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Machine Learning-Enabled Quantitative Analysis of Optically Obscure Scratches on Nickel-Plated Additively Manufactured (AM) Samples
Additively manufactured metal components often have rough and uneven surfaces, necessitating post-processing and surface polishing. Hardness is a critical characteristic that affects overall component properties, including wear. This study employed K-means unsupervised machine learning to explore the relationship between the relative surface hardness and scratch width of electroless nickel plating on additively manufactured composite components. The Taguchi design of experiment (TDOE) L9 orthogonal array facilitated experimentation with various factors and levels. Initially, a digital light microscope was used for 3D surface mapping and scratch width quantification. However, the microscope struggled with the reflections from the shiny Ni-plating and scatter from small scratches. To overcome this, a scanning electron microscope (SEM) generated grayscale images and 3D height maps of the scratched Ni-plating, thus enabling the precise characterization of scratch widths. Optical identification of the scratch regions and quantification were accomplished using Python code with a K-means machine-learning clustering algorithm. The TDOE yielded distinct Ni-plating hardness levels for the nine samples, while an increased scratch force showed a non-linear impact on scratch widths. The enhanced surface quality resulting from Ni coatings will have significant implications in various industrial applications, and it will play a pivotal role in future metal and alloy surface engineering.
Improved Throughput and Analysis of Scratch Test Results via Automation and Machine Learning
A data analysis automation interface that incorporates machine learning (ML) has been developed to improve productivity, efficiency, and consistency in identifying and defining critical load values (or other values associated with optically identifiable characteristics) of a coating when a scratch test is performed. In this specific program, the machine learning component of the program has been trained to identify the Critical Load 2 (L C2 ) value by analyzing images of the scratch tracks created in each test. An optical examination of the scratch by a human operator is currently used to determine where this value occurs. However, the vagueness of the standard has led to varying interpretations and nonuniform usage by different operators at different laboratories where the test is implemented, resulting in multiple definitions of the desired parameter. Using a standard set of training and validation images to create the dataset, the critical load can be identified consistently amongst different laboratories using the automation interface without requiring the training of human operators. When the model was used in conjunction with an instrument manufacturer's scratch test software, the model produced accurate and repeatable results and defined L C2 values in as little as half of the time compared to a human operator. When combined with a program that automates other aspects of the scratch testing process usually conducted by a human operator, scratch testing and analysis can occur with little to no intervention from a human beyond initial setup and frees them to complete other work in the lab.
Uniaxial tensile mechanical properties from a scratch on ultra-fine grained tungsten
We demonstrate that surface-based mechanical testing can be used to quantify a wide range of mechanical properties determined using traditional uniaxial tension experiments. These include mapping of dynamic stress–strain behaviour, which can be used as a screening tool for uniaxial tensile ductility (uniform elongation), fracture toughness (K C ), which is shown to be in good agreement with reported plane-strain K IC , as well work-hardening exponents (n), activation volumes (V) energies (Q) and strain-rate sensitivity (m) exponents as a function of temperature. Advantages of the surface-based approach are the ability to work with significantly smaller volumes and accommodate processing defects like pores, voids and cracks. These methods are applied to ultra-fine grained tungsten (average grain size of 650 nm), as it is an ideal candidate for alloy development efforts in advanced energy systems. Tungsten is a useful baseline as it exhibits a near-ambient temperature (approx. 200°C) ductile-to-brittle transition temperature (DBTT), enabling validation efforts for the proposed methods, and extraordinary material properties, including the highest melting temperature of any metal and complex temperature and strain-rate dependencies linked to transitions in dislocation mechanisms.
Estimating mode II fracture energy of rocks using the scratch test and phase field modeling
Not provided.
Scratch Reduction in Polystyrene Beads and Capsules via Alternative Flask Material Testing
Not provided.
Improved Throughput and Analysis of Scratch Test Results via Automation and Machine Learning.
Abstract not provided.
Improved Throughput and Analysis of Scratch Test Results via Automation and Machine Learning.
Abstract not provided.
Mathematics Integrated with Computer Science through Scratch: Curricular Modules for the Middle Grades
A project funded by the National Science Foundation, Computer Science Integrated with Mathematics in Middle Schools (CSIMMS) (DRL-1640039), brought middle-school mathematics teachers together with university computer science (CS) faculty and STEM education faculty to design, develop, and test curriculum modules in which CS is integrated into instruction for middle-school general-mathematics courses. The project developed integrated math/CS curriculum modules (for grades 6, 7, and 8), complete with student tasks and teacher materials to guide classroom implementation. Across the project, sixteen teachers from four middle schools representing different school contexts (both urban and suburban, serving students from a range of demographic and socioeconomic backgrounds) participated as members of the design team and trial testers of the math/CS modules. All modules underwent multiple years of testing and refinement, with extensive input from the teachers who co-designed and implemented them. Approximately 50-100 middle-school students participated in the classes of those who taught each year. The modules in this volume feature a range of models for integrating mathematics and computer science at the 6th and 7th grade levels. All modules foreground the teaching of grade-level mathematics content, with computer science functioning to motivate and/or reinforce these ideas.
Lateral cracks during sliding indentation on various optical materials
A series of static and sliding indentation (ie, scratching) was performed and characterized on a wide range of optical workpiece materials [single crystals of Al 2 O 3 (sapphire), SiC, Y 3 Al 5 O 12 (YAG), CaF 2 , and LiB 3 O 5 (LBO); a SiO 2 –Al 2 O 3 –P 2 O 5 –Li 2 O glass ceramic (Zerodur); and glasses of SiO 2 :TiO 2 (ULE), SiO 2 (fused silica), and P 2 O 5 –Al 2 O 3 –K 2 O–BaO (Phosphate)] at various applied loads using various indenters (Vickers, 10 µm conical, and 200 µm conical). Despite having different load dependencies, the lateral crack depth formed during sliding indentation quantitatively scales with that formed during static indentation, explaining why static indentation has been historically effective in describing various grinding parameters. Depending on the indenter geometry, the amount of residual trench damage (plastic deformation and local fracturing) during sliding indentation was often enhanced by more than an order of magnitude compared with static indentation. A simple ploughing scratch model, which considers both tangential and normal stresses (where the tangential stress is amplified by relatively small tangential contact area), explains this enhancement and other observed trends. Accounting for the high correlation between residual trench depth and volumetric fracturing, the model is extended to estimate the amount of fracture damage as a function of the material properties of the workpiece, indenter geometry, and applied load. Such a model has utility in the design of optimized grinding processes, particularly the abrasive geometry. Finally, at higher loads (>1 N), lateral cracks were often observed to preferentially propagate in the forward scratching direction, as opposed to perpendicular to the scratch as typically observed. High-speed imaging of the scratch process confirms that these cracks propagate ahead of the sliding indenter during the scratching event. Finite element stress analysis suggests the ploughing frictional forces increase the mode I tensile stresses at the leading edge of the sliding indenter explaining the direction of crack propagation of such cracks.
Multifunctional application of nanoscratch technique to characterize cementitious materials
This study aims to further explore the multifunctional applicability of nanoscratch technique to cementitious materials for comprehensive characterization. Based on the investigation of deformation, applied force, and fracture toughness of hardened cement paste during scratching, the methods of phase identification, fracture toughness of individual phase, and the interfacial transition zone (ITZ) thickness quantification were proposed and discussed. After redefining the vertical loading rate of scratching, the slope of the square scratching depth vs. scratching length is recognized as a reliable parameter to distinguish the individual phases for the first time. A fracture toughness-based method is proposed to quantify the thickness of ITZ between clinker and hydrates. Meanwhile, the applicability of phase identification by the scratch method is discussed for different transverse scratching speeds and the vertical loading rates. The findings will benefit the investigation and mechanism understanding of cementitious materials at the microscale by utilizing nanoscratch technique.
SURFACE FINISHING AND ELECTROLESS NICKEL PLATING OF ADDITIVELY MANUFACTURED (AM) METAL COMPONENTS
This study investigates the application of electroless nickel deposition on additively manufactured stainless steel samples. Current additive manufacturing (AM) technologies produce metal components with a rough surface. Rough surfaces generally exhibit fatigue characteristics, increasing the probability of initiating a crack or fracture to the printed part. For this reason, the direct use of as-produced parts in a finished product cannot be actualized, which presents a challenge. Post-processing of the AM parts is therefore required to smoothen the surface. This study analyzes chempolish (CP) and electropolish (EP) surface finishing techniques for post-processing AM stainless steel components CP has a great advantage in creating uniform, smooth surfaces regardless of size or part geometry EP creates an extremely smooth surface, which reduces the surface roughness to the sub-micrometer level. In this study, we also investigate nickel deposition on EP, CP, and as-built AM components using electroless nickel solutions. Electroless nickel plating is a method of alloy treatment designed to increase manufactured component's hardness and surface resistance to the unrelenting environment. The electroless nickel plating process is more straightforward than its counterpart electroplating.. We use low-phosphorus (2-5% P), medium-phosphorus (6-9% P), and high-phosphorus (10-13% P). These Ni deposition experiments were optimized using the L9 Taguchi design of experiments (TDOE), which compromises the prosperous content in the solution, surface finish, plane of the geometry, and bath temperature. The pre-and post-processed surface of the AM parts was characterized by KEYENCE Digital MicroscopeVHX-7000 and Phenom XL Desktop SEM. The experimental results show that electroless nickel deposition produces uniform Ni coating on the additively manufactured components up to 20 μm per hour. Mechanical properties of as-built and Ni coated AM samples were analyzed by applying a standard 10 N scratch test. Nickel coated AM samples were up to two times scratch resistant compared to the as-built samples. This study suggests electroless nickel plating is a robust viable option for surface hardening and finishing AM components for various applications and operating conditions.
Investigation and Mitigation of a Prominent Off-Axis Stray Light Path in Rubin Observatory Commissioning
The "scratched tape" stray light feature is the most prominent and prevalent stray light artifact identified during the commissioning of the Vera C. Rubin Observatory. The scratched tape feature originates when light from large off-axis angles (~20 deg) passes between the mid-level and center-section light baffles, reflects off the primary mirror, and illuminates the LSST Camera focal plane. This scenario represented an unobstructed stray light path to the sky during Rubin commissioning due to delays in the integration of the dome slit light-wind screen. This document describes the identification, modeling, characterization, and mitigation of the scratched tape stray light artifact.
Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective
Accurate and efficient interatomic potentials are essential for molecular dynamics (MD) simulations of radiation damage, gas diffusion, and phase stability in complex ceramics such as LiAlO 2 , especially under extreme conditions relevant to tritium production. Here, we evaluate the performance of six machine-learned interatomic potentials (MLIPs), moment tensor potential (MTP), Gaussian approximation potential, deep potential (DeePMD), atomic cluster expansion (ACE), message-passing ACE (multilayer atomic cluster expansion (MACE) pretrained) and MACE (trained from-scratch), all trained on the same density functional theory dataset with inclusion of tritium. The MLIPs are benchmarked against traditional Buckingham and ReaxFF potentials in terms of energy accuracy, density predictions, thermal equilibration behavior, threshold displacement energy (E d ), tritium diffusivity, and computational cost. Among the models, MTP shows the best overall balance between efficiency and accuracy, with low force and energy errors and realistic E d values for Li and Al. The ACE and MACE (pretrained and trained from scratch) models exhibit high E d (>200 eV) and unphysical pair interactions. DeePMD underestimates Ed due to overly repulsive behavior even at equilibrium distances. All models over-estimate tritium diffusion but the pretrained MACE model behaves well during tritium-diffusion simulations up to 500 K, maintaining diffusivities in the physically consistent 10 −11 m 2 /s range. Finally, we quantify the computational cost of each potential in large-scale atomic/molecular massively parallel simulator, finding that only MTP is more efficient than traditional empirical potentials, while others are significantly more expensive. These findings explain the trade-offs between accuracy and computational cost in MLIP development and provide essential guidance for use in high-throughput radiation damage and gas diffusion simulations in nuclear ceramics.
A non‐intrusive domain‐decomposition model reduction method for linear steady‐state partial differential equations with random coefficients
Abstract Domain decomposition methods have been proved to be an effective strategy to reduce the dimension of parametric partial differential equations (PDEs). However, existing domain decomposition methods for parametric PDEs are usually intrusive, which means domain decomposition based solvers need to be implemented from scratch for each target parametric PDE. To address this issue, we develop a new non‐intrusive domain‐decomposition model reduction method for linear steady‐state PDEs with random‐field coefficients. As a variant of our previous work by Mu and Zhang, the new method only needs access to the final linear system, that is, the global stiffness matrix and the right hand side, of a deterministic PDE solver, in order to build a domain‐decomposition‐based reduced model without intrusive implementation from scratch. The key idea is to remove the interface condition between sub‐domains and rely on the correlation between columns of the linear system to couple the sub‐domains. The non‐intrusive feature enables the applicability of the proposed method to a broader class of uncertainty quantification problems, where many legacy codes/solvers can be fully reused by our method. Two numerical examples including diffusion equations with random diffusivity and convection‐dominated transport with random velocity, are provided to demonstrate the effectiveness and efficiency of our method.
Healable Coatings as a Mechanism to Repair Leading Edge Erosion in Wind Energy
Wind turbine blades are highly engineered structures designed to face temperature extremes and high winds. However, erosion of the blade's leading edge and subsequent repair remains a significant and costly challenge for the wind energy industry. Repair of these leading edges can lead to large amounts of downtime for the turbine and significant operational inefficiencies. In this work, the strength of adhesion and healing ability of a commercially available vitrimer (Mallinda's VITRIMAX) was compared to that of a thermoplastic resin, which has previously been demonstrated in wind energy applications (Arkema's Elium) to evaluate their efficacy as surface coatings for wind turbine blades, particularly their leading edges. Vitrimers are a class of inherently reprocessable thermosets, and it was theorized that vitrimer-based leading edge coatings could enable more robust and efficient wind turbine blades with decreased operational downtime and safer maintenance practices. It was found that the VITRIMAX adhered better to the wind blades' surfaces than both the manufacturer's paint and Elium, with increases in pull-off strength of adhesion ranging from 24% to 83% above that of the original paint. Furthermore, the VITRIMAX adhered strongly to the underlying composite of each blade with strength of adhesion values increasing in ranges from 42% to 97% above that of the original paint. Finally, the vitrimer coating showed an 88% decrease in surface roughness compared to end-of-life blade materials, and initial healing demonstrations in which coatings were manually scratched and subsequently healed exhibited an ~84.5% decrease in scratch depths.
A comparative study of mechanical and chemical durability of non-wetting superhydrophobic and lubricant-infused surfaces
Fabrication of bioinspired non-wetting superhydrophobic surfaces (SHS) and lubricant-infused surfaces (LIS) has been studied extensively on a variety of materials. In contrast, durability of the surfaces exposed to harsh mechanical and chemical environments has been the subject of little attention. This study considers the mechanical and chemical durability of SHS and LIS copper surfaces fabricated via facile electrodeposition and chemical etching methods. The as-fabricated surfaces demonstrate excellent non-wetting characteristics with water contact angle of 160° and sliding angle below 5°. The surfaces are subject to mechanical wear through scratch test and water jet impingement at 15 psi and 20 psi as well as accelerated corrosion following the ASTM-E407 standard. The performance of the electrodeposited and etched non-wetting surfaces is systematically assessed in terms of contact and sliding angles and corrosion rate in a simulated marine environment. All surfaces are shown to be robust to mechanical wear after scratch test, with excellent stability of contact and sliding angles, and up to two orders of magnitude reduced corrosion rate compared to bare copper surface. SHS retained steadfast non-wetting characteristics under high-pressure water jet tests compared to the other surfaces while LIS, regardless of texturing method, showed one to two orders of magnitude reduced corrosion rate compared to bare copper surface throughout water jet impingement and chemical durability tests. The study presents for the first time a systematic comparison of durability of SHS and LIS through a common set of fabrication and testing protocol and helps identify appropriate non-wetting surfaces and fabrication methods based on the use environment.
Efficient distributed continual learning for steering experiments in real-time
Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs to achieve high accuracy, short runtime, and scalability. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input minibatches using unbiased, global sampling. We further propose a generalization of rehearsal buffers to support both classification and generative learning tasks, as well as more advanced rehearsal strategies (notably Dark Experience Replay, leveraging knowledge distillation). We illustrate this approach with a real-life HPC streaming application from the domain of ptychographic image reconstruction. Furthermore, we run extensive experiments on up to 128 GPUs of the ThetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 validation accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.