Purpose-The purpose of this paper is to understand the effect of four different factors: building orientation, heat treatment (solution annealing and aging), thermal history and process parameters on the mechanical properties and microstructural features of 17-4 precipitation hardening (PH) stainless steel (SS) parts produced using selective laser melting (SLM). Design/methodology/approach-Various sets of test samples were built on a ProX 100™ SLM system under argon environment. Characterization studies were conducted using mechanical tensile and compression test, microhardness test, optical microscopy, X-ray diffraction and scanning electron microscopy. Findings-Results indicate that building orientation has a direct effect on the mechanical properties of SLM parts, as vertically built samples exhibit lower yield and tensile strengths and elongation to failure. Post-SLM heat treatment proved to have positive effects on part strength and hardness, but it resulted in reduced ductility. Longer inter-layer time intervals between the melting of successive layers allow for higher austenite content because of lower cooling rates, thus decreasing material hardness. On the other hand, tensile properties such as elongation to failure, yield strength and tensile strength were not significantly affected by the change in inter-layer time intervals. Similar to other AM processes, SLM process parameters were shown to be instrumental in achieving desirable part properties. It is shown that without careful setting of process parameters, parts with defects (porosity and unmelted powder particles) can be produced. Originality/value-Although the manufacturing of 17-4 PH SS using SLM has been investigated in the literature, the paper provides the first comprehensive study on the effect of different factors on mechanical properties and microstructure of SLM 17-4 PH. Optimizing process parameters and using heat treatment are shown to improve the properties of the part.

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Metal additive manufacturing (AM) typically suffers from high degree of variability in the properties/performance of the fabricated parts, particularly due to the lack of understanding and control over the physical mechanisms that govern microstructure formation during fabrication. This paper directly addresses an important problem in AM: the determination of the thermal history of the deposited material. Any attempts to link process to microstructure in AM would need to consider the thermal history of the material. In-situ monitoring only provides partial information and simulations may be necessary to have a comprehensive understanding of the thermo-physical conditions to which the deposited material is subjected. We address this in the present work through linking thermal models to experiments via a computationally efficient surrogate modeling approach based on multivariate Gaussian processes (MVGPs). The MVGPs are then used to calibrate the free parameters of the multi-physics models against experiments, sidestepping the use of prohibitively expensive Monte Carlo-based calibration. This framework thus makes it possible to efficiently evaluate the impact of varying process parameter inputs on the characteristics of the melt pool during AM. We demonstrate the framework on the calibration of a thermal model for Laser-Powder Bed Fusion AM of Ti-6Al-4V against experiments carried out over a wide window in the process parameter space. While this work deals with problems related to AM, its applicability is wider as the proposed framework could potentially be used in many other ICME-based problems where it is essential to link expensive computational materials science models to available experimental data.

A growing research trend in additive manufacturing (AM) calls for layerwise anomaly detection as a step toward enabling real-time process control, in contrast to ex situ or postprocess testing and characterization. We propose a method for layerwise anomaly detection during laser powder-bed fusion (L-PBF) metal AM. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of the following four-step anomaly detection procedure: (1) using the captured thermal images, a process signature of a just-fabricated layer is generated. Next, a signature difference is obtained by subtracting the process signature of that particular layer from a prespecified reference signature, (2) a screening step selects potential regions of interests (ROIs) within the layer that are likely to contain process anomalies, hence reducing the computational burden associated with analyzing the full layer data, (3) the spatial dependence of these ROIs is modeled using a Gaussian process model, and then pixels with statistically significant deviations are flagged, and (4) using the quantity and the spatial pattern of the flagged pixels as predictors, a classifier is trained and implemented to determine whether the process is in- or out-of-control. We validate the proposed method using a case study on a commercial L-PBF system custom-instrumented with a dual-wavelength imaging pyrometer for capturing the thermal images during fabrication.