An Investigation of the Anisotropic Mechanical Properties of Additive-Manufactured 316L SS with SLM

Wang, Haibo; Jiang, Peng; Yang, Guangyong; Yan, Yu

doi:10.3390/ma17092017

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…Stainless steel alloys are widely used in many industries such as aerospace, medical device, pipeline, automotive, and die and tool industries. One of the most popular stainless steels is AISI 316L steel, which is an austenitic alloy widely used in industrial applications due to its high corrosion resistance with acceptable mechanical properties [ 1 , 2 ]. Additionally, austenitic steels are classified as difficult-to-cut, which is why additive manufacturing (AM) techniques have recently become an alternative to producing parts from them [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of the Dimensional and Shape Accuracy of Parts Made of 316L Manufactured Using the SLM and Casting Methods after Milling and WEDM

Machno,

Zębala

2024

Materials

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Parts made using selective laser melting (SLM) often require improvements to the quality of side surfaces. Therefore, the analysis of the machinability of metallic printed material is new/innovative. The surface of printed parts requires improvement in quality—surface roughness. Hence, there is a need for effective manufacturing techniques that improve the quality of the side surfaces of printed parts. In our work, we try to fill this research gap. This work comparatively analyzed the surface quality (roughness parameter Ra) after milling and wire electrical discharge machining (WEDM). The processed material was AISI 316L stainless steel, which was produced using the casting and SLM method. In the case of printed material, the influence of the direction of the tool (perpendicular, parallel) on the arrangement of sintered layers was also analyzed. The analysis of the results showed that processing the cast material and processing the material perpendicular to the arrangement of the layers gives similar results—similar relationships between the processing parameters and surface roughness were observed. However, processing parallel to the arrangement of sintered layers showed ambiguity in the relationships. Moreover, the best results of the Ra parameter (0.1–0.2 µm) were obtained for feeds of 0.08 mm/rev and 0.12 mm/rev and a cutting speed of 90 m/min. In this work, the novelty is the comparison of the surfaces of materials manufactured using different techniques (SLM, casting) after milling and WEDM processing.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of the Dimensional and Shape Accuracy of Parts Made of 316L Manufactured Using the SLM and Casting Methods after Milling and WEDM

Machno,

Zębala

2024

Materials

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Predicting defects in SLM-produced parts based on melt pools clustering analysis

Malashin,

Martysyuk,

Tynchenko

et al. 2024

Int J Adv Manuf Technol

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Assessment of Anisotropic Acoustic Properties in Additively Manufactured Materials: Experimental, Computational, and Deep Learning Approaches

Malashin,

Tynchenko,

Martysyuk

et al. 2024

Sensors

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The influence of acoustic anisotropy on ultrasonic testing reliability poses a challenge in evaluating products from additive technologies (AT). This study investigates how elasticity constants of anisotropic materials affect defect signal amplitudes in AT products. Experimental measurements on AT samples were conducted to determine elasticity constants. Using Computational Modeling and Simulation Software (CIVA), simulations explored echo signal changes across ultrasound propagation directions. The parameters A13 (the ratio between the velocities of ultrasonic transverse waves with vertical and horizontal polarizations at a 45-degree angle to the growth direction), A3 (the ratio for waves at a 90-degree angle), and Ag (the modulus of the difference between A13 and A3) were derived from wave velocity relationships and used to characterize acoustic anisotropy. Comparative analysis revealed a strong correlation (0.97) between the proposed anisotropy coefficient Ag and the amplitude changes. Threshold values of Ag were introduced to classify anisotropic materials based on observed amplitude changes in defect echo signals. In addition, a method leveraging deep learning to predict Ag based on data from other anisotropy constants through genetic algorithm (GA)-optimized neural network (NN) architectures is proposed, offering an approach that can reduce the computational costs associated with calculating such constants.

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An Investigation of the Anisotropic Mechanical Properties of Additive-Manufactured 316L SS with SLM

Cited by 3 publications

References 37 publications

Comparative Study of the Dimensional and Shape Accuracy of Parts Made of 316L Manufactured Using the SLM and Casting Methods after Milling and WEDM

Comparative Study of the Dimensional and Shape Accuracy of Parts Made of 316L Manufactured Using the SLM and Casting Methods after Milling and WEDM

Predicting defects in SLM-produced parts based on melt pools clustering analysis

Assessment of Anisotropic Acoustic Properties in Additively Manufactured Materials: Experimental, Computational, and Deep Learning Approaches

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