Engineered Surface Properties of Porous Tungsten from Cryogenic Machining

Porous tungsten is used to manufacture dispenser cathodes due to it refractory properties. Surface porosity is critical to functional performance of dispenser cathodes because it allows for an impregnated ceramic compound to migrate to the emitting surface, lowering its work function. Likewise, surface roughness is important because it is necessary to ensure uniform wetting of the molten impregnate during high temperature service. Current industry practice to achieve surface roughness and surface porosity requirements involves the use of a plastic infiltrant during machining. After machining, the infiltrant is baked and the cathode pellet is impregnated. In this context, cryogenic machining is investigated as a substitutionary process for the current plastic infiltration process. Along with significant reductions in cycle time and resource use, surface quality of cryogenically machined un-infiltrated (as-sintered) porous tungsten has been shown to significantly outperform dry machining. The present study is focused on examining the relationship between machining parameters and cooling condition on the as-machined surface integrity of porous tungsten. The effects of cryogenic pre-cooling, rake angle, cutting speed, depth of cut and feed are all taken into consideration with respect to machining-induced surface morphology. Cermet and Polycrystalline diamond (PCD) cutting tools are used to develop high performance cryogenic machining of porous tungsten. Dry and pre-heated machining were investigated as a means to allow for ductile mode machining, yet severe tool-wear and undesirable smearing limited the feasibility of these approaches. By using modified PCD cutting tools, high speed machining of porous tungsten at cutting speeds up to 400 m/min is achieved for the first time. Beyond a critical speed, brittle fracture and built-up edge are eliminated as the result of a brittle to ductile transition. A model of critical chip thickness (hc) effects based on cutting force, temperature and surface roughness data is developed and used to study the deformation mechanisms of porous tungsten under different machining conditions. It is found that when hmax = hc, ductile mode machining of otherwise highly brittle porous tungsten is possible. The value of hc is approximately the same as the average ligament size of the 80% density porous tungsten workpiece

In-Situ Calibrated Modeling of Residual Stresses Induced in Machining under Various Cooling and Lubricating Environments

Although many functional characteristics, such as fatigue life and damage resistance depend on residual stresses, there are currently no industrially viable ‘Digital Process Twin’ models (DPTs) capable of efficiently and quickly predicting machining-induced stresses. By leveraging advances in ultra-high-speed in-situ experimental characterization of machining and finishing processes under plane strain (orthogonal/2D) conditions, we have developed a set of physics-based semi-analytical models to predict residual stress evolution in light of the extreme gradients of stress, strain and temperature, which are unique to these thermo-mechanical processes. Initial validation trials of this novel paradigm were carried out in Ti-6Al4V and AISI 4340 alloy steel. A variety dry, cryogenically cooled and oil lubricated conditions were evaluated to determine the model’s ability to capture the tribological changes induced due to lubrication and cooling. The preliminarily calibrated and validated model exhibited an average correlation of better than 20% between the predicted stresses and experimental data, with calculation times of less than a second. Based on such fast-acting DPTs, the authors envision future capabilities in pro-active surface engineering of advanced structural components (e.g., turbine blades)

An Iterative Size Effect Model of Surface Generation in Finish Machining

In this work, a geometric model for surface generation of finish machining was developed in MATLAB, and subsequently verified by experimental surface roughness data gathered from turning tests in Ti-6Al4V. The present model predicts the behavior of surface roughness at multiple length scales, depending on feed, nose radius, tool edge radius, machine tool error, and material-dependent parameters—in particular, the minimum effective rake angle. Experimental tests were conducted on a commercial lathe with slightly modified conventional tooling to provide relevant results. Additionally, the model-predicted roughness was compared against pedigreed surface roughness data from previous efforts that included materials 51CrV4 and AL 1075. Previously obscure machine tool error effects have been identified and can be modeled within the proposed framework. Preliminary findings of the model’s relevance to subsurface properties have also been presented. The proposed model has been shown to accurately predict roughness values for both long and short surface roughness evaluation lengths, which implies its utility not only as a surface roughness prediction tool, but as a basis for understanding three-dimensional surface generation in ductile-machining materials, and the properties derived therefrom

The Effect of Cutting Edge Geometry, Nose Radius and Feed on Surface Integrity in Finish Turning of Ti-6Al4V

While the respective effects of nose radius, feed and cutting edge geometry on surface integrity have each been studied at depth, coupling between these effects is not yet sufficiently understood. Recent studies have clearly established that cutting edge micro-geometries may not only have positive effects on tool-life, but can also be used to tailor surface integrity characteristics, such as surface roughness and near-surface severe plastic deformation. To further a more fundamental understanding of the effects of cutting edge micro-geometries on surface integrity, experimental turning data was generated for a varied range of cutting tool geometries and feeds. Scanning laser interferometry was used in conjunction with a recently developed profile-analysis algorithm to analyze, characterize, and verify the geometry of complex cutting edge micro-geometries. Near surface nanostructure, and surface roughness of the produced surfaces were characterized and correlated to the varied tool geometries. An interaction between two geometry characteristics, predicted kinematic roughness and hone size, was discovered. Scanning laser interferometry analysis of the surfaces revealed that large hones provided either an increase or decrease in roughness, depending on predicted kinematic roughness

A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes

Computationally Efficient, Multi-Domain Hybrid Modeling of Surface Integrity in Machining and Related Thermomechanical Finishing Processes

In order to enable more widespread implementation of sophisticated process modeling, a novel, rapidly deployable multi-physics hybrid model of surface integrity in finishing operations is proposed. Rather than modeling detailed chip formation mechanics, as is common in numerical models, the proposed models integrates existing analytical and semi-empirical models of the plastic, elastic, thermal and thermodynamic domains. Using this approach, highly complex surface integrity phenomena such as residual stresses, grain size, phase composition, microhardness profile, etc. can be accurately predicted in a manner of seconds. It is envisioned that this highly efficient modeling scheme will drive new innovations in surface engineering

In-situ characterization of tool temperatures using in-tool integrated thermoresistive thin-film sensors

Metal cutting is characterized by high temperatures at the tool-workpiece interface. Although valuable information could be provided by the temperature values, their direct measurement still presents a challenge due to the high contact pressure and the inaccessibility of the process kinematic. In this research work, the current state of thin-film sensors for measuring temperatures on the chip-tool interface has been analyzed with a focus on the measuring phenomena: thermoelectricity and thermoresistivity. Thin-film sensors placed on the cutting tools in or close to the tool-chip contact area are expected to obtain accurate temperature information at the expense of a short lifetime. New insights into thin-film sensors manufacturing, design and calibration are presented, and a new concept of a three-point thermoresistive thin-film sensor is proposed. During orthogonal cutting tests the workpiece deformations were measured through high-speed imaging and the process temperatures were measured with thin-film sensors. In order to validate the temperatures and to obtain the temperature distribution on the cutting edge, Finite Element simulations were carried out. Finally, the potential of using cutting tools with integrated thin-film sensors for in-situ characterization is investigated and a statement for its limitations and potential applications is given

Characterization and Modeling of Surface Roughness and Burr Formation in Slot Milling of Polycarbonate

Thermoplastic materials hold great promise for next-generation engineered and sustainable plastics and composites. However, due to their thermoplastic nature and viscoplastic material response, it is difficult to predict the properties of surfaces generated by machining. This is especially problematic in micro-channel machining, where burr formation and excessive surface roughness lead to poor component-surface integrity. This study attempts to model the influence of size effects, which occur due to the finite sharpness of any cutting tool, on surface finish and burr formation during micro-milling of an important thermoplastic material, polycarbonate. Experimental results show that the depth of cut does not affect either surface finish or burr formation. A proposed new sideflow model shows the dominant effect of cutting-edge radius and feed rate on surface finish, while tool edge roughness, coating and feed rate have the most pronounced influence on burr formation. Overall, a good agreement between the experimental data and the proposed size effect model for the machining of thermoplastic material was found. Based on these results, tool geometry and process parameters may be optimized for improved surface integrity of machined thermoplastic components

In-Situ Calibrated Modeling of Residual Stresses Induced in Machining under Various Cooling and Lubricating Environments

Although many functional characteristics, such as fatigue life and damage resistance depend on residual stresses, there are currently no industrially viable ‘Digital Process Twin’ models (DPTs) capable of efficiently and quickly predicting machining-induced stresses. By leveraging advances in ultra-high-speed in-situ experimental characterization of machining and finishing processes under plane strain (orthogonal/2D) conditions, we have developed a set of physics-based semi-analytical models to predict residual stress evolution in light of the extreme gradients of stress, strain and temperature, which are unique to these thermo-mechanical processes. Initial validation trials of this novel paradigm were carried out in Ti-6Al4V and AISI 4340 alloy steel. A variety dry, cryogenically cooled and oil lubricated conditions were evaluated to determine the model’s ability to capture the tribological changes induced due to lubrication and cooling. The preliminarily calibrated and validated model exhibited an average correlation of better than 20% between the predicted stresses and experimental data, with calculation times of less than a second. Based on such fast-acting DPTs, the authors envision future capabilities in pro-active surface engineering of advanced structural components (e.g., turbine blades)

A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes