The Concept of Intelligent Support for Laser Additive Manufacturing Process Engineer

A general concept of software and information support for laser-based additive manufacturing of metal parts from powder compositions is proposed. It is based on an ontological two-level approach to the formation of knowledge about the processes of laser additive manufacturing. Under this approach, the ontology is clearly separated from the knowledge base. So subject-matter specialists can create and develop knowledge in the representation that they understand. The conceptual architecture of the decision support software for laser-based additive manufacturing processes is presented. Its information and software components are described. Information components are ontologies, databases of laserbased additive manufacturing system components, databases of materials for additive manufacturing, knowledge base and case database. The knowledge base contains formalized information on the settings of laser-based additive manufacturing modes that ensure compliance of the obtained metal parts with the requirements of the current industry-specific guidelines (regulatory documents for this type of parts). The case database contains a structured description of the protocols for using laser technological equipment for additive manufacturing of metal parts from powder compositions. Software components are editors for creating and maintaining data and knowledge bases (controlled by the corresponding ontologies), decision support system based on both, knowledge and cases, and tool for cases structuring. There are also external tools for mathematical modelling of directed energy deposition physicochemical processes. When making decisions a hybrid approach that combines knowledge engineering methods and case-based search by analogy is proposed. The specialty of the approach is the continuous knowledge base update due to its improvement by experts and due to its verification in the process of accumulating cases.

Microtexture in additively manufactured Ti-6Al-4V fabricated using directed energy deposition

Ultrasound-aided laser additive manufacturing achieves synergistic optimization of microstructure and properties in high-temperature titanium alloy

Recent advances in additive manufacturing (AM) have attracted significant industrial interest. Initially, AM was mainly associated with the fabrication of prototypes, but the AM advances together with the broadening range of available materials, especially for producing metallic parts, have broaden the application areas and now the technology can be used for manufacturing functional parts, too. Especially, the AM technologies enable the creation of complex and topologically optimised geometries with internal cavities that were impossible to produce with traditional manufacturing processes. However, the tight geometrical tolerances along with the strict surface integrity requirements in aerospace, biomedical and automotive industries are not achievable in most cases with standalone AM technologies. Therefore, AM parts need extensive post-processing to ensure that their surface and dimensional requirements together with their respective mechanical properties are met. In this context, it is not surprising that the integration of AM with post-processing technologies into single and multi set-up processing solutions, commonly referred to as hybrid AM, has emerged as a very attractive proposition for industry while attracting a significant R&D interest. This paper reviews the current research and technology advances associated with the hybrid AM solutions. The special focus is on hybrid AM solutions that combine the capabilities of laser-based AM for processing powders with the necessary post-process technologies for producing metal parts with required accuracy, surface integrity and material properties. Commercially available hybrid AM systems that integrate laser-based AM with post-processing technologies are also reviewed together with their key application areas. Finally, the main challenges and open issues in broadening the industrial use of hybrid AM solutions are discussed.

Solid oxide fuel cells (SOFCs) are electrochemical conversion devices which directly convert chemical energy of the fuel into electrical energy with high efficiency. SOFC comprises of an anode, electrolyte and cathode layer that are fabricated individually upon each other, usually one at a time. Additive manufacturing (AM) is a new fabrication technique that utilizes a layer by layer fabrication technique, which can potentially be suitable for use in SOFC, since it is also building different materials in a layer-wise manner. Furthermore, the use of laser-based AM techniques can eliminate the issue of long sintering hours by directly densifying the materials using laser as the heat source. In addition, SOFCs possessing newer designs can be fabricated with ease using this technique, gradient-structure electrodes can also be created either by design or varying the laser parameter and material composition. Therefore, this thesis aims to develop a process as well as study the feasibility for fabricating SOFC using laser-based AM techniques. First, a step-by-step process was conducted to identify significant factors affecting the building of Nickel metal support structures using Selective Laser Melting (SLM) process. Laser power, scanning speed and hatch spacing were found to be the dominant factors affecting the printability of the parts. Next, a porous structure was designed and printed which acted as a metal support for SOFC. A functional cell was further built upon the metal support and the electrochemical performance was investigated. Although a low open circuit voltage (OCV) of 0.53 V was obtained with a maximum power density of 11.0 mW/cm2 from the cell, it demonstrated potential of this technique as a new fabrication process. In order to realize the fabrication of the entire SOFC by laser-based AM, the influence of laser on SOFC materials were conducted. The effect of two different laser sources, Nd:YAG fibre laser and CO2 laser was studied on NiO-YSZ anodic material, which showed that CO2 xvi laser was better absorbed by the oxide material. In addition, dendrite features were formed on the surface, which were influenced by the rapid solidification process and could be modified by adjusting laser processing parameters. Although the laser-modified SOFC did not perform as well as the conventional one, it was found that significant improvement was made on the charge transfer kinetics. Lastly, the influence of laser on YSZ electrolyte surface showed that the polarization resistance of the cell was reduced due to better oxygen adsorption/dissociation process. These experiments demonstrated that laser-based AM is a viable fabrication tool for SOFC.

Smart-Alloying enables local modification of the chemical composition of metallic components within additive manufacturing processes. This new method represents an innovative process for localized grading of metallic materials in laser-based additive manufacturing. Smart-Alloying involves applying nanoparticulate alloying elements onto the surfaces of additive manufactured components in precise doses using suspensions. The method is shown here using laser powder bed fusion as an example. By coating the base material powder and exposing it to the laser source, localized modifications in chemical composition and altered properties can be achieved. Using a chromium-rich ferritic stainless steel, significant alterations in the fundamental microstructure are attained through localized re-alloying of carbon. Investigation utilizing focused ion beam-scanning electron microscope elucidates the evolution of increasing carbon content within the re-alloyed area. Next to the formation of chromium-rich carbides and ultrafine regions of needle-like martensite, retained austenite could be made visible. Transmission electron microscopy investigations proved these findings and revealed mainly α-ferritic microstructure with small γ-austenitic grains as well as the presence of chromium-rich carbides on the main grain boundaries. The completely renewed microstructure is then further modified using secondary exposures of the laser source. Under the influence of the subsequent melting process, there is an increasing distribution of the re-alloyed carbon, resulting in a completely new structure of a line-shaped duplex microstructure. The integration of secondary exposure introduces a completely novel aspect, offering the potential to merge localized grading via Smart-Alloying with targeted applications of laser parameters.

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.

The presence of anomalies in components generated using laser-based Additive Manufacturing (AM) processes greatly affects mechanical properties, making anomaly detection and prevention significant for the improvement of AM. Melt pool thermal imaging of laser-based AM processes such as Direct Energy Deposition (DED) provide rich data from which anomalies can be detected. Although through feature-based learning methods (e.g., support vector machine and k-nearest neighbor) or end-to-end deep learning models (e.g., self organizing map) the anomalies can be identified, the sparsity of defective instances and lack of interpretable analysis, together with high dimensionality and velocity of melt pool images further increase the difficulty of anomaly detection. We propose implementing Neural Vector Quantized Variational Autoencoder (Neural-VAE) to analyze thermal images from a DED process to determine where and when anomalies occur, indicating a flaw in the generated component. A Vector Quantized VAE is designed to represent high-dimensional images as low-dimensional vectors. Then, hyperdimensional computing is introduced to leverage the low-dimensional representation of image data and determine defective and non-defective images. Our proposed methodology can detect anomalies with an accuracy, sensitivity, specificity, and f-score of 99.2%, 99.6%, 98.8%, and 99.2%, respectively, while successfully generating anomalous images.

The paper presents the research results of laser-based additive process Directed Energy Deposition (DED) aimed at improving the mechanical properties of thin-walled parts made of sheet AMg3 aluminum alloy, by creating on the surface of the lattice structure of powder material based on aluminum AK4-1. Taking into account the influence of a number of key technological parameters for laser powder-based additive processes of DED type on the properties of the final product and the lack of a proper level of intelligent support for such processes, the concept of cloud-based knowledge portal for intelligent support of decision-making by the equipment operator has been proposed. The concept is based on a two-level ontological approach to the formation of data- and knowledge bases of laser powder-based DED additive manufacturing. A brief description of the information bases and tools for the decision support in laser-based additive manufacturing is given. Mathematical modeling of the distribution process of thermal fields formed by interaction of laser radiation with the processed material is performed. Software that implements numerical calculations for the presented mathematical model of the DED process was created. A remote interaction scheme of intelligent decision support service for laser-based additive manufacturing processes with the created software has been proposed. The obtained results of numerical calculations in the Wolfram Mathematica technical computing system together with the expert information that is accumulated in the knowledge portal of the IACPaaS cloud platform are promising for training and decision-making by users of laser technological equipment.KeywordsLaser technologyAdditive manufacturingDirected energy depositionDecision support systemLaser powder cladding

Chapter 14 - Laser aided metal additive manufacturing and postprocessing: a comprehensive review

Additive manufactured Mg-Gd-Y-Zr alloys: Effects of Gd content on microstructure evolution and mechanical properties

Effects of laser printing parameters on molten pool formation, microstructure evolution and mechanical properties of laser directed energy deposition of difficult-to-process tungsten heavy-alloy

Microstructural and mechanical characterization of stainless steel 420 and Inconel 718 multi-material structures fabricated using laser directed energy deposition

The ever-growing interest in additive manufacturing (AM) is evidenced by its extensive utilisation to manufacture a broad spectrum of products across a range of industries such as defence, medical, aerospace, automotive, and electronics. Today, most laser-based AM is carried out by employing continuous-wave (CW) and long-pulsed lasers. The CW and long-pulsed lasers have the downside in that the thermal energy imparted by the laser diffuses around the irradiated spot and often leads to the creation of heat-affected zones (HAZs). Heat-affected zones may degrade the material strength by producing micro-cracks, porous structures and residual stresses. To address these issues, currently, attempts are being made to employ ultrafast laser sources, such as femtosecond (fs) lasers, in AM processes. Femtosecond lasers with pulse durations in the order of 10−15 s limit the destructive laser–material interaction and, thus, minimise the probability of the HAZs. This review summarises the current advancements in the field of femtosecond laser-based AM of metals and alloys. It also reports on the comparison of CW laser, nanosecond (ns)/picosecond (ps) lasers with fs laser-based AM in the context of heat-affected zones, substrate damage, microstructural changes and thermomechanical properties. To shed light on the principal mechanisms ruling the manufacturing processes, numerical predictions are discussed and compared with the experimental results. To the best of the authors’ knowledge, this review is the first of its kind to encompass the current status, challenges and opportunities of employing fs lasers in additive manufacturing.

Microstructure and mechanical behavior of additively manufactured CoCrFeMnNi high-entropy alloys: Laser directed energy deposition versus powder bed fusion