Influence of Machine Type and Powder Batch During Laser-Based Powder Bed Fusion (L-PBF) of AISI 316L

Manufacturing machine parts using additive manufacturing (AM) has several benefits. AM can be used for manufacturing metal parts with a high degree of geometric complexity, with significant reduction in cost, time, and energy consumption. Additionally, it can be beneficial in refurbishing and replacement of parts by manufacturing it in a remote location. AM can be used as a platform for producing novel high-performance alloys for several industrial applications and enables us to create graded structures (at microstructural level or composition) using Laser Based powder bed fusion (L-PBF) technology. Common AM systems have been offered as a black-box solution where customers print parts at the recommended settings and are advised to change little to nothing. However, the standard parameters are not applicable to all types of geometries. When complex structures like gyroid, which is a lattice structure, help to maximize stiffness and reduce the weight of the given volume there is need of optimization of key process variables such as laser power, scanning strategy, layer thickness and scan speed. L-PBF technology has emerged as a leading candidate for manufacturing mission-critical components. It requires parameters optimization towards obtaining desired output property to weight ratio. This study focuses on manufacturing of different type of lattice structures (i.e. Body diagonal, Diamond; Dode thick, Trun-octa dense and Gyroid) and investigate the most suitable structure which will demonstrate better strength to weight ratio based on the mechanical testing. Optimization of process parameters for lattice structures was out of scope in this study. This Current work also is targeted towards assessment of structural parameters on properties of AM produced using Ti6Al4V alloy by L-PBF with parameters using fiber laser of 400 W (max. energy of laser beam), spot size 100μm, a laser power of 280 W, a scanning speed of 1,300 m/s, a hatch distance of 150 μm, and a layer thickness of 60 μm under Argon atmosphere. The obtained structural variations showed compression strength to weight ratio of 7.89 to 12.45.

Additive Manufacturing (AM) processes intertwine aspects of many different engineering-related disciplines, such as material metrology, design, in-situ and off-line measurements, and controls. Due to the increasing complexity of AM systems and processes, data cannot be shared among heterogeneous systems because of a lack of a common vocabulary and data interoperability methods. This paper aims to address insufficiencies in laser-based Powder Bed Fusion (PBF), a specific AM process, data representations to improve data management and reuse in PBF. Our approach is to formally decompose the processes and align PBF process-specifics with information elements as fundamental requirements for representing process-related data. The paper defines the organization and flow of process information. After modeling selected PBF processes and sub-processes as activities, we discuss requirements for the development of more advanced process data models that provide common terminology and process knowledge for managing data from various stages in AM.

A conceptual framework for layerwise energy prediction in laser-based powder bed fusion process using machine learning

Implementation of Advanced Laser Control Strategies for Powder Bed Fusion Systems

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. Lastly this review contributes to the understanding of the PBAM process and is a valuable resource for potential patent applications related to additive manufacturing areas..

In automotive applications, high-strength low alloy (HSLA) steels are providing enhanced material properties, due to the high yield strength, less fragility, and lower weight, which also promotes lower fuel consumption. HSLA steels contain a small amount of carbon (under 0.2%) and also contain small amounts of alloying elements such as copper, nickel, niobium, vanadium, chromium, molybdenum and zirconium. This eliminates the toughness reducing effect of a pearlitic volume fraction, yet maintains and increases the material's strength by refining the grain size. Therefore, HSLA steels are used for structures intended to handle large amounts of stress or that need a good strength-to-weight ratio. Traditional manufacturing methods for steel (e.g., blast furnace or electric arc furnace methods) fail to provide the necessary optimization for best output in terms of performance and application. The quality of molten steel is greatly affected by scrap steel. The smelting period is longer, and the power consumption is large. In addition, these processes allow more impurities into the molten steel, which compromises the quality of the final product. However, additive manufacturing (AM) can can be used to fabricate HSLA steel components without these drawbacks. In addition, AM can provide a relatively faster process with low manufacturing costs, in comparison with traditional processing methods. Although AM has several benefits, studies shows that AM processes can result in HSLA steels with microstructural defects, such as non-homogeneity, internal cavities, inclusions, and impurities. Consequently, these microstructural features have a significant affect on the corrosion properties of AM parts as corrosion tends to initiate in defective regions. The AM processing parameters directly impact the microstructure of the fabricated part. Hence, it is important to understand the relationship between the AM processing parameters on resulting microstructural features. The present work evaluated the electrochemical corrosion properties of HSLA steels fabricated by AM via selective laser melting (SLM) under different processing conditions. The goal of the work was to investigate the role of microstructure on electrochemical corrosion in high-strength low alloy steels due to SLM processing conditions.Two types of HSLA steels, Fe 367 and Fe 398, were fabricated by AM via (SLM). Fe 398 differs from Fe 367 as it contains molybdenum and nickel. Each type was fabricated at a laser power of 100W, and scan speed of 600, 800, 100 and 1200 mm/s. The samples were exposed to 3.5% NaCl for 15 days, individually. To acquire the electrochemical corrosion data and to perform analysis, a Gamry reference 600 potentiostat/galvanostat was used. The electrochemical data were obtained by collecting the impedance spectra, and measuring the polarization resistance every 5 days. On day 15, cyclic polarization data was collected for each sample. These measurements helped to identify the localized corrosion as well as provide detailed information about the corrosion properties, such as passive layer growth, initiation and secession of pitting, and corrosion rate. The topography of the materials was observed by SEM before and after the corrosion tests. Energy Dispersive Spectroscopy (EDS) was performed on the samples to identify the chemical elements. The surface roughness was observed through confocal microscope.The Confocal and SEM images showed the change in surface microstructure and topographical properties of the samples before and after the corrosion testing. The difference in laser power and scan speed affected the microstructure and corrosion properties of the materials. As the samples were manufactured at same laser power, the scan speed was responsible for different topographical and corrosion behavior. Samples manufactured at lower scan speed showed less flaws on the surface of the materials than the samples manufactured at higher scan speed. Therefore, both Fe 398 and Fe 367 showed better surface topography at 600 mm/s and 800 mm/s. They also demonstrated significantly lower corrosion rate than the other samples. EDS identified chemical oxides and chlorides which were formed during the corrosion test. Overall, this work demonstrated that Fe 398 shows better microstructural and corrosion properties than Fe 367 at lower scan speed.

Numerical analysis of the effect of processing parameters on the microstructure of stainless steel 316L manufactured by laser-based powder bed fusion

In laser-based powder bed fusion (L-PBF), the volumetric energy density (VED) controlled by laser power (LP) and scan speed (SS) significantly affects the desired surface quality. Any variations in the LP and SS on the VED notably affect the surface quality due to changes in laser powder interactions. Therefore, the present study concentrated on the effect of various combinations of LP and SS, i.e. low LP (160 W) with high SS (1250 mm/s), high LP (320 W) with low SS (410 mm/s), low LP (160 W) with low SS (550 mm/s) and high LP (320 W) with high SS (1100 mm/s). The wide range of varying VEDs was chosen between 29 and 177 J/mm3. The results showed the VED range between 66 and 90 J/mm3 exhibited minimum surface roughness parameters of 0.51 < Ra < 0.52 µm, 0.63 < Rq < 0.64 µm, and 2.53 < Rz < 2.79 µm. The specific impact of LP and SS combination between 160 and 320 W with 400–1250 mm/s by maintaining a constant VED of 66 J/mm3 was also investigated. At a constant VED of 66 J/mm3, the combination of 280 W and 960 mm/s shows minimum roughness parameters of Ra = 0.527 µm, Rq = 0.645 µm, and Rz = 2.446 µm. The results showed that the combination of low LP with low SS, high LP with high SS, low LP with high SS, and high LP with low SS, increases surface roughness.

Additive manufacturing (AM) has the potential to revolutionize discrete part manufacturing, but improvements in processing of metallic materials are necessary before AM will see widespread adoption. A better understanding of AM processes, resulting from physics-based modeling as well as direct process metrology, will form the basis for these improvements. Infrared (IR) thermography of AM processes can provide direct process metrology, as well as data necessary for the verification of physics-based models. We review selected works examining how IR thermography was implemented and used in various powder-bed AM processes. This previous work, as well as significant experience at the National Institute of Standards and Technology in temperature measurement and IR thermography for machining processes, shapes our own research in AM process metrology with IR thermography. We discuss our experimental design, as well as plans for future IR measurements of a laser-based powder bed fusion AM process.

An experimental investigation of hybrid manufactured SLM based Al-Si10-Mg alloy under mist cooling conditions

Development of strength-hardness relationships in additively manufactured titanium alloys

Buckling phenomena in AM lattice strut elements: A design tool applied to Ti-6Al-4V LB-PBF

Wire arc additive manufacturing (WAAM) and laser-based powder bed fusion (L-PBF) are additive manufacturing (AM) processes that allow the manufacturing of complex part geometries. The manufacturing of AM parts does not result in high-quality functional surfaces; therefore, postprocessing such as milling is usually required. For L-PBF parts, the support structures and, for WAAM parts, the undulating surface are usually removed after AM processes. These two application-related cases are investigated in this work, with the conclusion that support structure milling and the milling of the surface of WAAM parts lead to the dimensionally increased wear of milling tools in comparison to milling of solid material.

Additive manufacturing allows for the fabrication of large-sized metallic glasses with complex geometries, which overcomes the size limitation due to limited glass-forming ability. To investigate the effect of synthesis parameters on the Mg-based metallic glasses, Mg65Cu20Zn5Y10 was fabricated by laser-based powder bed fusion under different scanning speeds and laser powers. For high energy density, the samples showed severe crystallization and macrocracks, while for low energy density, the samples contained pore defects and unfused powders. Three-dimensionally printed samples were used for the compression test, and the mechanical properties were analyzed by Weibull statistics. Our work identifies proper parameters for 3D printing Mg-based metallic glasses, which provide a necessary, fundamental basis for the fabrication of 3D-printed Mg-based metallic glass materials with improved performance.

The journey of production tools in cold working, hot working, and injection molding from rapid tooling to additive manufacturing (AM) by laser-based powder bed fusion (L-PBF) is described. The current machines and their configurations, tool steel powder materials and their properties, and the L-PBF process parameters for these materials are specified. Examples of production tools designed for and made by L-PBF are described. Efficient design, i.e., high tooling efficiency and performance in operation, should be the primary target in tool design. Topology and lattice structure optimization provide additional benefits. Using efficient design, L-PBF exhibits the greatest potential for tooling in hot working and injection molding. L-PBF yields high tooling costs, but competitive total costs in hot working and injection molding. Larger object sizes that can be made by L-PBF, a larger number of powder metals that are designed for different tooling applications, lower feedstock and L-PBF processing costs, further L-PBF productivity improvement, improved surface roughness through L-PBF, and secured quality are some of the targets for the research and development in the future. A system view, e.g., plants with a high degree of automation and eventually with cyber-physically controlled smart L-PBF inclusive manufacturing systems, is also of great significance.