Development of ANN model for surface roughness prediction of parts produced by varying fabrication parameters

Selective laser melting (SLM) is the most common additive manufacturing technique designed to fabricate functional parts with high accuracy. Depending on the desired properties, the process parameters for a given material need to be optimized for improving the overall reliability of the SLM devices. As all the process parameters are inter-dependent on each other, it is important to find an optimum value to suit the requirement and render the best build quality. This work primarily focuses on the effect of various process parameters such as laser power, scanning speed, and hatch spacing on the roughness of Inconel 718 parts fabricated on an EOS M290 machine. Statistical models of surface roughness are established to identify the relationship between the abovementioned process parameters. The capabilities developed in this study will permit a deep understanding of the process- property relationships in structural SLM components.

[1]  J. S. Zuback,et al.  Additive manufacturing of metallic components – Process, structure and properties , 2018 .

[2]  Ming Gao,et al.  The microstructure and mechanical properties of deposited-IN718 by selective laser melting , 2012 .

[3]  D. Basak,et al.  Support Vector Regression , 2008 .

[4]  M. Elbestawi,et al.  Effect of Selective Laser Melting Process Parameters on the Quality of Al Alloy Parts: Powder Characterization, Density, Surface Roughness, and Dimensional Accuracy , 2018, Materials.

[5]  Amirhesam Amerinatanzi,et al.  Fabrication of NiTi through additive manufacturing: A review , 2016 .

[6]  P. Alvarez,et al.  Effect of IN718 Recycled Powder Reuse on Properties of Parts Manufactured by Means of Selective Laser Melting , 2014 .

[7]  Weihui Wu,et al.  Investigation into the influence of laser energy input on selective laser melted thin-walled parts by response surface method , 2018 .

[8]  M. Elahinia,et al.  Anisotropic tensile and actuation properties of NiTi fabricated with selective laser melting , 2018 .

[9]  M. Elahinia,et al.  The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy , 2021, Materials Today Communications.

[10]  Ohyung Kwon,et al.  A deep neural network for classification of melt-pool images in metal additive manufacturing , 2018, J. Intell. Manuf..

[11]  Robert X. Gao,et al.  Machine learning-based image processing for on-line defect recognition in additive manufacturing , 2019, CIRP Annals.

[12]  N. Shayesteh Moghaddam,et al.  Study on the Effect of Powder-Bed Fusion Process Parameters on the Quality of as-Built IN718 Parts Using Response Surface Methodology , 2020, Metals.

[13]  Wenyao Xu,et al.  Surfel convolutional neural network for support detection in additive manufacturing , 2019, The International Journal of Advanced Manufacturing Technology.

[14]  Roya Etminani-Ghasrodashti,et al.  Investigating the Role of Transportation Barriers in Cancer Patients’ Decision Making Regarding the Treatment Process , 2021 .

[15]  Narges Shayesteh Moghaddam,et al.  Achieving superelasticity in additively manufactured NiTi in compression without post-process heat treatment , 2019, Scientific Reports.

[16]  Amirhesam Amerinatanzi,et al.  Additive manufacturing of NiTiHf high temperature shape memory alloy , 2018 .

[17]  L. Hitzler,et al.  Position dependent surface quality in selective laser melting , 2017 .

[18]  J. Nellesen,et al.  Hot isostatic pressing of IN718 components manufactured by selective laser melting , 2017 .

[19]  Frank W. Liou,et al.  Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network , 2020, Applied Sciences.

[20]  A. Amerinatanzi,et al.  Study on variations of microstructure and metallurgical properties in various heat-affected zones of SLM fabricated Nickel–Titanium alloy , 2020 .