1,721,033 research outputs found
Influence of the atomization medium on the properties of stainless steel SLM parts
Full text linkThe importance of powders on the final part properties in selective laser melting (SLM) process is well-known. At the moment, gas atomization is used to produce powders for the SLM process. However, there is a growing interest in investigating the use of the less expensive water atomization process. In this work, water and gas atomized powders were used as feedstock to produce specimens with an industrial SLM system. First, the influence of particle size distribution and layer thickness on the final part density was studied by varying process parameters by means of a factorial design. The results showed that by using a particle size distribution in the range 15–60 μm the difference in part density between water and gas atomized is negligible even using the same combination of parameters. The tensile properties of GA and WA powder are comparable: respectively 599 and 601 MPa for the ultimate tensile strength, 18% and 20% for the elongation
A comparison of neural network and control charting for monitoring profiles in manufacturing processes
No full textThe issue of monitoring profiles has been defined as being one of the most promising areas of research in statistical process control. One immediate difficulty is how to characterize a profile. As a matter of fact, the identification of a statistical model may become more difficult than expected, thus representing an obstacle to the introduction of profile monitoring in actual applications. For example, when a profile represents the physical dimensions of a machined surface, as it results in manufacturing applications, measurements data often exhibit complex spatial correlation.
The aim of this work is to explore a different approach for monitoring profiles, which uses the Adaptive Resonance Theory (ART) neural network. The implementation of this neural network is based on a set of profiles which are representative of the process in its natural, or in-control, state.
Throughout the paper, a real case study related to profiles data obtained by a common machining process is used. With reference to the Phase II of profile monitoring, performance of the proposed approach are compared to those of multivariate control charting of the parameters vector. Although the proposed neural network does not produce always outperforming results, it presents comparable performance in several cases. The main advantage presented by the approach is that the model of profile data is “autonomously” derived by the neural network, without requiring any further intervention by the quality practitioner. This feature may create an important bridge between profile monitoring and quality monitoring of several specifications in actual applications
Fast optimisation procedure for the selection of L-PBF parameters based on utility function
Full text linkL-PBF is an additive manufacturing process forming parts with complex geometries by adding material layer by layer. The selection of the process parameters in L-PBF has a significant impact on the mechanical properties of the printed parts. Scan speed, laser power, and hatch distance are among the most influential process parameters in L-PBF because, depending on their combination, different solidification mechanisms take place. However, the procedure for selecting these parameters can be expensive from an experimental point of view. Therefore, it is necessary to identify simplified models that allow fast and reliable optimization of the parameters in L-PBF. Furthermore, the choice of parameters cannot be based exclusively on qualitative aspects but must also consider the productivity of the process to obtain a satisfactory compromise. Increasing productivity leads to the formation of lack of fusion porosity which should be avoided. This paper proposes a procedure for selecting parameters based on a semi-analytical thermal model, which, together with a geometric-based defect model, allows identifying an optimality region where good solidification and productivity are considered. The optimization is carried using a properly defined utility function. The procedure is validated through the production of AISI 316L specimens using an industrial L-PBF system
Improvement of SLM Build Rate of A357 alloy by optimizing Fluence
Full text linkSelective Laser Melting is one of the most widely used Additive manufacturing technologies for producing metal parts. Among the many advantages of SLM, the low build rate is still one of the most difficult challenges to address. The Build Rate (BR) of SLM depends on many factors, one of them is the scanning time which is directly related to Fluence. In this work, a procedure to select a process parameter combination with an increased BR is presented and validated experimentally. A357 alloy was selected to print tensile specimens using different combinations of process parameters resulting in the same value of Fluence, and four levels of Fluence are selected for the analysis. Despite the wide range of Fluence considered, 85–140 J/mm3, the mechanical properties did not change. The combination of parameters ensuring the highest productivity was selected for the validation run. Experimental data were used to estimate regression equations able to predict the mechanical properties of the high-productivity condition. Density and UTS of the validation samples were accurately predicted by the regression equations, and they were consistent with the base material properties. The procedure allowed us to identify a combination of parameters ensuring an increase in productivity of 26 % compared to the standard condition
On the Lack of fusion porosity in L-PBF processes
No full textLack of fusion porosity is a typical defect of laser powder bed fusion processes generated by a wrong selection of process parameters that do not ensure a proper overlapping of melt pools. Melt pool dimensions and lack of fusion porosity can be predicted using analytical models that use as inputs material properties and process parameters. None of these models considers the variability of the melt pool dimensions in predicting the lack of fusion porosity. In this work, Monte Carlo simulations are used to determine the influence of the variability of the melts pools dimensions on the selection of process parameters
XCT characterization and mechanical properties of Ti6Al4V produced by L-PBF using the same volumetric energy density
No full textVolumetric energy density (VED) is an important synthetic index for predicting part density in additive manufacturing and its correct selection can significantly minimize internal porosity. This study investigates the influence of varying process parameters, all with the same nominal VED, on the porosity structure and mechanical performance of Ti6Al4V specimens produced via laser powder bed fusion (L-PBF). Tensile and fatigue samples were manufactured using different parameter combinations to assess their effects on porosity and performance. X-ray computed tomography (XCT) was conducted on the gauge sections of the specimens to acquire porosity data, which was subsequently analyzed using functional data analysis (FDA). All parameter combinations yielded a calculated density exceeding 99%. Functional analysis of variance (F-ANOVA) was employed to test the hypothesis of equal means between groups on the cumulative distributions of equivalent spherical diameter (ESD) and radial position. The results indicate that different parameter combinations lead to the formation of distinct porosity structures. Despite these variations, tensile and fatigue tests demonstrated comparable behavior across all the groups. The observed porosity structures did not significantly impact the macroscopic properties, suggesting that substantial variations in process parameters may be introduced to enhance machine productivity without compromising high part density and mechanical performance
Reliability of FMS performance measures estimated via Operational Analysis: the role of part-mix characteristics
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