..., multiple machine learning regression algorithms and an artificial neural network ( ANN ) were systematically compared under unified preprocessing conditions. In addition, ensemble models were constructed using histogram-based gradient boosting regressor ( HGBR ), gradient boosting machine ( GBM ), random...

...) 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...

... manufacturing. Relative density Laser powder bed fusion Printing parameters Machine learning Agencia Nacional de Investigación y Desarrollo (FONDECYT INICIACIÓN Nº11260020), Agencia Nacional de Investigación y Desarrollo (FONDECYT Nº 1201068), Agencia Nacional de Investigación y Desarrollo...

...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...

... optimisation algorithm. The layer thickness was concluded to be the most significant factor for SR, BT and VPE . In another literature (Cerro et al., 2021), multiple machine learning models were evaluated to study the non-linearity effects of layer thickness, printing speed, number of contours...

...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...

...) of the defects listed above. This is where high part density is usually found. Laser powder bed fusion Additive manufacturing Machine learning Process optimisation Laser powder bed fusion (LPBF) is a type of additive manufacturing (AM) that uses a laser beam to melt regions of a metal powder...

...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. The first step was the hardware setup concerning the inspection of the feedstock material. The goal was to secure the purity...

.... 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...

... 2020 20 06 2021 25 09 2021 21 10 2021 © Emerald Publishing Limited 2021 Emerald Publishing Limited Licensed re-use rights only Yingjie Zhang can be contacted at: arabinjasmine@scut.edu.cn Additive manufacturing Condition monitoring Data fusion Machine learning...