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In this paper, the effect of shot peening and cryogenic turning on the surface morphologyof the metastable austenitic stainless steel AISI 347 was investigated. In the shot peeningprocess, the coverage and the Almen intensity, which is related to the kinetic energy of thebeads, were varied. During cryogenic turning, the feed rate and the cutting edge radiuswere varied. The manufactured workpieces were characterized by X-ray diffractionregarding the phase fractions, the residual stresses and the full width at half maximum.The microhardness in the hardened surface layer was measured to compare the hardeningeffect of the processes. Furthermore, the surface topography was also characterized. Thenovelty of the research is the direct comparison of the two methods with identical work-pieces (same batch) and identical analytics. It was found that shot peening generally leadsto a more pronounced surface layer hardening, while cryogenic turning allows the hard-ening to be realized in a shorter process chain and also leads to a better surface topog-raphy. For both hardening processes it was demonstrated how the surface morphology canbe modified by adjusting the process parameter.
When machining metastable austenitic stainless steel with cryogenic cooling, a deformation-induced phase transformation from γ-austenite to α′-martensite can be realized in the workpiece subsurface. This leads to a higher microhardness and thus improved fatigue and wear resistance. A parametric and a non-parametric model were developed in order to investigate the correlation between the thermomechanical load in the workpiece subsurface and the resulting α′-martensite content. It was demonstrated that increasing passive forces and cutting forces promoted the deformation-induced phase transformation, while increasing temperatures had an inhibiting effect. The feed force had no significant influence on the α′-martensite content. With the proposed models it is now possible to estimate the α′-martensite content during cryogenic turning by means of in-situ measurement of process forces and temperatures.
In selective laser melting (SLM) the variation of process parameters significantly impacts the resulting workpiece characteristics. In this study, AISI 316L was manufactured by SLM with varying laser power, layer thickness, and hatch spacing. Contrary to most studies, the input energy density was kept constant for all variations by adjusting the scanning speed. The varied parameters were evaluated at two different input energy densities. The investigations reveal that a constant energy density with varying laser parameters results into considerable differences of the workpieces’ roughness, density, and microhardness. The density and the microhardness of the manufactured components can be improved by selecting appropriate parameters of the laser power, the layer thickness, and the hatch spacing. For this reason, the input energy density alone is no indicator for the resulting workpiece characteristics, but rather the ratio of scanning speed, layer thickness, or hatch spacing to laser power. Furthermore, it was found that the microhardness of an additively manufactured material correlates with its relative density. In the parameter study presented in this paper, relative densities of the additively manufactured workpieces of up to 99.9% were achieved.
During cryogenic turning of metastable austenitic stainless steels, a deformation-induced phase transformation from γ-austenite to α’-martensite can be realized in the workpiece subsurface, which results in a higher microhardness as well as in improved fatigue strength and wear resistance. The α’-martensite content and resulting workpiece properties strongly depend on the process parameters and the resulting thermomechanical load during cryogenic turning. In order to achieve specific workpiece properties, extensive knowledge about this correlation is required. Parametric models, based on physical correlations, are only partly able to predict the resulting properties due to limited knowledge on the complex interactions between stress, strain, temperature, and the resulting kinematics of deformation-induced phase transformation. Machine learning algorithms can be used to detect this kind of knowledge in data sets. Therefore, the goal of this paper is to evaluate and compare the applicability of three machine learning methods (support vector regression, random forest regression, and artificial neural network) to derive models that support the prediction of workpiece properties based on thermomechanical loads. For this purpose, workpiece property data and respective process forces and temperatures are used as training and testing data. After training the models with 55 data samples, the support vector regression model showed the highest prediction accuracy.
As additive manufacturing offers only low surface quality, a subsequent machining of functional and highly loaded areas is required. Thus, a sound knowledge of the interrelation between the additive and subtractive manufacturing process as well as the resulting mechanical properties is indispensable. In this work, specimens were manufactured by using laser-based powder bed fusion (L-PBF) with substantially different sets of process parameters as well as subsequent grinding (G) or milling (M). Despite the substantially different surface topographies, the fatigue tests revealed only a slight influence of the subtractive manufacturing on the fatigue behavior, whereas the different laser-based powder bed fusion process parameters led to pronounced changes in fatigue strength. In contrast, a significant influence of subtractive finishing on the fatigue properties of the defect-free continuously cast (CC) reference specimens was observed. This can be explained by a dominating influence of process-induced defects in laser-based powder bed fusion material, which overruled the influence of surface machining. However, although both laser-based powder bed fusion parameter sets resulted in substantial defects, one set yielded similar fatigue strength compared to continuously cast specimens.