Engineering & Materials

98 verified cases, highest-rated first.

Industries: materials science 9, additive manufacturing 9, structural health monitoring 6, materials discovery 3, energy materials 3, civil infrastructure 3, infrastructure inspection 3, rail transport 3, aerospace engineering 3, structural engineering 3, battery materials 2, grid forecasting 2

• Scaling deep learning for materials discovery

  Discovery of inorganic crystals for applications from microchips to batteries and photovoltaics has been bottlenecked by expensive trial-and-error approaches.

  Engineering & Materials · 2023 · partially solved · 8/10 importance

• Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review

  Detecting, localizing, and predicting damage in aerospace structures from complex sensor data is critical for safety.

  Engineering & Materials · 2025 · partially solved · 7/10 importance

• Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

  Discovering energy materials is slow because quantum-accurate simulations (DFT) are computationally expensive and chemical space is vast.

  Engineering & Materials · 2025 · ai assisted · 7/10 importance

• Multi‐Agent‐Network‐Based Idea Generator for Zinc‐Ion Battery Electrolyte Discovery: A Case Study on Zinc Tetrafluoroborate Hydrate‐Based Deep Eutectic Electrolytes

  Discovering new aqueous deep eutectic electrolytes for zinc-ion batteries is time-consuming and resource-intensive.

  Engineering & Materials · 2025 · solved · 7/10 importance

• Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect

  Traditional trial-and-error methods are inadequate for predicting battery lifetime and guiding design of next-generation battery systems.

  Engineering & Materials · 2025 · ai assisted · 7/10 importance

• Predicting dynamic stability from static features in power grid models using machine learning

  Predicting the risk of power grid desynchronization after transmission line failures, a major threat to grid stability.

  Engineering & Materials · 2024 · partially solved · 7/10 importance

• Application of Machine Learning in Material Synthesis and Property Prediction

  Traditional experimental and simulation-based discovery of new materials is slow and resource-intensive, motivating faster computational approaches.

  Engineering & Materials · 2023 · ai assisted · 7/10 importance

• Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries

  Discovering ionic liquids for ionic polymer electrolytes to enable safe, high-energy-density lithium metal batteries.

  Engineering & Materials · 2023 · solved · 7/10 importance

• Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology

  Assessing the dynamic (transient) stability of modern power grids, whose structure and parameters change due to renewables and low inertia, in order to support emergency control.

  Engineering & Materials · 2023 · ai assisted · 7/10 importance

• Power-grid stability predictions using transferable machine learning

  Assessing the dynamic stability (frequency synchronization) of power grids via numerical simulation is computationally too expensive. The study asks whether machine learning can estimate node synchronization stability more cheaply and transferably.

  Engineering & Materials · 2021 · ai assisted · 7/10 importance

• A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction

  Traditional computational crystal structure and property prediction is costly, poorly scalable and struggles with complex energy surfaces.

  Engineering & Materials · 2025 · ai assisted · 6/10 importance

• Deep Learning for Concrete Crack Detection and Measurement

  Detecting and measuring cracks in concrete structures automatically, in practical millimetre units rather than pixels, where traditional image processing struggles.

  Engineering & Materials · 2024 · solved · 6/10 importance

• Deep learning-based image segmentation for defect detection in additive manufacturing: an overview

  Additive manufacturing parts can contain defects such as porosity, melt-pool anomalies and spattering, blocking reliable use in medical, aerospace and automotive applications.

  Engineering & Materials · 2024 · ai assisted · 6/10 importance

• Optimal tuning of three deep learning methods with signal processing and anomaly detection for multi-class damage detection of a large-scale bridge

  Detecting multi-class structural damage in a large-scale bridge from noisy, nonstationary vibration sensor data is difficult for deep learning methods.

  Engineering & Materials · 2024 · ai assisted · 6/10 importance

• A multi‐agent deep reinforcement learning approach for traffic signal coordination

  Coordinating traffic signals across multiple intersections to reduce congestion and waiting times.

  Engineering & Materials · 2024 · ai assisted · 6/10 importance

• Interpretable machine learning for materials discovery: Predicting CO2 adsorption properties of metal–organic frameworks

  Screening millions of hypothetical metal–organic frameworks to find ones with high CO2 adsorption capacity is computationally expensive.

  Engineering & Materials · 2024 · ai assisted · 6/10 importance

• Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders

  Discovering shape memory polymers with high glass transition temperatures for high-temperature uses such as geothermal energy, oil and gas, and aerospace.

  Engineering & Materials · 2024 · ai assisted · 6/10 importance

• An autonomous laboratory for the accelerated synthesis of inorganic materials

  Synthesizing novel inorganic materials is slow and labor-intensive; the work aimed to autonomously accelerate solid-state synthesis of targeted compounds.

  Engineering & Materials · 2023 · solved · 6/10 importance

• Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

  Manual visual inspection of bridges for cracks is costly, time-consuming and error-prone, and no automated reliable system exists, especially in developing countries.

  Engineering & Materials · 2023 · ai assisted · 6/10 importance

• Unsupervised deep learning framework for ultrasonic-based distributed damage detection in concrete: integration of a deep auto-encoder and Isolation Forest for anomaly detection

  Detecting hidden damage inside concrete structures using ultrasonic signals, without needing labeled data from damaged specimens.

  Engineering & Materials · 2023 · ai assisted · 6/10 importance

• Application of machine learning for advanced material prediction and design

  Traditional experimental and computational approaches in materials science consume enormous time and resources and may not yield satisfactory results.

  Engineering & Materials · 2022 · ai assisted · 6/10 importance

• End-to-end deep learning framework for printed circuit board manufacturing defect classification

  Quickly and accurately detecting and classifying the types of manufacturing defects present on printed circuit boards.

  Engineering & Materials · 2022 · solved · 6/10 importance

• Crystal twins: self-supervised learning for crystalline material property prediction

  Accurate machine-learning prediction of crystalline material properties is limited by the scarcity of large labeled datasets, which are computationally expensive to generate.

  Engineering & Materials · 2022 · ai assisted · 6/10 importance

• Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events

  Automatically detecting and categorizing structural damage in infrastructure from 2D images after extreme events such as earthquakes.

  Engineering & Materials · 2022 · ai assisted · 6/10 importance

• Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation

  Estimating unknown power grid parameters such as inertia and damping coefficients from observed system dynamics.

  Engineering & Materials · 2022 · ai assisted · 6/10 importance

• Machine learning for perovskite materials design and discovery

  Discovering and rationally designing new perovskite materials is slow; the paper reviews how machine learning can assist that discovery process.

  Engineering & Materials · 2021 · ai assisted · 6/10 importance

• Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning

  Identifying ABO3-type perovskite oxides with high conductivity for energy applications is slow with traditional methods.

  Engineering & Materials · 2021 · ai assisted · 6/10 importance

• Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage

  Detecting structural damage normally requires labeled data from many damage scenarios, which is time-consuming, costly, and often impractical for infrastructure in service.

  Engineering & Materials · 2020 · ai assisted · 6/10 importance

• A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring

  Detecting railway wheel defects online and quantitatively using only track-side strain-monitoring data, without a physical model.

  Engineering & Materials · 2020 · ai assisted · 6/10 importance

• Machine learning enabled discovery of application dependent design principles for two-dimensional materials

  Large-scale searches for high-performing two-dimensional materials were limited by the high computational cost of first-principles density functional theory calculations.

  Engineering & Materials · 2020 · ai assisted · 6/10 importance

• Damage detection in a novel deep-learning framework: a robust method for feature extraction

  Extracting damage features precisely from mode shapes while overcoming measurement noise and incomplete data is an unsolved need in structural health monitoring.

  Engineering & Materials · 2019 · partially solved · 6/10 importance

• Defect Detection and Monitoring in Metal Additive Manufactured Parts through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals

  Laser powder bed fusion 3D-printed metal parts contain defects unacceptable for safety-critical applications, requiring in-process monitoring.

  Engineering & Materials · 2018 · ai assisted · 6/10 importance

• Manifold Learning for Aerodynamic Shape Design Optimization

  The high computational cost of iterative CFD-based aerodynamic shape design under modern complex design requirements.

  Engineering & Materials · 2025 · ai assisted · 5/10 importance

• Machine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid

  Accurate prediction of solar power plant parameters is needed to minimise losses when traditional grids convert to smart grids.

  Engineering & Materials · 2024 · partially solved · 5/10 importance

• A novel procedure for cable damage identification of cable-stayed bridge using particle swarm optimization and machine learning

  Cables of cable-stayed bridges can become damaged, and identifying and quantifying that damage early is needed to prevent disasters.

  Engineering & Materials · 2024 · ai assisted · 5/10 importance

• Machine Learning Guided Discovery of Non‐Linear Optical Materials

  Discovering new nonlinear optical (NLO) materials for solid-state lasers has relied on slow high-throughput calculations or chemical intuition.

  Engineering & Materials · 2024 · ai assisted · 5/10 importance

• Phase Transformation Temperature Prediction in Steels via Machine Learning

  Predicting the four key steel phase transformation temperatures (Ac1, Ac3, MS, BS) and understanding which alloying elements and atomic parameters influence them, which matters for steel design and heat treatment.

  Engineering & Materials · 2024 · ai assisted · 5/10 importance

• Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing

  Laser-based metal additive manufacturing (PBF-LB/M, DED-LB/M) frequently forms defects and existing machines lack closed-loop control, making processes hard to control.

  Engineering & Materials · 2023 · ai assisted · 5/10 importance

• Wheel Defect Detection Using a Hybrid Deep Learning Approach

  Detecting railway wheel defects early despite interfering vibrations that cause false alarms during operation.

  Engineering & Materials · 2023 · ai assisted · 5/10 importance

• Machine learning based charge mobility prediction for organic semiconductors

  Quantum-chemical calculation of transfer integrals for all molecular pairs in organic semiconductors is computationally unaffordable, blocking charge mobility prediction.

  Engineering & Materials · 2023 · solved · 5/10 importance

• Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method

  Transonic buffet, large self-excited shock oscillations on wings, is a flow instability that makes optimal airfoil design difficult.

  Engineering & Materials · 2023 · solved · 5/10 importance

• Additive manufacturing: Application and validation of machine learning-based process-structure-property linkages in Ti-6Al-4V

  Predicting how additive manufacturing process conditions and heat treatments affect the microstructure, properties and machining behavior of Ti-6Al-4V.

  Engineering & Materials · 2023 · partially solved · 5/10 importance

• Machine learning of material properties: Predictive and interpretable multilinear models

  Machine learning models predict material properties accurately but lack transparency, limiting scientific insight.

  Engineering & Materials · 2022 · ai assisted · 5/10 importance

• Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure

  RGB-image-based automated damage detection in civil infrastructure degrades in poor and low-light conditions.

  Engineering & Materials · 2022 · ai assisted · 5/10 importance

• Identification of microstructures critically affecting material properties using machine learning framework based on metallurgists’ thinking process

  Identifying which microstructures critically affect a material's physical properties normally requires human expert prior knowledge.

  Engineering & Materials · 2022 · ai assisted · 5/10 importance

• Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

  Existing methods poorly handle diverse industrial laser weld image data, so robust automatic defect detection and recognition is needed.

  Engineering & Materials · 2021 · ai assisted · 5/10 importance

• Novel machine learning framework for thermal conductivity prediction by crystal graph convolution embedded ensemble

  Predicting the lattice thermal conductivity of crystal materials is hard because usable datasets are scarce, limiting data-driven materials science.

  Engineering & Materials · 2021 · ai assisted · 5/10 importance

• A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

  Rail tracks (especially continuous welded rails) can buckle in hot weather and cause derailments, so operators need early warning of dangerous rail temperatures.

  Engineering & Materials · 2021 · ai assisted · 5/10 importance

• Machine learning paradigm for structural health monitoring

  Conventional vibration-based methods struggle to detect damage in actual structures due to incomplete monitoring data, intense uncertainties, and coupled environmental effects.

  Engineering & Materials · 2020 · ai assisted · 5/10 importance

• A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

  Defects from suboptimal parameter settings in laser-powder bed fusion 3D printing are usually detected only after the process, not during printing.

  Engineering & Materials · 2020 · ai assisted · 5/10 importance

• Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

  The lack of a full picture of the hydrogen evolution reaction impeded development of potential electrocatalysts, motivating systematic screening of graphdiyne-based atomic catalysts.

  Engineering & Materials · 2020 · ai assisted · 5/10 importance

• Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection

  Deteriorated concrete surfaces cause hazardous incidents, so automated crack inspection is needed instead of manual checks.

  Engineering & Materials · 2020 · ai assisted · 5/10 importance

• A hybrid learning strategy for structural damage detection

  Most structural damage detection techniques fail to automatically identify novelties such as damage or abnormal behavior from dynamic measurements, making human decisions necessary.

  Engineering & Materials · 2020 · ai assisted · 5/10 importance

• Genetic algorithms for computational materials discovery accelerated by machine learning

  Finding stable nanoparticle alloy compositions via computational search is slow because each candidate requires expensive energy calculations.

  Engineering & Materials · 2019 · ai assisted · 5/10 importance

• A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function

  Detecting structural damage with high certainty from sensor measurements, to extend the life of engineered structures.

  Engineering & Materials · 2016 · ai assisted · 5/10 importance

• Dynamic microarchitectural adaptation using machine learning

  How to dynamically determine the best microprocessor configuration at runtime for any program or program phase, to improve energy and performance efficiency.

  Engineering & Materials · 2013 · solved · 5/10 importance

• Machine Learning-Assisted Polymer and Polymer Composite Design for Additive Manufacturing

  Predicting the final properties of 3D-printed polymer and polymer composite parts is difficult because material composition, processing conditions and microstructure vary.

  Engineering & Materials · 2026 · ai assisted · 4/10 importance

• Integrating machine learning and symbolic regression for predicting damage initiation in hybrid FRP bolted connections

  Hybrid FRP bolted connections are rapidly adopted in industry but lack interpretable predictive models for damage initiation loads.

  Engineering & Materials · 2025 · ai assisted · 4/10 importance

• Intelligent structural design of composite concrete‐encased steel columns based on hybrid machine learning and multiobjective optimization

  Predicting axial load capacity of composite concrete-encased steel columns is inaccurate, with traditional methods showing 10%-30% relative errors.

  Engineering & Materials · 2025 · ai assisted · 4/10 importance

• Machine Learning-Based Prediction of EDM Material Removal Rate and Surface Roughness

  Predicting material removal rate and surface roughness in electrical discharge machining (EDM) is difficult because of complex physical events in the electrode gaps.

  Engineering & Materials · 2025 · ai assisted · 4/10 importance

• Accelerating process control and optimization via machine learning: a review

  Identifying and tuning the best solution algorithm for chemical process control and optimization is challenging and time-consuming.

  Engineering & Materials · 2025 · ai assisted · 4/10 importance

• Solar Panel Surface Defect and Dust Detection: Deep Learning Approach

  Detecting defects, dust, physical damage, and snow on photovoltaic solar panels to maintain efficiency.

  Engineering & Materials · 2025 · ai assisted · 4/10 importance

• Interpretable Machine Learning for High-Accuracy Reservoir Temperature Prediction in Geothermal Energy Systems

  Accurate prediction of reservoir temperature in geothermal energy systems is critical for optimization, but complex geothermal data challenges traditional modeling.

  Engineering & Materials · 2025 · partially solved · 4/10 importance

• Advanced UAV Design Optimization Through Deep Learning-Based Surrogate Models

  Conceptual UAV design requires simultaneous optimization of aerodynamics, structure, stealth and propulsion, which is computationally expensive with traditional methods.

  Engineering & Materials · 2024 · ai assisted · 4/10 importance

• Modeling Xanthan Gum Foam’s Material Properties Using Machine Learning Methods

  Xanthan gum had never been studied as a foam material for insulation and packaging, nor modeled with machine learning.

  Engineering & Materials · 2024 · ai assisted · 4/10 importance

• A Survey of Incremental Deep Learning for Defect Detection in Manufacturing

  Deep learning defect detection models in manufacturing need to be rigorously updated in real time with new tasks, classes or decision pathways without degrading, e.g. via catastrophic forgetting.

  Engineering & Materials · 2024 · ai assisted · 4/10 importance

• Stress-constrained optimization of multiscale structures with parameterized microarchitectures using machine learning

  Designing multiscale structures under stress constraints is difficult because evaluating microstructure material properties and local stress states is computationally costly.

  Engineering & Materials · 2024 · ai assisted · 4/10 importance

• Resolution-enhanced X-ray fluorescence microscopy via deep residual networks

  X-ray fluorescence microscopy resolution is limited by the X-ray probe size, unlike diffraction-limited ptychography.

  Engineering & Materials · 2023 · solved · 4/10 importance

• Building Surface Crack Detection Using Deep Learning Technology

  Cracks in building facades worsen if not found quickly, and manual inspection of large buildings is impractical, motivating automated crack detection.

  Engineering & Materials · 2023 · ai assisted · 4/10 importance

• Research on tire crack detection using image deep learning method

  Tire defects such as cracks and debris are hard to detect manually, creating safety risks and labor costs for drivers and manufacturers.

  Engineering & Materials · 2023 · solved · 4/10 importance

• End-to-end material thermal conductivity prediction through machine learning

  Predicting the thermal conductivity of materials is slow and limited by scarce high-quality data.

  Engineering & Materials · 2023 · partially solved · 4/10 importance

• Machine learning-based soft-sensor development for road quality classification

  Road defects cause health, safety, and vehicle-damage costs, so a low-cost onboard system was built to detect and classify road quality.

  Engineering & Materials · 2023 · ai assisted · 4/10 importance

• Machine learning-based damage sensing and self-healing of carbon fiber/nylon composites via addressable conducting networks

  Detecting structural damage in carbon fiber reinforced nylon composites and healing the detected damage.

  Engineering & Materials · 2023 · partially solved · 4/10 importance

• Virtual sample generation in machine learning assisted materials design and discovery

  Machine learning-assisted materials design and discovery is limited by small datasets; virtual sample generation techniques are used to expand training data and guide discovery.

  Engineering & Materials · 2023 · ai assisted · 4/10 importance

• Fast Inverse Design of Transonic Airfoils by Combining Deep Learning and Efficient Global Optimization

  Inverse design of transonic airfoils—finding a shape that produces a prescribed pressure distribution—is computationally slow.

  Engineering & Materials · 2023 · solved · 4/10 importance

• Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Techniques

  Measuring liquid steel temperature in the ladle is challenging, expensive, and unsafe in industrial settings.

  Engineering & Materials · 2023 · ai assisted · 4/10 importance

• Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids

  Designing synthetic copolymers that stabilize proteins is difficult because the chemical and composition space is too vast for rational design.

  Engineering & Materials · 2022 · partially solved · 4/10 importance

• Single channel blind source separation of complex signals based on spatial‐temporal fusion deep learning

  Separating mixed radar, communication and jamming signals from a single channel in integrated electronic systems, where traditional methods have low accuracy and poor robustness.

  Engineering & Materials · 2022 · solved · 4/10 importance

• Additive manufacturing: A machine learning model of process-structure-property linkages for machining behavior of Ti-6Al-4V

  A knowledge gap exists in how additive manufacturing processes and heat treatments link to microstructure and machining behavior of Ti-6Al-4V parts.

  Engineering & Materials · 2022 · ai assisted · 4/10 importance

• Deep Learning-Based PC Member Crack Detection and Quality Inspection Support Technology for the Precise Construction of OSC Projects

  Quality inspection of precast concrete (PC) members in off-site construction is inaccurate, slow and costly due to shortage of inspection manpower.

  Engineering & Materials · 2022 · ai assisted · 4/10 importance

• Machine learning for the discovery of molecular recognition based on single-walled carbon nanotube corona-phases

  No generalized scheme exists to predict de novo the molecular recognition of DNA-SWCNT corona phases, due to their sequence-dependent three-dimensional complexity.

  Engineering & Materials · 2022 · partially solved · 4/10 importance

• Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings

  Automatic extraction of indoor spatial information from floor plans was mostly demonstrated only on small, simple, low-resolution buildings.

  Engineering & Materials · 2021 · ai assisted · 4/10 importance

• A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding

  Finding optimal machine settings for the plasticizing process in injection molding normally requires time-consuming, expensive physical trials.

  Engineering & Materials · 2021 · partially solved · 4/10 importance

• Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques

  The factors influencing VGF-GaAs crystal growth, such as achieving a favorable interface shape, are hard to derive and optimize.

  Engineering & Materials · 2021 · ai assisted · 4/10 importance

• Green Building Energy Cost Optimization With Deep Belief Network and Firefly Algorithm

  Commercial building HVAC systems consume high energy costs while needing to maintain acceptable indoor air quality.

  Engineering & Materials · 2021 · ai assisted · 4/10 importance

• In-situ identification of material batches using machine learning for machining operations

  Batches of the same specified material differ in machinability, and unidentified differences reduce product quality and raise manufacturing costs.

  Engineering & Materials · 2020 · partially solved · 4/10 importance

• Laser-Induced Breakdown Spectroscopy Assisted by Machine Learning for Plastics/Polymers Identification

  Discriminating plastic samples that share the same polymeric matrix but contain different additives is difficult.

  Engineering & Materials · 2019 · solved · 4/10 importance

• Optimization of Silicone 3D Printing with Hierarchical Machine Learning

  3D printing silicone via freeform reversible embedding requires optimizing many correlated material, formulation, and processing variables simultaneously.

  Engineering & Materials · 2019 · partially solved · 4/10 importance

• Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet

  Feedback control of thermal effects of atmospheric pressure plasma jets on complex substrates, which is difficult due to complex plasma-substrate interactions.

  Engineering & Materials · 2019 · partially solved · 4/10 importance

• Deep machine learning for detection of acoustic wave reflections

  Acoustic emission damage-detection algorithms can be deceived by reflected waves modulated by structural boundaries, so these reflections must be identified before damage can be located reliably.

  Engineering & Materials · 2019 · partially solved · 4/10 importance

• Machine learning for combinatorial optimization of brace placement of steel frames

  Optimally placing braces in steel frames during seismic retrofit is a combinatorial optimization problem whose many structural analyses make it computationally very costly.

  Engineering & Materials · 2018 · ai assisted · 4/10 importance

• Benchmarking deep learning models for surface defect detection: a reproducible and statistically-rigorous approach

  Comparing deep learning defect-detection models is unreliable because varying dataset partitions and stochastic training introduce variable outcomes.

  Engineering & Materials · 2025 · ai assisted · 3/10 importance

• Data imputation strategies for process optimization of laser powder bed fusion of Ti6Al4V using machine learning

  Additive manufacturing datasets for SLM Ti6Al4V are incomplete because individual studies cover only some parameters, hindering process-property linkage.

  Engineering & Materials · 2023 · ai assisted · 3/10 importance

• Deep Learning-Based Defect Detection for Sustainable Smart Manufacturing

  Product defects arise at several stages of manufacturing, and small-scale producers rely mainly on manual visual inspection to catch them.

  Engineering & Materials · 2022 · partially solved · 3/10 importance

• Application of Machine Learning to Bending Processes and Material Identification

  Manufacturers need to identify material constitutive parameters from bending tests and predict punch displacement to achieve a desired bending angle despite springback in press-brake air bending.

  Engineering & Materials · 2021 · ai assisted · 3/10 importance

• Material Identification Using a Microwave Sensor Array and Machine Learning

  Identifying materials via microwave sensing, where single-resonator systems limit characterization to narrow frequency ranges and reduce identification accuracy.

  Engineering & Materials · 2020 · partially solved · 3/10 importance

• Study on growth rate of TiO ₂ nanostructured thin films: simulation by molecular dynamics approach and modeling by artificial neural network

  Predicting the thickness of TiO2 nanostructured thin films based on sol-gel dip-coating process parameters.

  Engineering & Materials · 2013 · ai assisted · 3/10 importance

• Advancing Crack Detection Using Deep Learning Solutions for Automated Inspection of Metallic Surfaces

  Conventional image processing struggles to detect cracks on metallic surfaces (hex nuts) under real-world lighting and shadow variation.

  Engineering & Materials · 2024 · ai assisted · 2/10 importance

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