查看更多>>摘要：High-performance Fe-based amorphous (FA) composite coating was successfully fabricated on 304 stainless steels via employing a novel feedstock of FA powders partially clad with molybdenum (Mo). The microstructure, tribological and corrosion behavior of FA/Mo composite and pure FA coating coating were comparatively investigated. Experimental results show that with 20 vol. % Mo incorporation, FA/Mo composite coating became remarkably denser with porosity of 0.46 +/- 0.12 % and attained a 35 % increase in fracture toughness. As a result, compared to the pure FA coating, superior wear resistance including lower specific wear rate and coefficient of friction (COF) were achieved in the composite coating. In addition, the dominant wear mechanism of coating changed from abrasive wear to oxidative wear. Furthermore, due to its favorable amorphous composite microstructure with less pores and more hydrophobic surface, the FA/Mo coating simultaneously obtained better corrosion resistance. The present findings may provide a valuable strategy to prepare FA protective coating for industrial application.
查看更多>>摘要：Electrically-assisted double-sided incremental forming (EA-DSIF) is a flexible forming method suitable for processing hard-to-form materials and complex-shaped parts. A challenge in EA-DSIF experiments is temperature measurement. Since the localized forming zone is blocked by the tools, it is not possible to measure the actual forming temperature distribution in the forming zone. To address this issue, we propose an artificial neural network (ANN) framework for predicting the forming temperature using measurements of the surrounding temperature and toolpath features. The ANN model was trained using the temperature outputs of finite element models. A simplified EA-DSIF simulation model was developed for computational efficiency needed for synthetic data generation. Model simplifications were justified in multiple cases and validated with experimental data by comparing the temperatures from positions that is visible to an infrared camera. The feasibility of applying the developed ANN model to untrained geometries and in practical applications was demonstrated. The findings generated from this study are crucial for selecting optimum process parameters, estimating the forming force, and predicting microstructure evolution during EA-DSIF.
查看更多>>摘要：Today's manufacturing processes are pushed to their limits to generate products with ever-increasing quality at low costs. A prominent hurdle on this path arises from the multiscale, multiphysics, dynamic, and stochastic nature of many manufacturing systems, which motivated many innovations at the intersection of artificial in-telligence (AI), data analytics, and manufacturing sciences. This study reviews recent advances in Mechanistic-AI, defined as a methodology that combines the raw mathematical power of AI methods with mechanism-driven principles and engineering insights. Mechanistic-AI solutions are systematically analyzed for three aspects of manufacturing processes, i.e., modeling, design, and control, with a focus on approaches that can improve data requirements, generalizability, explainability, and capability to handle challenging and heterogeneous manufacturing data. Additionally, we introduce a corpus of cutting-edge Mechanistic-AI methods that have shown to be very promising in other scientific fields but yet to be applied in manufacturing. Finally, gaps in the knowledge and under-explored research directions are identified, such as lack of incorporating manufacturing constraints into AI methods, lack of uncertainty analysis, and limited reproducibility and established bench-marks. This paper shows the immense potential of the Mechanistic-AI to address new problems in manufacturing systems and is expected to drive further advancements in manufacturing and related fields.
查看更多>>摘要：In this work, process monitoring data, including layerwise imagery, multi-spectral emissions, and laser scan vector data, were collected during laser-based powder bed fusion additive manufacturing and correlated to fatigue performance. All parts were X-ray CT scanned post-build, and internal flaws were identified via an automated defect recognition software. Convolutional neural networks were trained to discriminate flaws from nominal build conditions using in situ data modalities only. Trained classifiers were then tested against a previously unseen data set collected from an independent build, and classification performance and metrics for information content provided by each individual modality were formally established. Correlations were drawn between the detected flaw populations and the corresponding fatigue properties, demonstrating that fatigue critical lack-of-fusion flaws can be detected via machine learning of in situ sensor data. The present results also show that, at least from a classification accuracy perspective, flaw detection via ML on process monitoring data is a viable path forward for real-time flaw detection and automated, interlayer repair strategies. However, strategies for extracting and analyzing sensor data in real-time without incurring excessive increases in build time must first be developed. These developments represent necessary components to draw direct correlations between in situ data modalities, internal part quality, and fatigue performance.
查看更多>>摘要：Wire arc additive manufacturing (WAAM) has received attention because of its high deposition rate, low cost, and high material utilization. However, quality issues are critical in WAAM because it builds upon arc welding technology, which can result in low precision and poor quality of the melted parts. Hence, anomaly detection is essential for identifying abnormal behaviors and process instability during WAAM to reduce the time and cost of post-process treatment. The relevant studies have been conducted on anomaly detection algorithms using machine learning in fused deposition modeling and laser powder bed fusion; however, they have less investigated the implementation for in situ quality monitoring in WAAM. This work presents a real-time anomaly detection method that uses a convolutional neural network (CNN) in WAAM. The proposed method enables creation of CNN-based models that detect abnormalities by learning from the melt pool image data, which are pre-processed to increase learning performance. A prototype system was implemented to classify melt pool images into "normal" and "abnormal" states, with the latter accounting for balling and bead-cut defects. Experiments were conducted using molybdenum, a cost-intensive and hard-to-machine material. Four CNN-based models were created using MobileNetV2, DenseNet169, Resnet50V2, and InceptionResNetV2. Then, their performances were validated in terms of classification accuracy and processing time. The MobileNetV2 model yielded the best performance with 98 % of classification accuracy and 0.033 s/frame of processing time. This model was also compared with an object detection algorithm named "YOLO", which yielded 73.5 % of classification accuracy and 0.067 s/frame of processing time.
查看更多>>摘要：Carbon fiber reinforced plastics (CFRPs) are employed in many industrial applications because of their attractive mechanical and structural properties. However, to date, drilling is still considered as the most pivotal process in manufacturing/assembly of CFRPs components, and the delamination damage can be regarded as the most se-vere machining-induced issue in the drilling process. To solve this problem, a delamination prediction model for CFRPs using Ultrasonic Vibration Assisted Drilling (UVAD) with the abrasive diamond hole saw or core drill is proposed to calculate delamination factor and thickness. The approach of the modeling development starts from simplified for tool and CFRPs workpiece, calculating the total cutting force and the critical thrust force, respectively, and adopting maximum nominal stress criterion to predict delamination. Experimental validation has indicated that the calculated results showed a reasonable agreement with the experimental results on the basis of both delamination factor and thickness, proving the feasibility and the accuracy of the model. This work provides an important reference for selecting and optimizing the drilling parameters to enable delamination-free holes on CFRPs composite.
查看更多>>摘要：Elliptical vibration-assisted machining (EVAM) has been employed in manufacturing industries to improve the machining performance of metal alloys. The mechanics of EVAM is dependent on the critical process parameters, including the horizontal speed ratio (HSR) defined as the ratio between the original cutting speed and the horizontal vibration speed. This paper presents a new mechanics model of EVAM, which reveals that the primary reason for shear angle variation at different HSR values is the strain-hardening property of the workpiece material instead of the friction reversal phenomenon. The model quantitatively determines the effect of the HSR on the shear angle, friction reversal, and instantaneous cutting forces in EVAM. A 2-D vibration assistance stage driven is developed to perform EVAM experiments on aluminum alloy with evident strain-hardening property and Zirconium-based bulk metallic glass that is less sensitive to strain-hardening. The chip morphology is examined at various speed ratios and uncut chip thicknesses to validate the shear angle prediction from the mechanics model. In addition, the model predicts the time instance corresponding to the friction reversal, which is also dependent on the shear angle and HSR in EVAM. Finite element simulations are performed to validate the predicted instantaneous cutting forces and friction reversal from the analytical mechanics model.
查看更多>>摘要：The coupled thermal-mechanical effect in the high-speed machining of hardened steels induces the formation of a surface white layer structure, which adversely affects the mechanical properties and service performance of the machined component. However, knowledge about the white layer formation mechanisms is in severe dearth, particularly for high-speed machining under a cryogenic cooling condition. This paper presents an experimental study of the formation mechanisms for surface white layers in high-speed machining of a hardened steel. Both machining with cryogenic liquid nitrogen (LN2) cooling and dry cutting conditions are considered and the characteristics of the white layers formed under the two cooling conditions are compared to reveal the effect of process parameters. It is shown that the hardness of the white layers is increased while their grain size is decreased under the cryogenic LN2 cooling condition as compared to dry cutting. Under both the cooling conditions, no material phase transformation or recrystallization is noticed alongside the white layer formation, but severe plastic deformation is found to be the dominant reason for white layer formation. Tool wear is noticed to increase the white layer thickness. It is shown that during the cutting process, the work material undergoes two stages of intense plastic deformation. In the first stage, the material slips and approaches the first deformation zone, then gradually bulges and produces significant dislocations, and eventually forms a dense dislocation center around the shear plane. In the second stage, part of the material slides toward the tool flank face. The material dislocation slip induced by friction and material shearing further stretches the surface martensite lath bundles to form dislocation tangles and cellular substructures. The highly stretched martensite lath bundles are finally transformed into white layer structure under the interweaving multi-dislocation movement.
查看更多>>摘要：Hydrogen embrittlement of steel has been widely studied; however, the hydrogen behavior during thermoplastic deformation is still unknown. The hydrogen behavior in thermoplastic deformation may have a significant impact on the properties of materials after processing. In this study, hydrogen embrittlement samples were obtained using electrolytic hydrogen charging, and thermoplastic deformation experiments of hydrogen-charged samples at different temperatures were conducted. Hydrogen slightly increased the flow stress, particularly when the deformation temperature was 1123 K. Microstructure characterization revealed that the samples with a high hydrogen content had a higher dislocation density. Molecular dynamics and density functional theory calculations were performed to understand this mechanism. The difference in Gibbs free energy between systems showed that hydrogen reduced the driving force of defect recovery, making recovery more difficult. This microstructure evolution law explained the experimentally observed increase in the dislocation density due to hydrogen. The increase in dislocation density leads to an increase in the flow stress. This study provides useful information and understanding about the behavior of hydrogen during alloy processing. Improving the alloy processing method may be a powerful way to inhibit hydrogen embrittlement.
查看更多>>摘要：The joining of dissimilar materials is a widely used procedure with applications in many industries. While laser welding of such materials has gained traction interest recent years, this has intrinsic limitations, especially considering harsh requirements including surface roughness and weak bonding strength. This paper proposes a novel method to join Al6082 and SiO2 using a femtosecond laser to substantially reduce the requirements of material surface roughness and improve bonding strength. The proposed method involves drilling, ejection, filling and solidification, which results in forming a mechanical pin structure with some adhesive regions between Al6082 and SiO2, ensuring the two materials lock tightly. Besides, a significant tolerance of approximately 2 mm for focal positions was identified, overcoming the limitation of sensitivity of focus position in laser welding. Furthermore, over 100 MPa shear strength of Al6082-SiO2, which was comparable to the tensile strength of the aluminum alloy, was achieved. The proposed method provides a novel technique to improve the quality of products and facilitate the manufacturing process for joining dissimilar materials.
NSTL
Elsevier