arXiv:2606. 16032v1 Announce Type: cross Abstract: Interest in applying data-driven approaches in manufacturing has grown significantly, particularly for mapping complex, high-dimensional relationships.
arXiv:2609.39738v1 Announce Type: new Abstract: Learning to generate machining process plans and toolpaths from B-rep CAD requires coupling discrete operation decisions with continuous tool motion as...
The article explores physics-informed and hybrid machine learning approaches for predicting bond quality and porosity in fused filament fabrication (FFF) parts. It examines three strategies—embedding physics constraints in the loss function, adding physics model outputs as inputs, and pre‑training with physics data—to enforce consistency with physical laws. Eight combinations of these strategies are tested, showing that integrating multiple approaches yields accurate predictions even with limited experimental data.
arXiv:2607. 07863v1 Announce Type: new Abstract: In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm.
The paper introduces a cooperative neural network framework for partially inverse designing high‑performance concrete (HPC) mixes. It combines an imputation model with a surrogate strength predictor and is trained cooperatively, enabling it to produce valid, performance‑consistent mix designs in a single forward pass without retraining for different constraints. Compared to baseline methods, the approach achieves higher strength consistency (R² 0.84–0.89) and reduces mean squared error by 42–60%.
arXiv:2606. 11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models.
arXiv:2609.07046v1 Announce Type: new Abstract: AI has revolutionized various engineering domains, but its impact on semiconductor device design and defect discovery is still limited, due to limited...
arXiv:2602. 15648v2 Announce Type: replace Abstract: Inverse design problems are common in engineering and materials science.
arXiv:2602. 09120v2 Announce Type: replace Abstract: Electrospinning is a powerful technique for producing micro to nanoscale fibers with application specific architectures.
This study introduces a data‑driven method for multi‑objective optimization in fused filament fabrication (FFF), targeting both geometric accuracy and filament bond quality. Experiments supply part‑quality data, which feed Bayesian neural network models that predict the two objectives while accounting for epistemic and aleatory uncertainties. Using these stochastic predictions, robustness‑based optimization explores nozzle temperature, speed, and layer thickness, producing Pareto surfaces that reveal trade‑offs and are validated through actual part manufacturing.
arXiv:2608. 11281v1 Announce Type: cross Abstract: Tool wear prediction is an important task in CNC machining, where accurate monitoring of tool condition supports product quality and process reliability.
The paper explores global sensitivity analysis (GSA) when both physics-based models and experimental data are available, focusing on physics-informed machine learning to improve sensitivity estimates. It evaluates two ML approaches—deep neural networks (DNN) and Gaussian processes (GP)—and two physics integration strategies: physics-constrained loss functions and sequential pre‑training with simulation followed by experimental fine‑tuning. Four models per ML type are constructed, incorporating model uncertainties into Sobol index calculations, and results show DNNs yield tighter sensitivity bounds than GP models, demonstrated on additive manufacturing and lake temperature examples.
arXiv:2606. 23851v1 Announce Type: new Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion (LPBF) additive manufacturing.