Engineering-Driven Factor Analysis for Variation Source Identification in Multistage Manufacturing Processes

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Jian Liu ◽  
Jianjun Shi ◽  
S. Jack Hu
Keyword(s):  
Factor Analysis ◽  
Source Identification ◽  
Domain Knowledge ◽  
Measurement Data ◽  
Factor Loading ◽  
Data Driven ◽  
Manufacturing Processes ◽  
Multistage Manufacturing ◽  
Engineering Models

Variation source identification is an important task of quality assurance in multistage manufacturing processes (MMPs). However, existing approaches, including the quantitative engineering-model-based methods and the data-driven methods, provide limited capabilities in variation source identification. This paper proposes a new methodology that does not depend on accurate quantitative engineering models. Instead, engineering domain knowledge about the interactions between potential variation sources and product quality variables is represented as qualitative indicator vectors. These indicator vectors guide the rotation of the factor loading vectors that are derived from factor analysis of the multivariate measurement data. Based on this engineering-driven factor analysis, a procedure is presented to identify multiple variation sources that are present in a MMP. The effectiveness of the proposed methodology is demonstrated in a case study of a three-stage assembly process.

Diagnosing Multistage Manufacturing Processes With Engineering-Driven Factor Analysis Considering Sampling Uncertainty

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Jian Liu ◽  
Jionghua Jin
Keyword(s):  
Factor Analysis ◽  
Spatial Variation ◽  
Identification Problem ◽  
Factor Loading ◽  
Manufacturing Processes ◽  
Multistage Manufacturing ◽  
Factor Rotation ◽  
Variation Patterns ◽  
True Values

A new engineering-driven factor analysis (EDFA) method has been developed to assist the variation source identification for multistage manufacturing processes (MMPs). The proposed method investigated how to fully utilize qualitative engineering knowledge of the spatial variation patterns to guide the factor rotation. It is shown that ideal identification can be achieved by matching the rotated factor loading vectors with the qualitative indicator vectors (IV) that are defined according to spatial variation patterns based on the design constraints. However, the random sampling variability may significantly affect the estimation of the rotated factor loading vectors, leading to the deviations from their true values. These deviations may change the matching results and cause misidentification of the actual variation sources. By using implicit differentiation approach, this paper derives the asymptotic distribution and the associated variance-covariance matrix of the rotated factor loading vectors. Therefore, by considering the effect of sample estimation variability, the variation sources identification problem is reformulated as an asymptotic statistical test of the hypothesized match between the rotated factor loading vectors and the indicator vectors. A real-world case study is provided to demonstrate the effectiveness of the proposed matching method and its robustness to the sample uncertainty.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

Online Data-Driven Prediction of Spatio-Temporal System Behavior Using High-Fidelity Simulations and Sparse Sensor Measurements

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
X. Zhao ◽  
S. Azarm ◽  
B. Balachandran
Keyword(s):  
Dynamical Systems ◽  
Computational Cost ◽  
Measurement Data ◽  
Data Driven ◽  
High Fidelity ◽  
Simulation Data ◽  
Online Data ◽  
Temporal Prediction ◽  
Spatio Temporal

Abstract Predicting the behavior or response for complicated dynamical systems during their operation may require high-fidelity and computationally costly simulations. Because of the high computational cost, such simulations are generally done offline. The offline simulation data can then be combined with sensors measurement data for online, operational prediction of the system's behavior. In this paper, a generic online data-driven approach is proposed for the prediction of spatio-temporal behavior of dynamical systems using their simulation data combined with sparse, noisy sensors measurement data. The approach relies on an offline–online approach and is based on an integration of dimension reduction, surrogate modeling, and data assimilation techniques. A step-by-step application of the proposed approach is demonstrated by a simple numerical example. The performance of the approach is also evaluated by a case study which involves predicting aeroelastic response of a joined-wing aircraft in which sensors are sparsely placed on its wing. Through this case study, it is shown that the results obtained from the proposed spatio-temporal prediction technique have comparable accuracy to those from the high-fidelity simulation, while at the same time significant reduction in computational expense is achieved. It is also shown that, for the case study, the proposed approach has a prediction accuracy that is relatively robust to the sensors’ locations.

scholarly journals Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities

2021 ◽  
Vol 13 (3) ◽  
pp. 59
Author(s):  
Albert Weichselbraun ◽  
Philipp Kuntschik ◽  
Vincenzo Francolino ◽  
Mirco Saner ◽  
Urs Dahinden ◽  
...  
Keyword(s):  
Social Sciences ◽  
Domain Knowledge ◽  
Data Driven ◽  
Media Criticism ◽  
Domain Experts ◽  
Social Sciences And Humanities ◽  
The Social ◽  
Recent Developments ◽  
Selection Of

Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.

Variation Source Identification in Manufacturing Processes Based on Relational Measurements of Key Product Characteristics

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Jean-Philippe Loose ◽  
Shiyu Zhou ◽  
Dariusz Ceglarek
Keyword(s):  
Quality Improvement ◽  
Source Identification ◽  
Joint Probability ◽  
Manufacturing Processes ◽  
Quality Model ◽  
Dimensional Measurements ◽  
Identification Techniques ◽  
Joint Probability Density ◽  
Quality Measurements

Variation source identification for manufacturing processes is critical for product dimensional quality improvement, and various techniques have been developed in recent years. Most existing variation source identification techniques are based on a linear fault-quality model, in which the relationships between process faults and product dimensional quality measurements are linear. In practice, many dimensional measurements are actually nonlinearly related to the process faults: For example, relational dimension measurements such as the relative distance between features are used to monitor composite tolerances. This paper presents a variation source identification methodology in the presence of these relational dimension measurements. In the proposed methodology, the joint probability density of the measurements is determined as a function of the process parameters; then, series of statistical comparisons are performed to differentiate and identify the variation source. A case study is also presented to illustrate the effectiveness of the methodology.

Professional Stress among Junior Commissioned Officers and Non Commissioned Officers of Indian Military Intelligence – A Case Study

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 133-145
Author(s):  
Dr. S. S. Nirmala ◽  
Dr. N. Kogila ◽  
T. Porkodi
Keyword(s):  
Factor Analysis ◽  
Correlation Matrix ◽  
Null Hypothesis ◽  
Primary Data ◽  
Questionnaire Data ◽  
Military Intelligence ◽  
Sphericity Test ◽  
Professional Stress ◽  
Structured Questionnaire

The present study is focusing on the professional stress on organisation among the Junior Commissioned Officers (JCOs) and Non-Commissioned Officers (NCOs) of Indian Military Intelligence. 384 samples of Military Intelligence personnel will be taken for this study. Sources of data is Primary data include a structured questionnaire. Data was collected through structured questionnaire and measure through Likert’s scale, using KMO measure of sampling adequacy, Cronbach’s alpha for checking internal consistency, Bartlett sphericity test for testing the null hypothesis and various factor analysis including Eigenvalues, Extract square Sum loading, variance percent and Accumulation percent values relative comparison and Correlation matrix will be used as tools to arrive at desired results and statistical interpretations. The hypotheses put for test and the resultant values at 0.01 and 0.05 (for different factors) clearly indicated that there is an existence of association between different level of cadres and professional stress among personnel of Indian Military Intelligence. The authority who can formulate the rules and regulations and binding them on the lower cadres and professions to accept and adopt.

Sign in / Sign up

Export Citation Format

Share Document