다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Additive manufacturing of metallic components – Process, structure and properties

Interpretation of X-ray Powder Diffraction Patterns . By H. Lipson and H. Steeple. Pp. viii + 335 + 3 plates. (Mac-millan: London; St Martins Press: New York, May 1970.) £4.

X-Ray Diffraction

Remaining useful life estimation - A review on the statistical data driven approaches

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

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

Effects of building orientation and heat treatment on fatigue behavior of selective laser melted 17-4 PH stainless steel

Accurate predictions of equipment failure times are necessary to improve replacement and spare parts inventory decisions. Most of the existing decision models focus on using population-specific reliability characteristics, such as failure time distributions, to develop decision-making strategies. Since these distributions are unaffected by the underlying physical degradation processes, they do not distinguish between the different degradation characteristics of individual components of the population. This results in less accurate failure predictability and hence less accurate replacement and inventory decisions. In this paper, we develop a sensor-driven decision model for component replacement and spare parts inventory. We integrate a degradation modeling framework for computing remaining life distributions using condition-based in situ sensor data with existing replacement and inventory decision models. This enables the dynamic updating of replacement and inventory decisions based on the physical condit...

Sensor-driven prognostic models for equipment replacement and spare parts inventory

Recent developments in degradation modeling have been targeted towards utilizing degradation-based sensory signals to predict residual life distributions. Typically, these models consist of stochastic parameters that are estimated with the aid of an historical database of degradation signals. In many applications, building a degradation database, where components are run-to-failure, may be very expensive and time consuming, as in the case of generators or jet engines. The degradation modeling framework presented herein addresses this challenge by utilizing failure time data, which are easier to obtain, and readily available (relative to sensor-based degradation signals) from historical maintenance/repair records. Failure time values are first fitted to a Bernstein distribution whose parameters are then used to estimate the prior distributions of the stochastic parameters of an initial degradation model. Once a complete realization of a degradation signal is observed, the assumptions of the initial degradation model are revised and improved for future predictions. This approach is validated using real world vibration-based degradation information from a rotating machinery application.

Residual Life Predictions in the Absence of Prior Degradation Knowledge

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.

Alaa Elwany

Papers

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

Effects of building orientation and heat treatment on fatigue behavior of selective laser melted 17-4 PH stainless steel

Sensor-driven prognostic models for equipment replacement and spare parts inventory

Residual Life Predictions in the Absence of Prior Degradation Knowledge

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