Subjectivity in Failure Mode Effects Analysis (FMEA) Severity Classification within a Reliability Centered Maintenance (RCM) Context

This research paper investigated subjectivity in the severity rating of failure modes within a risk analysis process. Although several risk analysis processes can be utilized, the study considered the application of Failure Modes Effects Analysis (FMEA) or Failure Modes Effects and Criticality Analysis (FMECA) due to its common use within the Aerospace Industry. The study investigated both differences in severity selection given varying amounts of experience as well as any association between severity selection and the provided input information. The main goal of the research was to investigate the impact of data quality on severity selection and to identify factors that impact the severity score, and thus greatly influence the overall risk reduction strategies both in new acquisition and fielded systems. Participants consisted of both experienced and inexperienced FMEA/FMECA users. Participants were tasked to select a severity rating for nine failure modes (across three trials) assuming a typical severity scale. Different input data sets were provided in each trial to ascertain if an association exits between severity class selection and the amount of information available during analysis. This study provided evidence that risk analysis participants are subjective during severity rating selection when utilizing FMEA/FMECA processes. Users who are provided with irrelevant failure and mishap data tend to select similar severity levels; however, when no information is provided to users, user selections will be dramatically more conservative. Participants appear to select similar severity ratings regardless of the relevancy of the provided data

Effects of Inter-Layer Time Interval on Temperature Gradients in Direct Laser Deposited Ti-6Al-4V

Parts fabricated via additive manufacturing (AM) methods are prone to experiencing high temperature gradients during manufacture resulting in internal residual stress formation. In the current study, a numerical model for predicting the temperature distribution and residual stress in Directed Energy Deposited (DED) Ti–6Al–4V parts is utilized for determining a relationship between local part temperature gradients with generated residual stress. Effects of time-interval between successive layer deposits, as well as layer deposition itself, on the temperature gradient vector for the first and each layer is investigated. The numerical model is validated using thermographic measurements of Ti-6Al-4V specimens fabricated via Laser Engineered Net Shaping® (LENS), a blown-powder/laser-based DED method. Results demonstrate the heterogeneity in the part’s spatiotemporal temperature field, and support the fact that as the part number, or single part size or geometry, vary, the resultant residual stress due to temperature gradients will be impacted. As the time inter-layer time interval increases from 0 to 10 second, the temperature gradient magnitude in vicinity of the melt pool will increase slightly.Mechanical Engineerin

Multi-Objective Process Optimization of Additive Manufacturing: A Case Study on Geometry Accuracy Optimization

Despite recent research efforts improving Additive Manufacturing (AM) systems, quality and reliability of AM built products remains as a challenge. There is a critical need to achieve process parameters optimizing multiple mechanical properties or geometry accuracy measures simultaneously. The challenge is that the optimal value of various objectives may not be achieved concurrently. Most of the existing studies aimed to obtain the optimal process parameters for each objective individually, resulting in duplicate experiments and high costs. In this study we investigated multiple geometry accuracy measures of parts fabricated by Fused Filament Fabrication (FFF) system. An integrated framework for systematically designing experiments is proposed to achieve multiple sets of FFF process parameters resulting in optimal geometry integrity. The proposed method is validated using a real world case study. The results show that optimal properties are achieved in a more efficient manner compared with existing methods.Mechanical Engineerin

SIMILARITY-BASED MULTI-SOURCE TRANSFER LEARNING APPROACH FOR TIME SERIES CLASSIFICATION

This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM). Conventional machine learning (ML) algorithms are widely adopted in PdM, however, most existing methods assume that the training (source) and testing (target) data follow the same distribution, and that labeled data are available in both source and target domains. For real-world PdM applications, the heterogeneity in machine original equipment manufacturers (OEMs), operating conditions, facility environment, and maintenance records collectively lead to heterogeneous distribution for data collected from different machines. This will significantly limit the performance of conventional ML algorithms in PdM. Moreover, labeling data is generally costly and time-consuming. Finally, industrial processes incorporate complex conditions, and unpredictable breakdown modes lead to extreme complexities for PdM. In this study, similarity-based multi-source transfer learning (SiMuS-TL) approach is proposed for real-time classification of time series signals. A new domain, called "mixed domain," is established to model the hidden similarities among the multiple sources and the target. The proposed SiMuS-TL model mainly includes three key steps: 1) learning group-based feature patterns, 2) developing group-based pre-trained models, and 3) weight transferring. The proposed SiMuS-TL model is validated by observing the state of the rotating machinery using a dataset collected on the Skill boss manufacturing system, publicly available standard bearing datasets, Case Western Reserve University (CWRU), and Paderborn University (PU) bearing datasets. The results of the performance comparison demonstrate that the proposed SiMuS-TL method outperformed conventional Support Vector Machine (SVM), Artificial Neural Network (ANN), and Transfer learning with neural networks (TLNN) without similarity-based transfer learning methods

Stochastic modeling and prognostic analysis of complex systems using condition-based real-time sensor signals

This dissertation presents a stochastic framework for modeling the degradation processes of components in complex engineering systems using sensor based signals. Chapters 1 and 2 discuses the challenges and the existing literature in monitoring and predicting the performance of complex engineering systems. Chapter 3 presents the degradation model with the absorbing failure threshold for a single unit and the RLD estimation using the first-passage-time approach. Subsequently, we develop the estimate of the RLD using the first-passage-time approach for two cases: information prior distributions and non-informative prior distributions. A case study is presented using real-world data from rolling elements bearing applications. Chapter 4 presents a stochastic methodology for modeling degradation signals from components functioning under dynamically evolving environmental conditions. We utilize in-situ sensor signals related to the degradation process, as well as the environmental conditions, to predict and continuously update, in real-time, the distribution of a component’s residual lifetime. Two distinct models are presented. The first considers future environmental profiles that evolve in a deterministic manner while the second assumes the environment evolves as a continuous-time Markov chain. Chapters 5 and 6 generalize the failure-dependent models and develop a general model that examines the interactions among the degradation processes of interconnected components/subsystems. In particular, we model how the degradation level of one component affects the degradation rates of other components in the system. Hereafter, we refer to this type of component-to-component interaction caused by their stochastic dependence as degradation-rate-interaction (DRI). Chapter 5 focuses on the scenario in which these changes occur in a discrete manner, whereas, Chapter 6 focuses on the scenario, in which DRIs occur in a continuous manner. We demonstrate that incorporating the effects of component interactions significantly improves the prediction accuracy of RLDs. Finally, we outline the conclusion remarks and a future work plan in Chapter 7.Ph.D

In-Situ Layer-Wise Quality Monitoring for Laser-Based Additive Manufacturing Using Image Series Analysis

Quality assurance has been one of the major challenges in laser-based additive manufacturing (AM) processes. This study proposes a novel process modeling methodology for layer-wise in-situ quality monitoring based on image series analysis. An image-based autoregressive (AR) model has been proposed based on the image registration function between consecutively observed thermal images. Image registration is used to extract melt pool location and orientation change between consecutive images, which contains sensing stability information. Subsequently, a Gaussian process model is used to characterize the spatial correlation within the error matrix. Finally, the extracted features from the aforementioned processes are jointly used for layer-wise quality monitoring. A case study of a thin wall fabrication by a Directed Laser Deposition (DLD) process is used to demonstrate the effectiveness of the proposed methodology.Mechanical Engineerin

Porosity Detection of Laser Based Additive Manufacturing Using Melt Pool Morphology Clustering

The microstructural and mechanical properties of Laser Based Additive Manufacturing (LBAM) are still inconsistent and unreliable, which is a major barrier that prevents Additive Manufacturing (AM) from entering main stream production. The key challenge is the lack of understanding for the underlying process-properties relationship. We monitor Laser Engineered Net Shaped (LENS) process using a state-of-art thermal image system, and the resulting high-speed Melt Pool (MP) data stream is used to characterize the complex thermo-physical process. We propose a novel method based on Self-Organizing Map to cluster the MPs based on their morphology and link MPs clusters’ characteristics to the porosity of fabricated parts, which is crucial to mechanical properties of parts. The results are validated using X-Ray tomography of Ti-64 thin-wall. Our approach identifies various patterns of MP morphology, which corresponds to different types of porosities. The proposed method can potentially be used to certify the part quality in a real-time and non-destructive manner.Mechanical Engineerin

Effect of Build Orientation on the Fatigue Behavior of Stainless Steel 316L Manufactured via a Laser-Powder Bed Fusion Process

n this study, the effects of build orientation on the mechanical properties and fatigue life of stainless steel (SS) 316L, fabricated using the Laser-Powder Bed Fusion (L-PBF) additive manufacturing (AM) process, were investigated under monotonic tensile and uniaxial strain-controlled fully-reversed (R = -1) cyclic loadings. Tensile tests were conducted at a strain rate of 0.001 s-1 , while fatigue tests were performed at strain amplitudes ranging from 0.1% to 0.4% at various frequencies to have a nearly consistent average strain rate in all tests. The comparison between the tensile properties of additively manufactured and wrought SS 316L revealed that L-PBF specimens exhibited higher yield and ultimate tensile stresses as compared to the wrought specimen. In addition, the elongation to failure of the wrought specimen was similar to that of the horizontally oriented specimen, while it was lower relative to specimens built in vertical and diagonal directions. From the strain-life fatigue analysis, the diagonally oriented L-PBF specimens generally exhibited lower fatigue strength as compared to vertical and horizontal specimens. The fractography analysis revealed three major types of defects to be responsible for the crack initiation and failure. These included (1) voids formed due to lack of fusion between the subsequent layers and entrapped gas, (2) inclusions formed due to the partially melted powder particles, and (3) un-melted powder particles clustered near a void.Mechanical Engineerin