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The dimensional accuracy of additively manufactured (3D printed) medical models can be affected by various parameters. Although different methods are used to evaluate the accuracy of additively manufactured models, this study focused on the investigation of the dimensional accuracy of the medical model based the combination of reverse engineering (RE) and additive manufacturing (AM) technologies. Human femur bone was constructed from CT images and manufactured, using Fortus 450mc Industrial material extrusion 3D Printer. The additive manufactured femur bone was subsequently 3D scanned using three distinct non-contact 3D scanners. MeshLab was used for mesh analysis, while VX Elements was used for post-processing of the point cloud. A combination of the VX Inspect environment and MeshLab was used to evaluate the scanning performance. The deviation of the 3D scanned 3D models from the reference mesh was determined using relative metrics and absolute measurements. The scanners reported deviations ranging from -0.375 mm to 0.388 mm, resulting in a total range of approximately 0.763 mm with average root mean square (RMS) deviation of 0.22 mm. The results indicate that the additively manufactured model, as measured by 3D scanning, has a mean deviation with an average range of approximately 0.46 mm and an average mean value of around 0.16 mm.
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Thin-walled structures are a significant and growing portion of engineering construction, with a wide range of applications, including storage vessels, industrial buildings, warehouses, aircraft, automobiles, bridges, ships, and oil rigs. Thin-walled components with minimum thickness without compromising strength and other quality characteristics are the desire of modern industry. Reducing wall thickness not only aids in lowering the cost of production. It also improves the effectiveness of engineering systems, resulting in lower fuel consumption and lower emissions of hazardous gases to the environment. Nowadays, even though thin-walled parts are demanded, the constraints of the production process, quality, and reliability are the concerns of current research and development. The ability to produce parts with intricate geometries and tight dimensional tolerances are important criteria for advanced manufacturing processes. In the early days of society, investment casting was used to produce jewelry, weapons, and statues. In modern industry, investment casting is still used to produce thin-walled and intricate parts such as turbine blades. The current advancements in SLM, which has the capacity to produce thin-walled and intricate parts, have recently attracted attention due to several benefits, such as the supreme degree of design freedom and the viability of tool-free production directly from CAD data. However, the current technological applications of SLM and investment casting are crucial for producing parts at the desired quality and reliability. This review article focuses on comparative studies of SLM and investment casting at the current technology level. The basis of comparison via systematic approach is mechanical characterization; quality in terms of porosity, microstructure, surface roughness and dimensional accuracy; and residual stress. Therefore, the latest open scientific sources published are considered to obtain sufficient literature coverage. Better tensile strength and fine microstructure are found in SLM, while better surface quality, fatigue load resistance, ductility, and residual stress are found in investment casting. The research gap for further investigation is indicated.
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Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.
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Aluminum alloy is the second most abundant metal on Earth, known for its wide range of utilization in commercial goods due to its heat capacity and tensile strength. This study examines the effect of nose radius on the turning process. Further, it explores the implications of cutting parameters such as the cutting speed, the rate of feed, the cutting depth, and the nose radius of the tool. The trials were carried out with an Al 6061 workpiece and an Al2O3-coated carbide tool as the cutter, utilizing the response surface methodology. A mathematical model was developed to investigate the performance characteristics of the turning operation using the analysis of variance method. The multi-response desirability function analysis combines individual desirability values to create a composite desirability value. The ideal parameter levels were determined using the composite desirability value, and the significant influence of parameters was assessed. The obtained optimum surface roughness and temperature parameters are at a cutting speed of 116.37 m/min, a rate of feed of 0.408 mm/rev, a cutting depth of 0.538 mm, and a tool nose radius of 0.20 mm. The related ideal surface roughness and temperature values are 0.374 µm and 27.439 °C. The optimal overall desirability value is 0.829, close to the target response.
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The Markforged Metal X (MfMX) printing machine (Markforged Inc., Massachusetts, USA) is one of the latest introduced additive manufacturing (AM) devices. It is getting popular because of its safety, simplicity, and ability to utilize various types of powders/filaments for printing. Despite this, only a few papers have so far reported the various properties and performances of the components fabricated by the MfMX printer. In this study, the microstructure and mechanical properties of MfMX-fabricated 17-4 stainless steel (ss) in the as-printed and heat-treated conditions were investigated. XRD and microscopy analyses revealed a dominant martensitic microstructure with some retained austenite phase. The microstructure is generally characterized by patterned voids that were unfilled due to a lack of fusion between the adjacent filaments. Disregarding these defects (voids), the porosity of the dense region was less than 4%. Depending on the heat treatment conditions, the hardness and tensile strength were enhanced by 17-28% and 21-27%, respectively. However, the tensile strength analyzed in this work was low compared with some previous reports for L-PBF-fabricated 17-4 ss. In contrast, the hardness of the as-printed (331 ± 28 HV) and heat-treated samples under the H900 condition (417 ± 29 HV) were comparable with (and even better than) some reports in the literature, despite the low material density. The results generally indicated that the Markforged printer is a promising technology when the printing processes are fully developed and optimized.
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This paper presents the results of predictions of springback of cold-rolled anisotropic steel sheets using an approach based on a multilayer perceptron-based artificial neural network (ANN) coupled with a genetic algorithm (GA). A GA was used to optimise the number of input parameters of the multilayer perceptron that was trained using different algorithms. In the investigations, the mechanical parameters of sheet material determined in uniaxial tensile tests were used as input parameters to train the ANN. The springback coefficient, determined experimentally in the V-die air bending test, was used as an output variable. It was found that specimens cut along the rolling direction exhibit higher values of springback coefficient than specimens cut transverse to the rolling direction. An increase in the bending angle leads to an increase in the springback coefficient. A GA-based analysis has shown that Young's modulus and ultimate tensile stress are variables having no significant effect on the coefficient of springback. Multilayer perceptrons trained by back propagation, conjugate gradients and Lavenberg-Marquardt algorithms definitely favour punch bend depth under load as the most important variables affecting the springback coefficient.
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Three-dimensional finite element-based numerical analysis of Vickers indenter hardness test was conducted to investigate the effect of frictional conditions and material anisotropy on indentation results of deep drawing quality steel sheets. The strain hardening properties and Lankford's coefficient were determined through the uniaxial tensile tests. The numerical computations were carried out using ABAQUS nonlinear finite element (FE) analysis software. Numerical simulations taken into account anisotropy of material described by Hill (1948) yield a criterion. The stress and strain distributions and loading-unloading characteristics were considered to study the response of the material. It was found that the hardness values seemed to be influenced by the value of the friction coefficient due to the pile-up phenomenon observed. The increasing of the friction coefficient led to a decrease of the pile-up value. Moreover, the width of the pile-ups differed from each other in the two perpendicular directions of measurement. Frictional conditions did not significantly affect the maximum force and the character of load-displacement curves. Frictional regime between the indenter and workpiece caused that the region of maximum residual stresses to be located in the subsurface.
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Fused-deposition modeling (FDM), one of the additive manufacturing (AM) technologies, is an advanced digital manufacturing technique that produces parts by heating, extruding and depositing filaments of thermoplastic polymers. The properties of FDM-produced parts apparently depend on the processing parameters. These processing parameters have conflicting advantages that need to be investigated. This article focuses on an investigation into the effect of these parameters on the flexural properties of FDM-produced parts. The investigation is carried out on high-performance ULTEM 9085 material, as this material is relatively new and has potential application in the aerospace, military and automotive industries. Five parameters: air gap, raster width, raster angle, contour number, and contour width, with a full factorial design of the experiment, are considered for the investigation. From the investigation, it is revealed that raster angle and raster width have the greatest effect on the flexural properties of the material. The optimal levels of the process parameters achieved are: air gap of 0.000 mm, raster width of 0.7814 mm, raster angle of 0°, contour number of 5, and contour width of 0.7814 mm, leading to a flexural strength of 127 MPa, a flexural modulus of 2400 MPa, and 0.081 flexural strain.