...) by correlating the process, wear performance and sustainability. Design/methodology/approach This study uses machine learning ( ML ) to optimize process parameters in additive manufacturing ( AM ), focusing on wear resistance, accuracy, cost and printing time. A gradient boosting regressor is trained...

... to dimensional inaccuracies and structural misalignments in printed parts. The present study aims to develop a machine learning ( ML )-based framework supported by sensor data to detect layer shifting at an early stage of the printing process. Design/methodology/approach A Cartesian FFF 3D printer...

...Xina Huang; Deng Wang; Wenjing Chen; Miaoyi Li Purpose The purpose of this paper is to accurately predict and optimize the process parameters of laser metal deposited Ni-based superalloys using machine learning ( ML ). Design/methodology/approach Orthogonal experiments were carried out...

... earlier, this section outlines a comprehensive framework designed to leverage the curated data set for machine-learning-driven modeling of the L-PBF process. Beyond data collection, relevant experimental parameters and material properties were compiled to serve as critical inputs for predictive models...

...Veera Siva Reddy Bobbili; Chandrasekhara Sastry C; Hafeezur Rahman A Purpose This study aims to establish an interpretable machine learning framework for predicting high-strain-rate mechanical responses of laser powder bed fusion ( LPBF )-fabricated A286 steel lattice structures subjected to split...

... parameters influence mechanical performance and to explore the potential of machine learning in enhancing material property prediction and optimization. Design/methodology/approach A series of controlled experiments were conducted by varying nozzle diameter, nozzle temperature, and fan speed. The EAC...

... methods. Furthermore, the thermal model predictions align well with experimental findings, reinforcing the validity of the proposed optimization strategy. Originality/value This study provides a novel hybrid approach to optimizing FFF process parameters by integrating machine learning techniques...

... attribution to the original publication and authors. The full terms of this licence may be seen at Link to the terms of the CC BY 4.0 licence. Fused filament fabrication (FFF) Multi-objective optimisation Mechanical properties Build time Machine learning High-impact polystyrene (HIPS...

...Deepak Mudakavi; Somashekara M. Adinarayanappa Purpose Functionally graded materials (FGMs) possess gradual compositional and microstructural transitions. This study aims to predict the mechanical properties of FGMs, especially the microhardness, by integrating experimentation and machine learning...

... predicted by machine learning models (R² > 0.86), with nozzle size and print speed identified as the most influential parameters. Angularity showed moderate predictability but was statistically controlled under specific settings. Straightness and parallelism were better explained through...

... conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning. Design/methodology/approach A high-speed camera was used...

... manufacturing Machine learning Process optimisation Due to the high thermal conductivity of the Al-Si-10Mg relative to the other alloys, the parameter space of this alloy has been expanded to ensure that all 4 melt morphologies are observed in the track images. The build files for each alloy were...

...Muhammad Arif Mahmood; Marwan Khraisheh; Andrei C. Popescu; Frank Liou Purpose This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing the laser powder bed fusion (LPBF) process...

.... Originality/value The proposed solution entails the development of a promising tool for the optimization of the quality in 3D prints based on machine learning algorithms. 15 11 2023 05 03 2024 07 04 2024 © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use...

.... The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates. Design/methodology/approach The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models...

... hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models. Design/methodology/approach The approach that is used by this study is to use traditional image processing and classification...

... of a computer-aided design (CAD) model that expresses the probability that the model is fabricated correctly via an AM technology for a specific application. Design/methodology/approach This study predicts the dimensional deviations of the manufactured object per vertex and per part using a machine learning...

... temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning (ML) models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing. Findings Using...

... fusion Machine learning Dempster-Shafer evidence Powder-bed fusion (PBF) process, known as a category of additive manufacturing (AM) for functional metal part fabrication has shown a great application prospect recently. It has some unique advantages compared with the conventional manufacturing...

... for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure. Design/methodology/approach The introduced methodology uses regression-based machine learning algorithms...