Alaa Elwany

Structured Replacement Policies for Components with Complex Degradation Processes and Dedicated Sensors

Failure of many engineering systems usually results from a gradual and irreversible accumulation of damage, a degradation process. Most degradation processes can be monitored using sensor technology. The resulting degradation signals are usually correlated with the degradation process. A system is considered to have failed once its degradation signal reaches a prespecified failure threshold. This paper considers a replacement problem for components whose degradation process can be monitored using dedicated sensors. First, we present a stochastic degradation modeling framework that characterizes, in real time, the path of a components degradation signal. These signals are used to predict the evolution of the components degradation state. Next, we formulate a single-unit replacement problem as a Markov decision process and utilize the real-time signal observations to determine a replacement policy. We focus on exponentially increasing degradation signals and show that the optimal replacement policy for this class of problems is a monotonically nondecreasing control limit policy. Finally, the model is used to determine an optimal replacement policy by utilizing vibration-based degradation signals from a rotating machinery application.

Accelerated process optimization for laser-based additive manufacturing by leveraging similar prior studies

ABSTRACT Manufacturing parts with target properties and quality in Laser-Based Additive Manufacturing (LBAM) is crucial toward enhancing the “trustworthiness” of this emerging technology and pushing it into the mainstream. Most of the existing LBAM studies do not use a systematic approach to optimize process parameters (e.g., laser power, laser velocity, layer thickness, etc.) for desired part properties. We propose a novel process optimization method that directly utilizes experimental data from previous studies as the initial experimental data to guide the sequential optimization experiments of the current study. This serves to reduce the total number of time- and cost-intensive experiments needed. We verify our method and test its performance via comprehensive simulation studies that test various types of prior data. The results show that our method significantly reduces the number of optimization experiments, compared with conventional optimization methods. We also conduct a real-world case study that optimizes the relative density of parts manufactured using a Selective Laser Melting system. A combination of optimal process parameters is achieved within five experiments.

Mechanical and Microstructural Properties of Selective Laser Melted 17-4 PH Stainless Steel

The present article focuses on the mechanical properties and microstructural features of Selective Laser Melted (SLM) 17-4 precipitation hardening (PH) stainless steel (SS) as well as their comparison to conventionally built materials. The topics investigated are the effects of different building orientations and post-fabrication heat treatment (solution annealing and aging) on the mechanical and microstructural characteristics of samples fabricated by SLM. Yield and ultimate tensile strengths of SLM-produced 17-4 PH SS were found to be lower than those of wrought materials (H900 condition). In addition, building orientations showed a noticeable effect on tensile properties. Presence of defects, such as pores resulting from entrapped gas, un-melted regions, and powder particles resulting from lack of fusion were the main reasons for lower elongation to failure of SLM-produced 17-4PH SS in this study.Copyright

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

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.

Modeling, Simulation and Experimental Validation of Heat Transfer During Selective Laser Melting

Selective Laser Melting (SLM), a laser powder-bed fusion (PBF-L) additive manufacturing method, utilizes a laser to selectively fuse adjacent metal powders. The powders are aligned in a bed that moves vertically to allow for layer-by-layer part construction-Process-related heat transfer and thermal gradients have a strong influence on the microstructural features, and subsequent mechanical properties, of the parts fabricated via SLM. In order to understand and control the heat transfer inherent to SLM, and to ensure high quality parts with targeted microstructures and mechanical properties, comprehensive knowledge of the related energy and mass transport during manufacturing is required. In this study, the transient temperature distribution within and around parts being fabricated via SLM is numerically simulated and the results are provided to aid in quantify the SLM heat transfer. In order to verify simulation output, and to estimate actual thermal gradients and heat transfer, experiments were separately conducted within a SLM machine using a substrate with embedded thermocouples. The experiments focused on characterizing heat fluxes during initial deposition on an initially-cold substrate and during the fabrication of a thin-walled structure built via stainless steel 17-4 powders. Results indicate that it is important to model heat transfer thorough powder bed as well as substrate.Copyright

Mechanical properties and microstructural characterization of selective laser melted 17-4 PH stainless steel

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.

Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

AbstractMetal additive manufacturing (AM) typically suffers from high degrees 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 metal 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. Graphical AbstractTwo-stage multi-variate statistical calibration of the finite element thermal model

Numerical and experimental analysis of heat distribution in the laser powder bed fusion of Ti-6Al-4V

ABSTRACT Laser Powder Bed Fusion (LPBF) of metallic parts is a complex process involving simultaneous interplay between several physical mechanisms such as solidification, heat transfer (convection, conduction, radiation, etc.), and fluid flow. In the present work, a three-dimensional finite element model is developed for studying the thermal behavior during LPBF of Ti-6Al-4V alloy. Two phase transitions are considered in the model: solid-to-liquid and liquid-to-gas. It is demonstrated that metal evaporation has a notable effect on the thermal history evolution during fabrication and should not be overlooked in contrast with the majority of previous research efforts on modeling and simulation of additive manufacturing processes. The model is validated through experimental measurements of different features including the size and morphology of the Heat-Affected Zone (HAZ), melt pool size, and thermal history. Reasonable agreement with experimental measurements of the HAZ width and depth are obtained with corresponding errors of 3.2% and 10.8%. Qualitative agreement with experimental measurements of the multi-track thermal history is also obtained, with some discrepancies whose sources are discussed in detail. The current work presents one of the first efforts to validate the multi-track thermal history using dual-wavelength pyrometry, as opposed to single-track experiments. The effects of selected model parameters and evaporation on the melt pool/HAZ size, geometry and peak predicted temperature during processing, and their sensitivities to these parameters are also discussed. Sensitivity analysis reveals that thermal conductivity of the liquid phase, porosity level of the powder bed, and absorptivity have direct influence on the model predictions, with the influence of the thermal conductivity of the liquid phase being most significant.

Towards High-Frequency Shape Memory Alloy Actuators Incorporating Liquid Metal Energy Circuits

Large shape memory alloy (SMA) actuators are currently limited to applications with low cyclic actuation frequency requirements due to their generally poor heat transfer rates. This limitation can be overcome through the use of distributed body heating methods such as induction heating or by accelerated cooling methods such as forced convection in internal cooling channels. In this work, a monolithic SMA beam actuator containing liquid gallium–indium alloy-filled channels is fabricated through additive manufacturing. These liquid metal channels enable a novel multi-physical thermal control system, allowing for increased heating and cooling rates to facilitate an increased cyclic actuation frequency. Liquid metal flowing in the channels performs the dual tasks of inductively heating the surrounding SMA material and then actively cooling the SMA via forced internal fluid convection. A coupled thermoelectric model, implemented in COMSOL, predicts a possible fivefold increase in the cyclic actuation frequency due to these increased thermal transfer rates when compared to conventional SMA forms having external heating coils and being externally cooled via forced convection. The first ever experimental prototype SMA actuator of this type is described and, even at much lower flow rates, is shown to exhibit a decrease in cooling time of 40.9%.

A Delay Time Model With Multiple Defect Types and Multiple Inspection Methods

We develop delay time models to determine optimal inspection policies for deteriorating infrastructures. We consider the case where complex infrastructures can fail due to different causes (defects) originating from various environmental or operational conditions, and capture this through modeling the arrival of different types of defects as nonhomogeneous Poisson processes with distinct rates of occurrence of defects. Additionally, we assume at each inspection epoch, there are multiple inspection methods available for use from which one is to be selected for use at that particular epoch. The key contribution that distinguishes our proposed models from previous works on delay time modeling is simultaneously considering multiple defect types and multiple inspection methods. Two mixed-integer nonlinear programming models are introduced to address the problem described above. The first model focuses on determining the optimal inspection policy that maximizes the reliability of the system over its useful life subject to a minimal threshold value of this reliability term. The second model determines the optimal policy that minimizes the system downtime. The two models are solved using a branch-and-cut global optimization approach. Two separate numerical studies are conducted to demonstrate the performance of the models and validate it through benchmarking these results against a prior study in the literature.