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1. Predictive Modeling of Surface Topography Using Power Data in Wire Electrical Discharge Machining

   https://doi.org/10.1115/1.4069964  

   Liu, Bochi; Guo, Yifei; Guo, Y B; Guo, Weihong Grace (November 2025, Journal of Computing and Information Science in Engineering)

   Abstract Predicting surface topography in wire electrical discharge machining (WEDM) remains challenging due to complex electrical and thermal interactions during the process. Traditional methods relying on machining parameters and simplified surface finish indicators often fail to capture the full complexity of surface topography. This study introduces a novel approach that leverages cutting power data, an accessible proxy for discharge parameters, to predict and reconstruct surface topography in WEDM. The method decomposes surface topography into roughness, waviness, and form components using complete ensemble empirical mode decomposition with adaptive noise and sample entropy. Each component is modeled separately: roughness as a random process following a normal distribution, waviness as a uniform sinusoidal function, and form as a cubic B-spline. A multi-task convolutional neural network is developed to simultaneously predict these components based on features extracted from cutting power data. Experimental validation was conducted on a 6061 aluminum (AL 6061) workpiece machined under various cutting conditions. The proposed model outperformed benchmark models in both frequency and spatial domain metrics, accurately capturing key surface characteristics such as roughness variations, waviness patterns, and shape deviations. These results demonstrate an enhanced reliability in surface topography predictions. By integrating cutting power data with an advanced decomposition technique and artificial intelligence model, this study provides a framework for real-time surface topography prediction in WEDM, contributing to improved process optimization and control. 

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2. Characterizing the as-built surface topography of Inconel 718 specimens as a function of laser powder bed fusion process parameters

   https://doi.org/10.1108/RPJ-05-2024-0190  

   Raeymaekers, Bart; Berfield, Thomas (October 2024, Rapid Prototyping Journal)

   PurposeThe ability to use laser powder bed fusion (LPBF) to print parts with tailored surface topography could reduce the need for costly post-processing. However, characterizing the as-built surface topography as a function of process parameters is crucial to establishing linkages between process parameters and surface topography and is currently not well understood. The purpose of this study is to measure the effect of different LPBF process parameters on the as-built surface topography of Inconel 718 parts. Design/methodology/approachInconel 718 truncheon specimens with different process parameters, including single- and double contour laser pass, laser power, laser scan speed, build orientation and characterize their as-built surface topography using deterministic and areal surface topography parameters are printed. The effect of both individual process parameters, as well as their interactions, on the as-built surface topography are evaluated and linked to the underlying physics, informed by surface topography data. FindingsDeterministic surface topography parameters are more suitable than areal surface topography parameters to characterize the distinct features of the as-built surfaces that result from LPBF. The as-built surface topography is strongly dependent on the built orientation and is dominated by the staircase effect for shallow orientations and partially fused metal powder particles for steep orientations. Laser power and laser scan speed have a combined effect on the as-built surface topography, even when maintaining constant laser energy density. Originality/valueThis work addresses two knowledge gaps. (i) It introduces deterministic instead of areal surface topography parameters to unambiguously characterize the as-built LPBF surfaces. (ii) It provides a methodical study of the as-built surface topography as a function of individual LPBF process parameters and their interaction effects. 

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3. Intermethod comparison and evaluation of near surface residual stress in aluminum parts subject to various milling parameters

   https://doi.org/10.1007/978-3-030-30098-2  

   Chighizola, Christopher R.; D’Elia, Christopher R.; Hill, Michael R. (June 2019, Residual Stress, Thermomechanics & Infrared Imaging and Inverse Problems, Volume 6; Proceedings of the 2019 Annual Conference on Experimental and Applied Mechanics)

   Near surface residual stress (NSRS) induced by machining (e.g., milling) is known to drive distortion in machined aluminum, particularly in thin complex geometries with tight tolerance requirements where large distortion is undesirable. The understanding and characterization of NSRS in milled aluminum parts is important and should be included in the design and manufacturing process. There exist a variety of experimental tests characterizing these stresses. The objective of this paper is to assess the quality of three experimental methods for evaluating NSRS in prismatic aluminum parts subject to various milling parameters. The three methods are: hole-drilling, slotting, and x-ray diffraction, all of which include incremental material removal. The aluminum parts are cut from stress-relieved plate, AA7050-T7451. A combination of milling table and tool speeds are used to machine a flat surface in the parts. Measurements are made at specified locations and depths on each part. NSRS data from the hole drilling and slotting measurements were comparable; NSRS data from x-ray diffraction differed and was less repeatable. NSRS data for different milling parameters shows that the depth of NSRS increases with feed per tooth but is unaffected by different cutting speeds. 

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4. Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks

   https://doi.org/10.1007/s40544-023-0826-7  

   Seo, Junhyeon; Rao, Prahalada; Raeymaekers, Bart (December 2023, Friction)

   Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion (LPBF), such as partially melted metal particles, porosity, laser scan lines, and balling. Thus, we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network. We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters, radially averaged power spectral density, and material ratio curves. The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements. Furthermore, it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate, e.g. relating as-built surface topography to LPBF process parameters, or implementing digital surface twins to monitor complex end-use LPBF parts, amongst other applications. 

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5. Time-Series Data-Driven Machine Learning of Milling Chattering

   https://doi.org/10.1115/1.4069196  

   Hu, L; Gao, R; Guo, Y B (October 2025, Journal of Manufacturing Science and Engineering)

   Abstract Milling is a critical manufacturing process to produce high-value components in aerospace, tooling, and automotive industries. However, milling is prone to chatter, a severe vibration that damages surface quality, cutting tools, and machines. Traditional experimental and mechanistic methods of chatter prediction have significant limitations. This study presents a data-driven machine learning (ML) model to predict and quantify milling chatter directly based on time-series vibration data. Three ML models, including hybrid long short-term memory (LSTM)—fully convolutional network (FCN) model, gated recurrent unit (GRU)—FCN model, and temporal convolutional network (TCN) models, have been developed and verified by incorporating milling parameters to enhance prediction accuracy and stability. Among the proposed models, the best-performing ML model (GRU-FCN) demonstrates strong performance in chatter prediction and severity quantification, providing actionable insights with improved computational efficiency. The integration of milling parameters into the ML model notably enhances the prediction accuracy and stability, proving particularly effective in real-time monitoring scenarios. 

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