Additive Manufacturing Parts Research Articles

Active learning for adaptive surrogate model improvement in high-dimensional problems

This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a corresponding low-dimensional subspace of the input domain; then a surrogate model is built for each feature in its corresponding active subspace. A low-dimensional adaptive learning strategy is proposed to identify training samples to improve the surrogate model. In contrast to existing adaptive learning methods that focus on a scalar output or a small number of outputs, this paper addresses adaptive learning with high-dimensional input and output, with a novel learning function that balances exploration and exploitation, i.e., considering unexplored regions and high-error regions, respectively. The adaptive learning is in terms of the active variables in the low-dimensional space, and the newly added training samples can be easily mapped back to the original space for running the expensive physics model. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties). Various factors in the adaptive learning process are investigated, including the number of training samples, range and distribution of the adaptive training samples, contributions of various errors, and the importance of exploration versus exploitation in the learning function.

Fatigue crack initiation and propagation in plain and notched PBF-LB/M, WAAM, and wrought 316L stainless steel specimens

Additively manufactured (AM) components—either made by laser-powder bed fusion or wire and arc additive manufacturing—typically contain process-related defects on and near surfaces that can be removed by machining. Various studies have shown that post-treatment, such as machining significantly improves the fatigue strength of AM parts. To this day, however, hardly any studies have investigated the fatigue strength of post-treated additively manufactured components with notches. In this study, fatigue tests were performed on plain and notched specimens to determine and compare the crack initiation and crack propagation behavior due to different manufacturing-related effects. Tests were performed on specimens produced by the two aforementioned AM processes and compared to specimens taken from wrought sheets. The fatigue strength of AM materials is influenced by microstructure, defects, residual stress, and notches. PBF-LB/M specimens exhibit the highest fatigue strength in plain, notch-free conditions, attributed to differences in microstructure and static strength affecting fatigue crack initiation. Notched specimens show larger differences among materials, with PBF-LB/M having shorter fatigue crack propagation life related to line-type defect clusters, while the plain PBF-LB/M specimens are less affected as their fatigue strength is primarily determined by fatigue crack initiation.

Surface heterostructuring of 316L stainless steel manufactured by laser powder bed fusion and hot isostatic pressing

Metal additive manufacturing (AM), offering high freedom of design, has garnered attention as a cutting-edge manufacturing technology. Commonly, hot isostatic pressing (HIP), as post-processing, is utilized to remove undesirable defects in AM parts to obtain fully dense components. However, excessive heating during HIP can result in the deterioration of mechanical properties, limiting their potential for structural industry applications. Herein, we propose a new strategy to obtain an optimized gradient structure to achieve a substantial synergistic effect through ultrasonic nanocrystal surface modification (UNSM) on the HIP-processed AM substrate. The resulting microstructure shows an extended gradient layer reaching the center of the substrate with significant mechanical incompatibility between adjacent domains, showing an excellent combination of strength and ductility. Our study suggests that the optimized gradient structure with superior mechanical properties can be achieved by strategically exploiting HIP-induced effects, which are generally avoided in structural materials due to their deleterious effect on strength.

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.

Experimental investigation and validation of strength-based topology and fiber path optimization in additively manufactured continuous fiber reinforced composites

Material extrusion-based fused filament fabrication (FFF) is a widely used additive manufacturing (AM) technology that offers design freedom to fabricate intricate, robust, and lightweight parts when used with continuous carbon fiber (CCF) reinforced filaments. FFF is well-suited for optimization methods, as designers can specifically tailor the positioning of CCF filaments, albeit within certain manufacturing limitations. This study integrates experiments and finite element analysis (FEA) to investigate strength-based optimization methods applied to two benchmark structural domains and loading scenarios. Two methods were used to concurrently design the shape or topology and fiber paths: shape optimization using the level-set method, and topology optimization using both density-based solid orthotropic material with penalization (SOMP) and level-set methods. CCF filaments were printed to follow the optimized fiber paths, and a discontinuous fiber reinforced filament was used to fill in the remaining gaps. The parts were then compression molded for full consolidation, followed by testing for their stiffness and failure loads. FEA predicted locations and magnitudes of failure with reasonable accuracy, even in regions with complex stress states and fiber paths. X-ray micro-computed tomography (µCT) examination of failed coupons showed that fiber flow (deviations) during compression molding occurred and impacted parts’ performance. Finally, comparing designs optimized for stiffness versus strength showed premature failures due to stress concentrations in stiffness-optimized designs. This study underscores the critical need for integrating proper optimization strategies with realistic AM constraints and processing-induced defects to enhance the performance and reliability of CCF reinforced AM parts.

Current Research Situation and Prospect of Wire and Arc Additive Manufacturing of Titanium Alloy

Wire arc additive manufacturing is an important part of additive manufacturing. Because of its low processing cost, high forming efficiency, high material utilization rate and low equipment cost, it is favored in the fields of medical and aerospace. This paper briefly discuss the technologies of titanium alloy wire arc additive manufacturing. The influence of different deposited parameters, interpass rolling, ultrasonic assistance and heat treatment on the forming quality of titanium alloy wire arc additive components are summarized and analyzed. Finally, combined with the actual engineering requirements, the problems and research directions of the development of titanium alloy additive manufacturing are analyzed.

Influence of ultrasonic excitation on the melt pool and microstructure characteristics of Ti-6Al-4V at powder bed fusion additive manufacturing solidification velocities

Cavitation and acoustic streaming created by in situ high-intensity ultrasound have been exploited for microstructural refinement during directed energy deposition (DED) additive manufacturing (AM) of alloy Ti‐6Al‐4V. Whether the same ultrasound-driven mechanisms are applicable to powder bed fusion (PBF) of Ti‐6Al‐4V remains uncertain. The primary factors that control the microstructure during deposition processing are the solidification velocity and temperature gradient, which are orders of magnitude higher for PBF than DED processes. This work examines the role of high-intensity ultrasound on the melt pool and resulting microstructure of Ti‐6Al‐4V under PBF solidification conditions. The effect of the laser scanning velocity and ultrasonic excitation on the melt pool and grain structure characteristics is interrogated through controlled line scans and area scans without powder. Temperature field simulations are used to estimate the solidification velocities and temperature gradients and to compare the processing conditions with previously characterized columnar-to-equiaxed transition diagrams. The acoustic pressures during processing are estimated using a coupled field acoustic-elastic finite element simulation and used in a Keller-Miksis model to assess the likelihood of cavitation vs. cavity size. The microstructure of the samples with or without ultrasonic excitation was characterized, allowing the equivalent grain diameters and aspect ratios to be compared. All samples, including those exposed to scanning velocities where cavitation did not occur, showed a reduction in grain aspect ratio when subjected to ultrasonic excitation, but the effect on equivalent grain diameter was inconclusive. A key finding is that the primary effects of ultrasonic excitation may be attributed to acoustic streaming, rather than cavitation, during PBF processing. Further development of the ultrasonic excitation technique explored may permit tailoring of the microstructure and texture characteristics in bulk AM parts.

A Smart, Data-Driven Approach to Qualify Additively Manufactured Steel Samples for Print-Parameter-Based Imperfections.

With additive manufacturing (AM) processes such as Wire Arc Additive Manufacturing (WAAM), components with complex shapes or with functional properties can be produced, with advantages in the areas of resource conservation, lightweight construction, and load-optimized production. However, proving component quality is a challenge because it is not possible to produce 100% defect-free components. In addition to this, statistically determined fluctuations in the wire quality, gas flow, and their interaction with process parameters result in a quality of the components that is not 100% reproducible. Complex testing procedures are therefore required to demonstrate the quality of the components, which are not cost-effective and lead to less efficiency. As part of the project "3DPrintFEM", a sound emission analysis is used to evaluate the quality of AM components. Within the scope of the project, an approach was being developed to determine the quality of an AM part dependent not necessarily on its geometry. Samples were produced from WAAM, which were later cut and milled to precision. To determine the frequencies, the samples were put through a resonant frequency test (RFM). The unwanted modes were then removed from the spectrum produced by the experiments by comparing it with FEM simulations. Later, defects were introduced in experimental samples in compliance with the ISO 5817 guidelines. In order to create a database of frequencies related to the degree of the sample defect, they were subjected to RFM. The database was further augmented through frequencies from simulations performed on samples with similar geometries, and, hence, a training set was generated for an algorithm. A machine-learning algorithm based on regression modelling was trained based on the database to sort samples according to the degree of flaws in them. The algorithm's detectability was evaluated using samples that had a known level of flaws which forms the test dataset. Based on the outcome, the algorithm will be integrated into an equipment developed in-house to monitor the quality of samples produced, thereby having an in-house quality assessment routine. The equipment shall be less expensive than conventional acoustic equipment, thus helping the industry cut costs when validating the quality of their components.

Design and implementation of a machine log for PBF-LB/M on basis of IoT communication architectures and an ETL pipeline

Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M) has gained more industrial relevance and already demonstrated applications at a small series scale. However, its widespread adoption in various use cases faces challenges due to the absence of interfaces to established Manufacturing Execution Systems (MES) that support customers in the predominantly data-driven quality assurance. Current state-of-the-art PBF-LB/M machines utilize communication architectures, such as OPC Unified Architecture (OPC UA), Message Queuing Telemetry Transport (MQTT) and Representational State Transfer Application Programming Interface (REST API). In the context of the Reference Architecture Model Industry 4.0 (RAMI 4.0) and the Internet of Things (IoT), the assets, particularly the physical PBF-LB/M machines, already have an integration layer implemented to communicate data such as process states or sensor values. Missing is an MES component acting as a communication and information layer. To address this gap, the proposed Extract Transform Load (ETL) pipeline aims to extract relevant data from the fabrication of each build cycle down to the level of scan vectors and additionally to register process signals. The suggested data schema for archiving each build cycle adheres to all terms defined by ISO/TC 261—Additive Manufacturing (AM). In relation to the measurement frequency, all data are reorganized into entities, such as the AM machine, build cycle, part, layer, and scan vector. These scan vectors are stored in a runtime-independent format, including all metadata, to be valid and traceable. The resulting machine log represents a comprehensive documentation of each build cycle, enabling data-driven quality assurance at process level.

Towards a Sustainable Laser Powder Bed Fusion Process via the Characterisation of Additively Manufactured Nitinol Parts

Is additive manufacturing (AM) a sustainable process? Can the process be optimised to produce sustainable AM parts and production techniques? Additive manufacturing offers the production of parts made of different types of materials in addition to the complex geometry that is difficult or impossible to produce by using the traditional subtractive methods. This study is focused on the optimisation of laser powder bed fusion (L-PBF), one of the most common technologies used in additive manufacturing and 3D printing. This research was carried out by modulating the build layer thickness of the deposited metal powder and the input volumetric energy density. The aim of the proposed strategy is to save the build time by maximizing the applied layer thickness of nitinol powder while retrieving the different AM part properties. The saving in the process time has a direct effect on the total cost of the produced part as a result of several components like electric energy, inert gas consumption, and labour. Nickel-rich nitinol (52.39 Ni at.%) was selected for investigation in this study due to its extremely high superplastic and shape memory properties in addition to the wide application in various industries like aerospace, biomedical, and automotive. The results obtained show that significant energy and material consumption can be found by producing near full dens AM parts with limited or no alteration in chemical and mechanical properties.

Understanding the strain localization in additively manufactured materials: Micro-scale tensile tests and crystal plasticity modeling

Metallic parts fabricated by additive manufacturing (AM) usually exhibit unique microstructures and non-negligible residual stresses compared with the counterparts produced by conventional manufacturing. These inherent microstructural factors strongly affect the mechanical response of the as-built AM parts. In this study, we focus on the strain localization behavior of 316L stainless steel produced by laser powder-bed-fusion. In-situ tensile tests under a scanning electron microscope are performed, and the digital image correlation method is used to measure the strain distribution combined with electron backscatter diffraction. Meanwhile, a dislocation-based crystal plasticity finite element model incorporating residual stresses is developed to study the origins of the strain localization in the AM material. The results indicate that strain localization in AM materials is closely associated with microstructural features, encompassing behaviors related to slip activities, interactions with neighboring grains and dislocation evolutions. Additionally, the columnar grain features also render the strain distribution sensitive to the loading direction. The strain localization is serious in some small grains with high residual stresses, while in large grains the effect is less significant. These factors collectively contribute to the increasing likelihood of strain localization occurring in the AM microstructures with heterogeneous grain size and texture distribution. This work provides detailed insights into the strain localization in AM materials and would facilitate the manufacturing parameter optimization of AM materials by tuning the microstructure to reduce deformation inhomogeneity.

On the Post‐Processing of Complex Additive Manufactured Metallic Parts: A Review

Additive manufacturing (AM) is gaining more attention due to its capability to produce customized and complex geometries. However, one significant drawback of AM is the rough surface finish of the as‐built parts, necessitating post‐processing for achieving the desired surface quality that meets application requirements. Post‐processing of complex geometries, such as parts with internal holes, lattice structures, and free‐form surfaces, poses unique challenges compared to other components. This review classifies various post‐processing methods employed for complex AM parts, presenting the experimental conditions for each treatment alongside the resulting improvement in surface roughness as a success criterion. The post‐processing methods are categorized into four groups: electrochemical polishing (ECP), chemical polishing (CP), mechanical polishing, and hybrid methods. Notably, mechanical methods exhibit the highest roughness improvement at 69.9%, followed by ECP (59.9%), hybrid methods (47.4%), and CP (49.5%). Nevertheless, mechanical post‐processing techniques are less frequently utilized for lattice parts, making chemical or electrochemical methods more promising alternatives. In summary, all four categories of post‐processing methods can improve the internal surfaces quality of AM holes. While mechanical methods offer the most substantial roughness improvement overall, chemical and electrochemical methods show particular potential for addressing the challenges associated with complex geometries.

Low-cycle compression-compression fatigue behavior of MEX-printed PLA parts

Due to cyclic loads, additively manufactured (AM) parts can collapse at lower stress values than the strength corresponding to static loads. To date, there is limited research on the fatigue life of AM polymers. The present work investigates the fatigue behavior of material extrusion (MEX) printed Polylactic acid (PLA) parts. In this regard, the low-cycle fatigue (LCF) domain was studied and a compression-compression fatigue test setup was adopted. Initially, before the experimental tests, the MEX-printed samples were subjected to physical assessments. The printed fatigue samples presented a high accuracy with dimensional relative errors below 0.04%, while 6.66% was noted as their mass relative error. Subsequently, quasi-static and cyclic tests were performed in order to determine the mechanical behavior of the AM parts, with special focus on the low-cycle domain of the S-N (or Wöhler) curve. The quasi-static compression behavior of the PLA samples is typical of cellular materials, the samples highlighting a shear band as a failure mechanism. Five load levels were used to plot the low-cycle domain of the S-N (or Wöhler) curve with its 95% level confidence bands. The tests were performed in force-controlled mode with the stress asymmetry ratio R = 0.1 and at the test frequency of f = 10 Hz. An exponential relationship was also found between the strain at break and the number of cycles until failure. The failure of the samples in fatigue load occurred suddenly.

Information Embedding in Additively Manufactured Parts Through Printing Speed Control

Abstract Information lithography in manufacturing is a broad set of techniques for encoding sequences of bits as physical or behavioral features in physical parts. It is an effective approach for part traceability and anti-counterfeiting. Several techniques have recently been proposed for embedding 2D codes in 3D printed parts by local control of geometry or material. This paper presents an approach to embed and retrieve information in additive manufacturing (AM) parts by controlling the printing process parameters. The approach leverages variations in printing speed to encode information on the surface of AM parts. Optical imaging devices, such as 2D scanners and optical profilometers, are employed to read the embedded information, enabling the capture of local height differences on the part surfaces that embody 2D codes such as QR codes. The retrieved information is processed using computer vision techniques such as morphological segmentation and binary classification. First, the impact of variations in the encoding parameters on the information retrieval accuracy is characterized. Then, the feasibility and effectiveness of the proposed scheme are demonstrated through experimental results, showcasing a high accuracy in retrieving encoded messages and successfully distinguishing subtle surface features resulting from varying printing speeds. The proposed approach offers an inexpensive and efficient method for information lithography, allowing for the secure embedding of information, e.g., serial numbers and watermarks, while addressing counterfeiting and security concerns in diverse industries.

Feature-based energy consumption quantitation strategy for complex additive manufacturing parts

Additive manufacturing (AM) has significant advantages, including design freedom, mass customization, and complex-structure manufacturing capabilities. As reducing the manufacturing energy is challenging in terms of the industrial sustainability, the AM energy consumption must be further explored. Existing energy consumption quantitation methods for AM require complex models that considering the energy characteristics of equipment. Therefore, we propose a feature-based energy consumption quantitation method for complex AM parts using simple models and suitable for different AM technologies. First, a feature segmentation method is proposed to divide complex AM parts into typical AM features. Then, the energy consumption model for each AMF is developed for energy consumption quantification during part fabrication. Finally, the energy consumption of a typical mechanical part manufactured via three different AM processes is investigated using the proposed method and measured experimentally. The results show that the proposed method can effectively and rapidly predict the energy consumption of AM processes with an accuracy of more than 95 %. Furthermore, the efficiency of the three AM processes is compared and discussed to address suitable efficiency improvement methods. In general, the proposed method can be integrated into three-dimensional AM models, providing a reference for the structural optimization of AM parts and sustainable manufacturing.

Texture tailored through combination of molten pool morphology and scanning path in laser additive manufacturing of alloy 718

Texture control is vital for the performance improvement of laser additive manufacturing parts. In the current manuscript, the effect of molten pool morphology and scanning path on texture have been studied for Inconel 718 sample fabricated by directed energy deposition (DED). The molten pool formed at a pulse period of 20 ms is shallower than that formed at a pulse period of 100 ms. The V-shaped grains (<110> // BD) are generated in the overlapping area for all samples. While Z-type grains (<110> // SD) are generated in the molten pool center for the sample with bidirectional scanning path at a pulse period of 20 ms. The unique texture with V-shaped grains (<110> // BD) in the overlapping area and Z-type grains (<110> // SD) in the molten pool center was firstly observed in the current manuscript. The value of Taylor factor of Z-type grains (<110> // SD) is larger than that of V-shaped grains (<110> // BD). Meanwhile, the effect of different heat treatments on the texture evolution was analyzed. This research provides a promising approach for texture customization, which can also increase the understanding of texture evolution.

Powder BED Additive Manufacturing using Machine Learning Algorithms for Multidisciplinary Applications: A Review and Outlook

Abstract: Additive manufacturing overcomes the limitations associated with conventional processes, such as fabricating complex parts, material wastage, and a number of sequential operations. Powder-bed additives fall under the category of additive manufacturing process, which, in recent years, has captured the attention of researchers and scientists working in various fields of science and engineering. Production of powder bed additive manufacturing (PBAM) parts with consistent and predictable properties of powders used during the manufacturing process plays an important role in deciding printed parts' reliability in aeronautical, automobile, biomedical, and healthcare applications. In the PBAM process, the most commonly used powders are polymer, metal, and ceramic, which cannot be effectively used without understanding powder particles' physical, mechanical, and chemical properties. Several metallic powders like titanium, steel, copper, aluminum, and nickel, several polymer polyamides (nylon), polylactide, polycarbonate, glass-filled nylon, epoxy resins, etc., and the most commonly used ceramic powders like aluminum oxide (Al2O) and zirconium oxide (ZrO2) can be utilized depending upon the method being adopted during PBAM process. Adoption of some post-processing techniques for powder, such as grain refinement can also be employed to improve the physical or mechanical properties of powders used for the PBAM process. In this paper, the effect of powder parameters, such as particle size, shape, density, and reusing of powder, etc., on printed parts have been reviewed in detail using characterization techniques such as X-ray computed tomography, scanning electron microscopy, and X-ray photoelectron spectroscopy. This helps to understand the effect of particle size, shape, density, virgin and reused powders, etc., used during the PBAM process. This article has reviewed the selection of appropriate process parameters like laser power, scanning speed, hatch spacing, and layer thickness and their effects on various mechanical or physical properties, such as tensile strength, hardness, and the effect of porosity, along with the microstructure evolution. One of the drawbacks of additive manufacturing is the variability in the quality of printed parts, which can be eliminated by monitoring the process using machine learning techniques. Also, the prediction of the best combination of process parameters using some advanced machine learning algorithms (MLA), like random forest, k nearest neighbors, and support vector machine, can be effectively utilized to quantify the performance parameters in the PBAM process. Thus, implementing machine learning in the additive manufacturing process not only helps to learn the fundamentals but helps to identify, predict and help to make actionable recommendations that help optimize printed parts quality. The performance of various MLAs has been evaluated and compared for projecting future research directions and suggestions. In the last part of this article, multidisciplinary applications of the PBAM process have been reviewed in detail. Additive manufacturing processes carried out by using conventional machines, called hybrid additive manufacturing, have also been reviewed by discussing their methods and arrangements in detail.

Untapped Opportunities in Additive Manufacturing with Metals: From New and Graded Materials to Post-Processing

Metal additive manufacturing (AM) is an innovative manufacturing method with numerous metallurgical benefits, including fine and hierarchical microstructures and enhanced mechanical properties, thanks to the utilization of a local heat source and the rapid solidification nature of the process. High levels of productivity, together with the ability to produce complex geometries and large components, have added to the versatile applicability of metal AM with applications already implemented in various sectors such as medicine, transportation, and aerospace. To further enhance the potential benefits of AM in the context of small- to medium-scale bulk production, metallurgical complexities should be determined and investigated. Hence, this review paper focuses on three significant metallurgical aspects of metal AM processes: in situ alloying, functionally graded materials, and surface treatments for AM parts. The current text is expected to offer insights for future research works on metal AM to expand its potential applications in various advanced manufacturing sectors.

Surface roughness and microhardness improvement of laser cladding stainless-steel 316 by laser polishing based on multiple remelting

Laser polishing (LP) has emerged as a promising alternative to traditional post-treatment technologies. Nonetheless, the LP process encounters challenges when dealing with surfaces of high roughness, since the excessive thermal input could induce undesirable effects, including the formation of mid-frequency waviness, expansion of heat-affected zone (HAZ), etc. In this study, we investigated the surface roughness and the characters of the remelted layer and HAZ of polishing the laser-cladding 316 stainless steel layer deposited on the surface of a 45# steel cylinder by a 1064 nm hundred-watt pulsed laser and a 1080 nm thousand-watt CW laser respectively. It is observed that the combination of low power density and extended interaction time failed to produce minimum roughness by the pulse laser, contradicting existing LP theories. To address this, we developed a multi-physics model to elucidate the underlying mechanisms and propose an LP scheme based on multiple remelting cycles with the pulsed laser. The Sa roughness is reduced from 20.958 μm to 0.242 μm, decreasing 98.8 %. The processing quality is equivalent to the surface of the high-power CW laser (Sa of 0.283 μm), while the remelted layer and HAZ are significantly smaller. The pulse laser polishing based on multiple remelting increased the surface microhardness by 22 % to 560 HV, while the CW laser remelting reduced it by 33 % to 309 HV. And the HAZ microhardness all decreased in all cases. This study can provide a theoretical basis and solution for the surface finishing of additive manufacturing parts.

Process-Structure-Property Relationship Development in Large-Format Additive Manufacturing: Fiber Alignment and Ultimate Tensile Strength.

Parts made through additive manufacturing (AM) often exhibit mechanical anisotropy due to the time-based deposition of material and processing parameters. In polymer material extrusion (MEX), printed parts have weak points at layer interfaces, perpendicular to the direction of deposition. Poly(lactic acid) with chopped carbon fiber was printed on a large-format pellet printer at various extrusion rates with the same tool pathing to measure the fiber alignment with deposition via two methods and relate it to the ultimate tensile strength (UTS). Within a singular printed bead, an X-ray microscopy (XRM) scan was conducted to produce a reconstruction of the internal microstructure and 3D object data on the length and orientation of fibers. From the scan, discrete images were used in an image analysis technique to determine the fiber alignment to deposition without 3D object data on each fiber's size. Both the object method and the discrete image method showed a negative relationship between the extrusion rate and fiber alignment, with -34.64% and -53.43% alignment per extrusion multiplier, respectively, as the slopes of the linear regression. Tensile testing was conducted to determine the correlation between the fiber alignment and UTS. For all extrusion rates tested, as the extrusion multiplier increased, the percent difference in the UTS decreased, to a minimum of 8.12 ± 14.40%. The use of image analysis for the determination of the fiber alignment provides a possible method for relating the microstructure to the meso-property of AM parts, and the relationship between the microstructure and the properties establishes process-structure-property relationships for large-format AM.