Additive manufacturing of metallic components – Process, structure and properties

Additive manufacturing: scientific and technological challenges, market uptake and opportunities

Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing

Additive manufacturing of fatigue resistant materials: Challenges and opportunities

Advances in 4D Printing: Materials and Applications

A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing

Additive manufacturing (AM) is a set of emerging technologies that can produce physical objects with complex geometrical shapes directly from a digital model. With many unique capabilities, such as design freedom, it has recently gained increasing attention from researchers, practitioners, and public media. However, achieving the full potential of AM is hampered by many challenges, including the lack of predictive models that correlate processing parameters with the properties of the processed part. We develop a Gaussian process-based predictive model for the learning and prediction of the porosity in metallic parts produced using selective laser melting (SLM – a laser-based AM process). More specifically, a spatial Gaussian process regression model is first developed to model part porosity as a function of SLM process parameters. Next, a Bayesian inference framework is used to estimate the statistical model parameters, and the porosity of the part at any given setting is predicted using the Kriging method. A case study is conducted to validate this predictive framework through predicting the porosity of 17-4 PH stainless steel manufacturing on a ProX 100 selective laser melting system.

Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models

Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood Successful processing for one material, might not necessarily apply to a different material This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination The predictions are then mapped onto a power versus scan speed diagram delimiting the conduction from the keyhole melting controlled regimes This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

We demonstrate a method to achieve local control of 3-dimensional thermal history in a metallic alloy, which resulted in designed spatial variations in its functional response. A nickel-titanium shape memory alloy part was created with multiple shape-recovery stages activated at different temperatures using the selective laser melting technique. The multi-stage transformation originates from differences in thermal history, and thus the precipitate structure, at various locations created from controlled variations in the hatch distance within the same part. This is a first example of precision location-dependent control of thermal history in alloys beyond the surface, and utilizes additive manufacturing techniques as a tool to create materials with novel functional response that is difficult to achieve through conventional methods.

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Gustavo Tapia

Papers

A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing

Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

Spatial Control of Functional Response in 4D-Printed Active Metallic Structures.

On the printability and transformation behavior of nickel-titanium shape memory alloys fabricated using laser powder-bed fusion additive manufacturing