet metal products produced thereby
Processes for producing sheet metal products by machining a solid metal body with a cutting tool in a single step to continuously produce a continuous bulk form from material obtained from the solid metal body, and without performing a hot rolling operation thereon, cold rolling the continuous bulk form to produce a sheet metal product. The machining step is a large-strain machining process capable of being directly performed on an as-cast ingot or other solid body to produce a continuous intermediate product that can be directly cold rolled without any intervening hot rolling operation, and optionally without homogenization or annealing.
Materials and processes laboratory composite materials characterization task, part 1. Damage tolerance
A test run was performed on IM6/3501-6 carbon-epoxy in which the material was processed, machined into specimens, and tested for damage tolerance capabilities. Nondestructive test data played a major role in this element of composite characterization. A time chart was produced showing the time the composite material spent within each Branch or Division in order to identify those areas which produce a long turnaround time. Instrumented drop weight testing was performed on the specimens with nondestructive evaluation being performed before and after the impacts. Destructive testing in the form of cross-sectional photomicrography and compression-after-impact testing were used. Results show that the processing and machining steps needed to be performed more rapidly if data on composite material is to be collected within a reasonable timeframe. The results of the damage tolerance testing showed that IM6/3501-6 is a brittle material that is very susceptible to impact damage.
Data Visualization and Analytics for Optimal Process Parameter Selection in Turning
The objective of this project is to research physics-guided machine learning methods to recommend optimal tools and machining process parameters for turning applications using the MSC test database. For a given turning application, the MSC metalworking specialist needs to make decisions on tools and the associated process parameters for the MSC customer. For a given material, there are many alternatives for tools and a wide range of process parameters to consider. MSC has built a database of tools and parameters for different applications from the historical turning tests completed at various customer sites. The research project aims to use machine learning methods to predict optimal tools and process parameters for the MSC metalworking specialists using the MSC test database. This enables continuous learning of optimal tool and process parameters for different applications as new information is collected from testing. Through MSC, this information can be shared with machining shops across the US leading to improved productivity and efficiency.
Superconducting Vacuum-Gap Crossovers for High Performance Microwave Applications
The design and fabrication of low-loss wide-bandwidth superconducting vacuum-gap crossovers for high performance millimeter wave applications are described. In order to reduce ohmic and parasitic losses at millimeter wavelengths a vacuum gap is preferred relative to dielectric spacer. Here, vacuum-gap crossovers were realized by using a sacrificial polymer layer followed by niobium sputter deposition optimized for coating coverage over an underlying niobium signal layer. Both coplanar waveguide and microstrip crossover topologies have been explored in detail. The resulting fabrication process is compatible with a bulk micro-machining process for realizing waveguide coupled detectors, which includes sacrificial wax bonding, and wafer backside deep reactive ion etching for creation of leg isolated silicon membrane structures. Release of the vacuum gap structures along with the wax bonded wafer after DRIE is implemented in the same process step used to complete the detector fabrication.
Leveraging Automated Fiber Placement Computer Aided Process Planning Framework for Defect Validation and Dynamic Layup Strategies
Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. The current state of process planning requires a high degree of interaction from the process planner and could greatly benefit from increased automation. Therefore, a list of key steps and functions are created to identify the more difficult and time-consuming phases of process planning. Additionally, a set of metrics must exist by which to evaluate the effectiveness of the manufactured laminate from the machine code created during the Process Planning stage. Layup strategies, in addition to dog ears, stagger shifts, steering constraints, and starting points, represented the group of functions labeled as process optimization and ranked the highest in terms of priority for automation. The laminates resulting from the selected parameters are evaluated through the occurrences of principal defect metrics such as fiber gaps, overlaps, angle deviation and steering violations. This document presents an automated software solution to the layup strategy and starting point selection phase of process planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.
Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images
Light sheet microscopy has made possible the high temporal and spatial 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual process. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through the application of refinements such as transfer learning.
Analysis of Conduction Cooling Strategies for Wire Arc Additive Manufacturing
Metal additive manufacturing (AM) processing consists of numerous parameters which take time to optimize for various geometries. One aspect of the metal AM process that continues to be explored is the control of thermal energy accumulation during component manufacturing due to the melting and solidification of the feedstock. Excessive energy accumulation causes thermal failure of the component while minimal energy accumulation causes lack of fusion with the build plate or previous layer. The ability to simulate the thermal response of an AM component can increase research efficiency by reducing the time to optimize thermal energy accumulation. This paper presents an effective implementation of finite element analysis to determine the thermal response of a wire arc additive manufactured component with various build plate sizes and cooling methods including, integral build plate cooling, oversized build plates with passive cooling, and non-integral build plate cooling. The use of integral build plate cooling channels was shown to decrease the interpass temperature at the conclusion of the build process by 55% and build plate temperature by 96% compared to the conventionally deposited sample with 20 second dwell time. The use of a tall build plate with passive cooling was shown to reduce the interpass temperature by 32% as compared to the conventionally deposited sample with 20 second dwell time. Each cooling strategy evaluated decreased the interpass temperature within a range of 20–55% which enables higher deposition rates and decreased dwell times during depositions. The cooling strategies are designed to be implemented in a hybrid or retrofit AM platform to mitigate concerns of the thermal input from the additive process having detrimental effects on the precision of the machining process. This paper shows that accurate simulations of all strategies can be used to accurately predict the thermal response of the various strategies discussed. These cooling strategies will allow for increased deposition rates with comparable interpass temperature and decreased dwell time, increasing deposition efficiency. This model and these simulations are verified by experimental results. It is concluded that passive strategies, such as the over-sized tall build plate, can be used when liquid coolant in the AM environment could negatively affect the deposition process. Active cooling strategies, such as the integral build plate cooling could be used if low thermal conductivity materials are deposited or higher material deposition rates are desired. This paper discusses the use of active and passive cooling used during AM and shows how a simulation model can be used to make design choices for cooling strategies. The model also enables verification of select critical process parameters such as dwell times for a desired interpass temperature.