Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing

Improved Parameterization of the Transcranial Doppler Signal

Laser Directed Energy Deposition (L-DED) is a notable additive manufacturing method in which metal powder is sprayed through a nozzle and then consolidated layer by layer using a laser. Unlike other additive manufacturing processes, DED has fewer constraints on the size of the fabricated parts, making it advantageous for the production of large-scale components. However, the use of DED in additive manufacturing requires careful optimization of various process parameters, including laser power, powder feed rate, nozzle scanning speed, and deposition path, as these parameters significantly influence the geometry and properties of the fabricated parts. Recent studies have extensively investigated the microstructure and properties of parts fabricated via DED at different energy densities, but research on variables related to powder feed is still lacking. In this study, using Powder Line-Density (PLD) as a parameter, changes in bead geometry, microstructure and mechanical property followed by variation in powder feed density while conducting DED additive manufacturing with STS316L were observed. Controlled by powder feed rate and scanning speed, the Powder Line-Density was utilized for 1-line deposition with STS316L alloy powder, enabling the observation of bead geometry and melt-pool shape during the deposition process. Additionally, square-shaped specimens were manufactured via DED with controlled Powder LineDensity to observe the resulting microstructure and mechanical properties. It was observed that, even for the same energy density, specimens exhibited distinct grain morphologies, microstructure, and mechanical properties depending on the Powder Line-Density, with a particularly significant change in anisotropy. This highlights the importance of powder feed density as a key variable alongside energy density to optimize DED additive manufacturing processes. The findings of this study are expected to contribute to the control of anisotropy and strength of fabricated components in metal additive manufacturing processes by the regulation of powder feed density.

The advent of additive manufacturing (AM) has brought about radically new ways of designing and manufacturing of end-use parts and components, by exploiting freedom of design. Due to the unique manufacturing process of AM, both design and process parameters can strongly influence the part properties, thereby enlarging the possible design space. Thus, finding the optimal combination of embodiment design and process parameters can be challenging. A structured and systematic approach is required to effectively search the enlarged design space, to truly exploit the advantages of AM. Due to lowered costs in computing and data collection in the recent years, data-driven strategies have become a viable tool in characterization of process, and researches have starting to exploit data-driven strategies in the design domain. In this paper, a state-of-the-art data-driven design strategy for fused filament fabrication (FFF) is presented. The need for data-driven strategies is explored and discussed from design and process domain, demonstrating the value of such a strategy in designing an FFF part. A comprehensive review of the literature is performed and the research gaps and opportunities are analysed and discussed. The paper concludes with a proposed data-driven framework that addresses the identified research gaps. The proposed framework encompasses knowledge management and concurrent optimization of embodiment design and process parameters to derive optimal FFF part design. Contribution of this paper is twofold: A review of the state-of-the-art is presented, and a framework to achieve optimal FFF part design is proposed.

Advanced digital image segmentation framework implemented by using service oriented architecture is presented. The intelligence is not incorporated just in a segmentation method, which is controlled by 11 parameters, but mostly in a routine for easier parameters’ values determination. Three different approaches are implemented: 1) manual parameter value selection, 2) interactive step-by-step parameter value selection based on visual image content, and 3) fast and intelligent parameter value determination based on machine learning. Intelligence of second and third approach is introduced by end-users in the repeated interaction with our prototype in attempts to correctly segment out the structures from image. Fast and intelligent parameter determination predicts a new set of parameters’ values for current image being processed based on knowledge models constructed from previous successful (positive samples) and unsuccessful (negative samples) parameter selections. Such approach pointed out to be very efficient and fast, especially if we have many positive and negative samples in the learning set.

Laser Powder Bed Fusion (LPBF) and Laser Directed Energy Deposition (LDED) additive manufacturing technologies, as core techniques in high-end manufacturing, enable the production of complex parts and hold broad application prospects in aerospace, high-end equipment, and other fields. However, issues such as process parameter fluctuations and material property variations leading to defects have become stumbling blocks hindering the advancement of additive manufacturing toward mass production. In recent years, the rapid advancement of machine learning has shifted the research focus of LDED and LPBF from “process parameter optimization” to “AI-driven online quality control.” Integrating advanced sensing technologies has also become particularly crucial. This review outlines the progress and current status of intelligent monitoring research in LPBF and LDED additive manufacturing across five dimensions: process information sensing, internal/external defect assessment based on in-situ and ex-situ measurements, performance quality evaluation, process parameter optimization, and quality control. It highlights the challenges both technologies face in achieving mass production and outlines prospects.

Metal additive manufacturing: Principles and applications

The Additive Manufacturing (AM) modality of Laser-Based Powder Bed Fusion of Polymers (PBF-LB/P) is an established method for manufacturing semi-crystalline polymers. Like other AM processes, the selection of PBF-LB/P process parameters is critical as it has direct effect on final part properties. While prior research has been predominantly focused on polyamides (e.g., nylon 12), there exists a gap in exploring how process parameters affect higher performance polymers, such as polyphenylene sulfide (PPS). This work aims to explore the effects of PBF-LB/P process parameters on PPS parts printed via PBF-LB/P. While prior PBF-LB/P parameter research primarily relies on evaluating energy input to the system through a single numerical value of energy density, this study investigates the interplay of the print parameters within the energy density equation. To achieve these goals, an analysis was performed on the influence of the laser power, hatch spacing, and beam velocity on ultimate tensile strength (UTS), modulus, and crystallinity of printed parts. A Taguchi L8 array was used in balancing the print parameter combinations allowing for isolation of variance to the specific factors and interactions. Through this approach, print parameter combinations that improved UTS and modulus were identified. Additionally, the study revealed that numerically equivalent energy densities did not lead to equivalent performance, underscoring the significance for including the constitutive process parameters within the energy density equation when establishing process property relationships in printing with PBF-LB/P.

PurposeThe purpose of this paper is to develop a general mathematic approach to model the microstructures of porous structures produced by additive manufacturing (AM), which will result in fractal surface topography and higher roughness that have greater influence on the performance of porous structures.Design/methodology/approachThe overall shapes of pores were modeled by triply periodic minimal surface (TPMS), and the micro-roughness details attached to the overall pore shapes were represented by Weierstrass–Mandelbrot (W-M) fractal representation, which was integrated with TPMS along its normal vectors. An index roughly reflecting the irregularity of fractal TPMS was proposed, based on which the influence of the fractal parameters on the fractal TPMS was qualitatively analyzed. Two complex samples of real porous structures were given to demonstrate the feasibility of the model.FindingsThe fractal surface topography should not be neglected at a micro-scale level. In addition, a decrease in the fractal dimension Ds may exponentially make the topography rougher; an increase in the height-scaling parameter G may linearly increase the roughness; and the number of the superposed ridges has no distinct influence on the topography. Furthermore, the synthesis method is general for all implicit surfaces.Practical implicationsThe method provides an alternative way to shift the posteriori design paradigm of porous media to priori design mode through numeric simulation. Therefore, the optimization of AM process parameters, as well as the porous structure, can be potentially realized according to specific functional requirement.Originality/valueThe synthesis of TPMS and W-M fractal geometry was accomplished efficiently and was general for all implicit freeform surfaces, and the influence of the fractal parameters on the fractal TPMS was analyzed more systematically.

An economic evaluation of hybrid WAAM-subtractive manufacturing in relation to deposition process parameters

Process parameter optimization for laser directed energy deposition (LDED) of Ti6Al4V using single-track experiments with small laser spot size

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML-related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM-related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM-related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM-related research areas.

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

The 3D printing, also known as Additive Manufacturing (AM), is an emerging manufacturing technology in various industries like in automotive, aerospace, space applications, etc. Among the various AM process, fused deposition modeling (FDM) process is well known and widely used in developing the models. In spite of its advantages, few disadvantages of FDM process namely high energy consumption, production time and emission concentration, and rates of Fine Particles (FP), Ultra-Fine Particles (UFPs) as well as Volatile Organic Compounds (VOCs). Selection of optimum parameters for any FDM process to optimize the environmental effects is a cumbersome and difficult task and is a multi-criteria decision-making (MCDM) problem. In this paper, an attempt is made to optimize the green (i.e., environmental parameter into account) FDM process parameters using an integrated MCDM method, i.e., Entropy with VIKOR methods considering two inputs like print temperature (PT) and bed temperature (BT) and three outputs such as particle number concentration (PNC0.3–10 µm), particle number concentration (PNC5.6–560 µm) and specific emission rate (SER). Here, entropy method is used for extraction of precise priority weight while optimal selection of green FDM parameters using VIKOR method. In addition, most significant parameter is determined through parametric analysis. Results shows that, optimal conditions, i.e., PT as 254 °C and PBT as 30 °C and corresponding output as PNC0.3–10 µm = 14 #/cm3, PNC5.6–560 µm = 2.1 × 105 #/cm3 and SER’s = 2.0 × 109 are obtained. The parameter SER is shown significant effect on the green FDM process parameter among other parameters.KeywordsGreen FDMEntropyVIKOROptimizationParametric analysis

The strength of salt bridges resulting from the interaction of cations and anions is modulated by their environment. However, polarization of the solvent molecules by the charged moieties makes the accurate description of cation-anion interactions in an aqueous solution by means of a pairwise additive potential energy function and classical combination rules particularly challenging. In this contribution, aiming at improving the representation of solvent-exposed salt-bridge interactions with an all-atom non-polarizable force field, we put forth here a parametrization strategy. First, the interaction of a cation and an anion is characterized by hybrid quantum mechanical/molecular mechanics (QM/MM) potential of mean force (PMF) calculations, whereby constantly exchanging solvent molecules around the ions are treated at the quantum mechanical level. The Lennard-Jones (LJ) parameters describing the salt-bridge ion pairs are then optimized to match the reference QM/MM PMFs through the so-called nonbonded FIX, or NBFIX, feature of the CHARMM force field. We apply the new set of parameters, coined CHARMM36m-SBFIX, to the calculation of association constants for the ammonium-acetate and guanidinium-acetate complexes, the osmotic pressures for glycine zwitterions, guanidinium, and acetate ions, and to the simulation of both folded and intrinsically disordered proteins. Our findings indicate that CHARMM36m-SBFIX improves the description of solvent-exposed salt-bridge interactions, both structurally and thermodynamically. However, application of this force field to the standard binding free-energy calculation of a protein-ligand complex featuring solvent-excluded salt-bridge interactions leads to a poor reproduction of the experimental value, suggesting that the parameters optimized in an aqueous solution cannot be readily transferred to describe solvent-excluded salt-bridge interactions. Put together, owing to their sensitivity to the environment, modeling salt-bridge interactions by means of a single, universal set of LJ parameters remains a daunting theoretical challenge.

Additive manufacturing (AM) has recently been accorded considerable interest by manufacturers. Many manufacturing industries, amongst others in the aerospace sector, are already using AM parts or are investing in such manufacturing methods. Important material properties, such as microstructures, residual stress, and surface topography, can be affected by AM processes. In addition, a subtractive manufacturing (SM) process, such as machining, is required for finishing certain parts when accurate tolerances are required. This finish machining will subsequently affect the surface integrity and topography of the material. In this research work, we focused on the surface integrity of Ti-6Al-4V parts manufactured using three different types of AM and finished using an SM step. The aim of this study was to gain an understanding on how each process affects the resulting surface integrity of the material. It was found that each AM process affects the materials’ properties differently and that clear differences exist compared to a reference material manufactured using conventional methods. The newly generated surface was investigated after the SM step and each combination of AM/SM resulted in differences in surface integrity. It was found that different AM processes result in different microstructures which in turn affect surface integrity after the SM process.