Additive Manufacturing Parts Research Articles

Electrochemical polishing with glycol-based electrolyte of the curved internal channels fabricated via laser powder bed fusion

Electrochemical polishing (ECP) is used to process metal additive manufacturing parts with poor surface qualities arising from powder adhesion, spheroidal effect, and step effect during forming. Conventional ECP uses stationary acid-based solutions and very low current densities, which makes it challenging to efficiently polish complex-shaped internal channels. Typically, an oxide layer forms in the conventional aqueous-based electrolyte, which prevents uniform dissolution and consequently deteriorates surface quality. In this study, we proposed a modified ECP method for polishing curved internal channels in laser powder bed fusion (LPBF) 316 L stainless steel (SS) specimens using a high-speed flow of a NaCl–ethylene glycol (EG) solution at low current densities. During electrochemical tests, LPBF 316LSS specimens did not exhibit passivation in the NaCl–EG solution, and the current efficiency was high at low current densities (i < 4 A·cm−2). The reaction mechanism of ECP LPBF 316LSS in the NaCl–EG solution was clarified by observing the experimental phenomena and testing the composition of the reaction products. The ECP experiments showed that the decrease of roughness value is directly related to the charge. A small roughness value can also be obtained at low current densities (i < 2 A·cm−2). Finally, S-shaped curved holes with a diameter of Φ3 mm were efficiently polished using a flexible cathode in the NaCl–EG solution. In the case of an aperture increase rate of only about 7 %, it would only take 400 s to drastically reduce the surface roughness value Sa from 12.73 to 12.94 μm to 2.16– 2.43 μm.

Influence of build orientation and support structure on additive manufacturing of human knee replacements: a computational study.

Developing patient-specific implants has an increasing interest in the application of emerging additive manufacturing (AM) technologies. On the other hand, despite advances in total knee replacement (TKR), studies suggest that up to 20% of patients with elective TKR are dissatisfied with the outcome. By creating 3D objects from digital models, AM enables the production of patient-specific implants with complex geometries, such as those required for knee replacements. Previous studies have highlighted concerns regarding the risk of residual stresses and shape distortions in AM parts, which could lead to structural failure or other complications. This article presents a computational framework that uses CT images to create patient-specific finite element models for optimizing AM knee replacements. The workflow includes image processing in the open-source software 3DSlicer and MeshLab and AM process simulations in the commercial platform 3DEXPERIENCE. The approach is demonstrated on a distal femur replacement for a 50-year-old male patient from the open-access Natural Knee Data. The results show that build orientations have a significant impact on both shape distortions and residual stresses. Support structures have a marginal effect on residual stresses but strongly influence shape distortions, whereas conical support exhibits a maximum distortion of 18.5 mm. Future research can explore how these factors affect the functionality of AM knee replacements under in-service loading.

Vision based process monitoring in wire arc additive manufacturing (WAAM)

AbstractA stable welding process is crucial to obtain high quality parts in wire arc additive manufacturing. The complexity of the process makes it inherently unstable, which can cause various defects, resulting in poor geometric accuracy and material properties. This demands for in-process monitoring and control mechanisms to industrialize the technology. In this work, process monitoring algorithms based on welding camera image analysis are presented. A neural network for semantic segmentation of the welding wire is used to monitor the working distance as well as the horizontal position of the wire during welding and classic image processing techniques are applied to capture spatter formation. Using these algorithms, the process stability is evaluated in real time and the analysis results enable the direction independent closed-loop-control of the manufacturing process. This significantly improves geometric fidelity as well as mechanical properties of the fabricated part and allows the automated production of parts with complex deposition paths including weld bead crossings, curvatures and overhang structures.

Ultrasonic evaluation of additively manufactured components with embedded structures

In recent years, the use of ultrasonic nondestructive evaluation (NDE) for in situ and ex situ assessment of Additive Manufacturing (AM) parts has garnered interest due to its proficiency in defect detection and material characterization. This study focuses on evaluating the ability to resolve internal structures created by AM and hybrid AM using ultrasonic NDE as well as assessing the impact of microstructure heterogeneity. For this purpose, first the parameter space for laser powder bed fusion (LPBF) of SS316L is mapped in a Lumex Avance 25 system. Based on this parameter mapping, optimal process parameters are identified and sets of process parameters are selected to vary the thermal history of the samples, while minimizing the influence of porosity. Two groups of samples are printed with these parameter sets: a solid group of samples, and a group with internal structures, such that each internal structure sample has an equivalent solid sample. Measurements of ultrasonic velocity, attenuation, and backscatter amplitude are collected and compared against the intended geometry to assess the impact of non-optimal processing on the ability to resolve internal structures with ultrasound. The viability and limitations of using ultrasound to assess internal features created with AM are discussed.

A novel approach to enhance defect detection in wire arc additive manufacturing parts using radiographic testing without surface milling

A novel approach to enhance defect detection in wire arc additive manufacturing parts using radiographic testing without surface milling

A Methodology for the Rapid Qualification of Additively Manufactured Materials Based on Pore Defect Structures

Additive manufacturing (AM) can create net or near-net-shaped components while simultaneously building the material microstructure, therefore closely coupling forming the material and shaping the part in contrast to traditional manufacturing with distinction between the two processes. While there are well-heralded benefits to AM, the widespread adoption of AM in fatigue-limited applications is hindered by defects such as porosity resulting from off-nominal process conditions. The vast number of AM process parameters and conditions make it challenging to capture variability in porosity that drives fatigue design allowables during qualification. Furthermore, geometric features such as overhangs and thin walls influence local heat conductivity and thereby impact local defects and microstructure. Consequently, qualifying AM material within parts in terms of material properties is not always a straightforward task. This article presents an approach for rapid qualification of AM fatigue-limited parts and includes three main aspects: (1) seeding pore defects of specific size, distribution, and morphology into AM specimens, (2) combining non-destructive and destructive techniques for material characterization and mechanical fatigue testing, and (3) conducting microstructure-based simulations of fatigue behavior resulting from specific pore defect and microstructure combinations. The proposed approach enables simulated data to be generated to validate and/or augment experimental fatigue data sets with the intent to reduce the number of tests needed and promote a more rapid route to AM material qualification. Additionally, this work suggests a closer coupling between material qualification and part certification for determining material properties at distinct regions within an AM part.

An investigation over microstructure and HIP processing effects on wear performance of pure chromium parts fabricated by laser powder bed fusion

Chromium (Cr) and its alloys have long been valued for their exceptional properties, including corrosion resistance and high-temperature stability, rendering them indispensable in industrial applications such as chemical processing, energy production, and aerospace. However, due to the difficulties in Cr-based alloy manufacturing, there is a need for novel manufacturing methodologies. Additive Manufacturing (AM) emerges as a promising approach, particularly Laser Powder Bed Fusion (LPBF), which allows for intricate designs, reduced material waste, and simplified assembly. However, certain issues still need to be addressed, including defects like cracks and porosities in the manufactured parts. Post-processing techniques such as Hot Isostatic Pressing (HIP) have gained prominence for enhancing the material properties and quality of AM parts, including those produced using LPBF. HIP treatments are effective in eliminating internal pores, although some challenges remain, notably the presence of trapped argon and grain coarsening side effects of HIP process parameters. This study focuses on LPBF-processed pure chromium parts with crystallographic texture and investigates their properties after HIP treatment, including microstructure, hardness, and tribological performance. According to the obtained results: the HIP process reduced cracks, especially in the center region, but increased gaps in the side region. HIP also hindered grain realignment, limiting grain growth, and resulting in high HAGB density and low MUD values. Elevated HIP processing pressure negatively affected tribological performance due to increased grain size, and reduced hardness. This study, for the first time, realized the effect of HIP conditions on the microstructure and tribological performance of LPBF-processed pure Cr.

Tomographic eigenstrain reconstruction for full-field residual stress analysis in large scale additive manufacturing parts

Current experimental and numerical quantification methods are limited in their ability to full-field mapping of the unpredictable distribution of all residual stress and permanent plastic strain components in additive manufacturing parts with discontinuous processing properties. To address this limitation, a tomographic eigenstrain (inherent strain) reconstruction method, that merges eigenstrain reconstruction with diffraction strain tomography for mapping volumetric distribution of all components of eigenstrains and corresponding elastic deformations like residual stresses non-destructively using minimum amount of tomographic scans is presented through numerical experiments, and then applied to the analysis of a CM 247 LC superalloy additive manufacturing part using diffraction strain tomography data. The method reconstructs all eigenstrain and corresponding residual stress components, parallel to the build direction, aligned with the experimental data component accurately, demonstrating its potential in optimizing the performance and reliability of parts designed for high-tech industries such as aerospace. Subsequent validations using the X-ray diffraction sin2ψ and neutron diffraction strain scanning techniques confirm the method's reliability in reconstructing residual stress components parallel to the plane of powder bed that are different from the experimental data component. Furthermore, the novel findings of this study reveal a characteristic residual stress distribution pattern within additive manufacturing parts particularly those featuring rectangular shapes. Microstructural analysis also validates eigenstrain distribution in accordance with the findings on the characteristic distribution of residual stresses, highlighting the significance of this method in advancing materials research and development.

Direct voxel classification from x-ray projections for 3D pore detection applied to laser sintered parts

X-ray computed tomography (XCT) is a validated and frequently used tool to verify part geometry and to perform a non-destructive inspection of additive manufacturing parts. However, the acquisition of a large number of x-ray projections generates long inspection times. This conflicts with a high throughput of the production process and hinders the integration of XCT as an in-line quality control procedure for low-end parts. In this paper, we propose a method to obtain three-dimensional (3D) information of the internal pores from a limited view or limited angle scan. The method combines a forward projection model of a cone-beam x-ray system and a deep learning neural network to directly classify each individual voxel, based on the x-ray projections in order to avoid the reconstruction and segmentation step. Accompanying reconstruction artifacts for limited view and limited angle XCT scans are thereby reduced, while preserving 3D information of the pores, defects or inclusions inside the material. The method is validated on real x-ray projections of polymer laser sintered industrial parts and shows a significant reduction in the required x-ray projections, hence acquisition time.

Surface Defect Mitigation of Additively Manufactured Parts Using Surfactant-Mediated Electroless Nickel Coatings.

The emergence of defects during the early production phases of ferrous-alloy additively manufactured (AM) parts poses a serious threat to their versatility and adversely impacts their overall mechanical performance in industries ranging from aerospace engineering to medicine. Lack of fusion and gas entrapment during the manufacturing stages leads to increased surface roughness and porosities in the finished part. In this study, the efficacy of employing electroless nickel-boron (Ni-B) deposition to fill and level simulated AM defects was evaluated. The approach to levelling was inspired by the electrochemical deposition techniques used to fill vias in the electronics industry that (to some extent) resemble the size and shape of AM-type defects. This work investigated the use of surfactants to attenuate surface roughness in electroless nickel coatings, thereby achieving the preferential inhibition of the coating thickness on the surface and promoting the filling of the simulated defects. A cationic surfactant molecule, CTAB (cetyltrimethyl ammonium bromide), and a nonpolar surfactant, PEG (polyethylene glycol), at different concentrations were tested using a Ni-B electrolyte for the levelling study. It was found that the use of electroless Ni-B to fill simulated defects on ferrous alloys was strongly influenced by the concentration and nature of the surfactant. The highest levelling percentages were obtained for the heavy-molecular-weight PEG-mediated coatings at 1.2 g/L. The results suggest that electroless Ni-B deposition could be a novel and facile approach to filling defects in ferrous-based AM parts.

Laser shock peening and its effects and modeling on fatigue performance of additive manufactured metallic materials

Additive Manufacturing (AM) Technology has improved over the decades, and its usage has significantly increased in recent years. However, defects in AM parts have always been a major point of concern. Several post-processing techniques exist to improve the quality of AM parts, including Laser Shock Peening (LSP) which is gaining attention as an effective technique, although a costly technique. This process induces compressive residual stress in a part, subsequently improving the component's fatigue performance. LSP can also affect surface roughness, hardness, microstructure, and porosity level of fabricated components and, therefore, fatigue performance. This study reviews the effects of LSP as a post-processing technique to improve fatigue performance. In addition, LSP data from the literature are used to model the primary effect of LSP, which is compressive residual stress, in fatigue performance modeling by using several mean stress models.

Effect of geometry on the evolution of DLOFC transients in high temperature helium loop

Generation IV high-temperature gas-cooled reactors (HTGR) are designed to exhibit passive safety under all off-normal circumstances. One such scenario, known as depressurized loss of forced circulation (DLOFC), occurs after a break in the inlet/outlet header, allowing helium to leak from the reactor. Upon depressurization, the onset of natural circulation (ONC) can occur causing bulk air ingress leading to the oxidation and degradation of core components. The Transformational Challenge Reactor (TCR) has similar features to those of an HTGR, but the primary difference is the use of a more complex, additively manufactured (AM) fuel geometry. The more compact, additively manufactured, ceramic fuel elements can be conveniently produced with optimally configured channels that suppress the air ingress progress and improve thermofluidic performance. DLOFC and air ingress are experimentally studied in a scaled HTGR flow test setup. The experiments use an AM test element embedded with a distributed temperature sensor as well as a test geometry comprised of spherical pebble bed elements. With the test geometries aligned within a lowercase h-shaped tube flow setup, further data was obtained to improve the understanding of DLOFC. The thermal transient and air ingress data gathered for both test geometries are compared. The results show that the AM part delayed ONC as compared to the pebble bed test piece at higher temperatures. The distributed temperature sensor shows intra-leg circulation at higher temperature tests.

Research on an Online Monitoring Device for the Powder Laying Process of Laser Powder Bed Fusion.

Improving the quality of metal additive manufacturing parts requires online monitoring of the powder bed laying procedure during laser powder bed fusion production. In this article, a visual online monitoring tool for flaws in the powder laying process is examined, and machine vision technology is applied to LPBF manufacture. A multiscale improvement and model channel pruning optimization method based on convolutional neural networks is proposed, which makes up for the deficiencies of the defect recognition method of small-scale powder laying, reduces the redundant parameters of the model, and enhances the processing speed of the model under the premise of guaranteeing the accuracy of the model. Finally, we developed an LPBF manufacturing process laying powder defect recognition algorithm. Test experiments show the performance of the method: the minimum size of the detected defects is 0.54 mm, the accuracy rate of the feedback results is 98.63%, and the single-layer laying powder detection time is 3.516 s, which can realize the effective detection and control of common laying powder defects in the additive manufacturing process, avoids the breakage of the scraper, and ensures the safe operation of the LPBF equipment.

A prediction method of surface geometric deviation for additive manufacturing parts based on knowledge-integrated deep learning algorithm

Compared with traditional machining processes, additive manufacturing (AM) has received widespread attention in recent years because of its high degree of modeling freedom. However, due to the multiple manufacturing errors and complex physical state changes involved in the process, the geometric deviation on the AM part surface is a challenge for controlling product geometrical quality. To address this problem, data-driven machine learning (ML) techniques have been widely studied in product quality controlling. However, traditional ML greatly depends on the training sample data, and suffers the risk of violating physical mechanisms due to the lack of domain knowledge. In order to take the best advantage of domain knowledge, prior information and deep learning algorithm, this paper proposes a knowledge-integrated deep learning algorithm and constructs the geometric deviation prediction model of the AM part surface. After that, the method was verified with design of experiments. The results show that compared with the data-driven neural network (DDNN), the knowledge-integrated neural network (KINN) has fewer iterations during the training process, less sample data requirement and more accurate prediction results.

Preliminarna studija geometrije navara kod navarivanja na nerđajući čelik 316l korištenjem mig postupka zavarivanja

The application of the gas metal arc welding (GMAW) process can produce metal parts in additive manufacturing (AM) and has the advantages of fast production speed and material saving. There are some different requirements for welding beads between the AM process and the usually welded joints, so preliminary research on the 316L GMAW is conducted to find the optimal voltage, wire feed speed (WFS), and travel speed (TS). Taguchi algorithm was used firstly to obtain parameters to achieve the desired higher reinforcement, lower width, and higher aspect ratio (R/W). The voltage of 19 V, WFS of 500 cm/min, and TS of 15 cm/min were obtained as the optimal one among the samples. To further investigate the influence of the parameters and verify the result, a two-factor-three-level full factorial design was carried out, with the consideration of the interaction between factors. At last, the studied parameters were used in the AM process using a GMAW welding robot to verify the reliability.

A Framework to Assess the Impact of Recycled or Reused Metal Powder on Circular Additive Manufacturing

Additive manufacturing (AM), with its design advantages and economic and environmental benefits, facilitates the metal manufacturing industry in overcoming the changing world dynamics. Additionally, as access to raw materials has become more challenging in recent years, accompanied by an associated increase in raw material costs, researchers are investigating the effects of recycled or reused metal powder in AM on the particle properties of parts. However, the economic and environmental impacts of these effects on companies have not yet been thoroughly investigated. This study has developed a conceptual framework to delve into the impact of using recycled or reused metal powder on the printing quality and production performance of AM parts from a life cycle perspective.

Machine learning predictions of additively manufactured alloy crack susceptibilities

Additive manufacturing (AM) is revolutionizing the production of complex metal parts, offering both extreme precision and cost reduction. However, structural defects such as internal cracking can pose serious issues in AM parts, especially in safety-critical applications such as aerospace and automotive production. Empirically testing or running high-throughput simulations on a high volume of alloy formulas to quantify these cracking susceptibilities is overly costly and time-consuming. Recent developments in machine learning (ML) offer a more efficient solution to predicting the properties of a high volume of materials without the time cost needed to run mechanical simulations. In this study, we analyzed the solidification cracking susceptibilities (SCS) of potential printing alloys in safety-critical, additively manufactured parts. Based on the link between SCS and secondary alloy properties, we hypothesized that a multi-model ML pipeline would predict an alloy’s solidification cracking susceptibility at a higher accuracy than a model trained on alloy composition alone. Since predicting SCS directly from alloy composition eliminates the need for empirical measurements, our pipeline also predicted these secondary attributes directly from the original composition. We found that a pipeline using Random Forest architecture models performed at the highest accuracy and precision, outperforming the precision of a random forest model with no secondary predictions. Furthermore, the results of this study indicated a correlation between SCS and the included secondary attributes, which can be investigated in further research, as these attributes have not yet been factored into current SCS quantification methods.

Design for Additive Manufacturing Education of Process Engineering Students on an Industrial Challenge

As the use of additive manufacturing (AM) is increasing in many sectors, the need of design education for AM has gained importance. Solving the challenges of an application with the advantages of AM requires competencies in selecting a suitable AM process, identifying design opportunities and designing for AM based on an extensive knowledge in the application domain. Hence, it is necessary for all branches engineering to develop AM skills and competencies. This paper describes a lecture and practical to develop AM design competencies in chemical process engineering master students. The course teaches designing complex AM parts based on the need of an application by combining the opportunistic and restrictive aspects AM design with the domain-specific functional requirements. The design education benefits from AM’s advantages in rapid prototyping to provide feedback within the time frame of a course. In order to fulfill the above requirements, an application-oriented design course from requirements to testing, involving a simplified real industrial design problem, has been prepared. Students obtain design requirements at the beginning of the practical and have to develop a device using the advantages of AM. After completing CAD design, the designs of the students are produced with the laser based powder-bed fusion of metal (PBF-LB/M) and bath-based photopolymerisation of polymers, curing using ultraviolet light (VPP-UVL/P) methods. The manufactured designs are tested on the test bench provided by the institute. A case study of a hybrid manufactured reactor with internal condensation supports the conclusion of this paper.

Assessing the potential for additive manufacturable spare parts in the railway industry by a data-driven framework

Due to the long-life span of railway vehicles, spare part availability is a major challenge for railway companies. To avoid downtime, high procurement costs, and shortages, railway companies often stock large and costly inventories. However, additive manufacturing (AM) makes it possible to cost-effectively produce spare parts in small quantities to reduce inventory. To assess the suitability of a spare part and choose the optimal supply strategy, economic and technical data must be available in good quality. The lack of centralised information and poor data quality is one of the biggest challenges for AM potential assessment.A workflow was designed, in which system data from various databases was mapped and enhanced with technical information. In addition, the potential for additive manufacturability was evaluated based on costs and savings affected by transportation, design, manufacturing, storage compared to other supply strategies. The resulting database was then used to classify the spare parts based on specific characteristics. The defined classes provide information about the timeline for implementing the part and the type of application. A similarity check based on identified AM spare parts is used to search the entire database to identify additional potential AM parts. The result is a data-driven framework to conduct a holistic potential assessment for additive manufacturable spare parts.

Additive manufacturing of carbon nanocomposites for structural applications

Additive manufacturing (AM), also known as 3D printing, has attracted a lot of attention in science and technology in recent years. Improvement in AM processes and materials are necessary, particularly for structural and load-bearing components in mechanical applications. The addition of nano-fillers can potentially improve the mechanical properties of additively manufactured components, making them suitable for structural applications. Carbon nanofillers like graphene and carbon nanotubes are of special interest due to their exceptional properties. Several studies have already demonstrated the use of carbon nanofillers for improving the electrical properties of 3D printed components, facilitating their use in electrical and electronic applications. However, studies focusing on structural and mechanical applications have been limited, and are not widely known. This review seeks to fill this gap by exploring the effect of carbon nanofillers on the mechanical and thermal properties of AM parts and their use in structural applications. The effect of parameters like print orientation, nanofiller addition method, and nanofiller dispersion are also explored. The review points out that there are improvements in mechanical and thermal properties due to carbon nanofiller addition, which makes the AM components more suitable for structural application.