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Abstract Fatigue scattering caused by inherent geometrical defects in laser powder bed fusion (LPBF) imposes a great challenge for fabricating reliable load-bearing components. However, the lack of sufficient fatigue data and the limitation of runout conditions rationalize the need to bridge the gap between limited data and fatigue reliability. This work has developed two models based on censored linear regression (CR) and censored Gaussian process regression (CGP), respectively, to predict fatigue life scattering bounds at a given confidence for both as-built and heat-treated SS 316L samples. Furthermore, fatigue life reliability is modeled under different stress amplitudes with a CGP-based reliability model. 

Abstract While the complexity of laser powder bed fusion (LPBF) processes facilitates customized and metal-based functional parts to be built, existing process monitoring techniques have limitations. Therefore, the need for intricate process monitoring has grown. Non-uniform emission readings are correlated with overheating. Therefore, process monitoring of areas experiencing excess thermal emission during print to track potential overheating is needed. A process monitoring technique using deep neural network-long short-term memory (DNN-LSTM) deep learning (DL) models for emission tracking has been developed. The DNN component harnesses process parameters, while the LSTM harnesses the time-series emission structure on multiple sets of prints in parallel. Moreover, trust and interpretation of the opaque methodology are needed to make the process widely applicable. Existing explainable artificial intelligence (XAI) methods are inoperative with the model developed. We overcome this gap by developing an attribution-based XAI-enabled DNN-LSTM for predicting, explaining, and evaluating layer-wise emission prediction. Interpretation from attribution-based methods, namely, Shapley additive explanations, integrated gradient explanations, and local interpretable model-agnostic explanations, reveal an estimate of how each physics variable (process parameters, layer number, layer-wise average emission readings) impacts each future layer-wise average emission behavior as decided by the DL model. Finally, existing evaluation metrics of XAI are mostly domain-focused. We overcome this gap by establishing evaluation criteria appropriate for understanding the trust of the explanations in the context of thermal emission prediction for LPBF. 

Abstract Laser powder bed fusion (LPBF) is an enabling process manufacture of complex metal components. However, LPBF is prone to generate geometrical defects (e.g., porosity, lack of fusion), which causes a significant fatigue scattering. However, LPBF fatigue scattering data and analysis in the literature are not only sparse and limited to tension-compression mode but also inconsistent. This article presents a robust high-frequency fatigue testing method to construct stress-cycle curves of SS 316L to understand the scattering nature and predict the scattering pattern. A series of bending fatigue tests are performed at different stress amplitudes. Two different runout criteria are used to investigate fatigue life, fatigue limits, and scattering. The endurance limit reaches around 300 MPa for the defect size distribution at the selected process space. The defect size-based fatigue limit model is found to underestimate the endurance limit by about 30 MPa when comparing with the experimental data. Fatigue scattering is further calculated by using 95% prediction intervals, showing that low fatigue scattering is present at high stresses while a large variation of fatigue life occurs at stresses near the knee point. 

Abstract Deep learning has impacted defect prediction in additive manufacturing (AM), which is important to ensure process stability and part quality. However, its success depends on extensive training, requiring large, homogeneous datasets—remaining a challenge for the AM industry, particularly for small- and medium-sized enterprises (SMEs). The unique and varied characteristics of AM parts, along with the limited resources of SMEs, hamper data collection, posing difficulties in the independent training of deep learning models. Addressing these concerns requires enabling knowledge sharing from the similarities in the physics of the AM process and defect formation mechanisms while carefully handling privacy concerns. Federated learning (FL) offers a solution to allow collaborative model training across multiple entities without sharing local data. This article introduces an FL framework to predict section-wise heat emission during laser powder bed fusion (LPBF), a vital process signature. It incorporates a customized long short-term memory (LSTM) model for each client, capturing the dynamic AM process's time-series properties without sharing sensitive information. Three advanced FL algorithms are integrated—federated averaging (FedAvg), FedProx, and FedAvgM—to aggregate model weights rather than raw datasets. Experiments demonstrate that the FL framework ensures convergence and maintains prediction performance comparable to individually trained models. This work demonstrates the potential of FL-enabled AM modeling and prediction where SMEs can improve their product quality without compromising data privacy. 

Abstract Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation, including computational fluid dynamics (CFD), is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning method by integrating the conventional neural networks with the governing physical laws to predict the melt pool dynamics, such as temperature, velocity, and pressure, without using any training data on velocity and pressure. This approach avoids solving the nonlinear Navier–Stokes equation numerically, which significantly reduces the computational cost (if including the cost of velocity data generation). The difficult-to-determine parameters' values of the governing equations can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the extra penalty by incorporating governing PDEs, initial conditions, and boundary conditions in the PINN model. 

Prediction of surface topography in milling usually requires complex kinematics and dynamics modeling of the milling process, plus solving physical models of surface generation is a daunting task. This paper presents a multimodal data-driven machine learning (ML) method to predict milled surface topography. The proposed method predicts the height map of the surface topography by fusing process parameters and in-process acoustic information as model inputs. This method has been validated by comparing the predicted surface topography with the measured data. 

Abstract Powder bed fusion (PBF) is an additive manufacturing process in which laser heat liquefies blown powder particles on top of a powder bed, and cooling solidifies the melted powder particles. During this process, the laser beam heat interacts with the powder causing thermal emission and affecting the melt pool. This paper aims to predict heat emission in PBF by harnessing the strengths of recurrent neural networks. Long short-term memory (LSTM) networks are developed to learn from sequential data (emission readings), while the learning is guided by process physics including laser power, laser speed, layer number, and scanning patterns. To reduce the computational efforts on model training, the LSTM models are integrated with a new approach for down-sampling the pyrometry raw data and extracting useful statistical features from raw data. The structure and hyperparameters of the LSTM model reflect several iterations of tuning based on the training on the pyrometer readings data. Results reveal useful knowledge on how raw pyrometer data should be processed to work the best with LSTM, how physics features are informative in predicting overheating, and the effectiveness of physics-guided LSTM in emission prediction.