Performance evaluation of a manufacturing process under uncertainty using Bayesian networks

Nannapaneni, Saideep; Mahadevan, Sankaran; Rachuri, Sudarsan

doi:10.1016/j.jclepro.2015.12.003

Cited by 54 publications

(21 citation statements)

References 33 publications

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“…Bayesian networks have been widely used for the applications of diagnostics and prognostics. For example, in [34], the authors used BN to monitor the performance of the manufacturing process for molding and welding applications. In [35], BN was utilized to analyze the process disturbance cause and effect with a case study on heat recovery networks.…”

Section: Bayesian Network As An Alternative Methodsmentioning

confidence: 99%

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

Ardakani

Lee

2018

Machines

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Abstract:For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. Manufacturing processes, as a large group of applications in which data analytics can bring significant value to, are the focus of this study. As the cost of instrumenting the machines in a manufacturing process is significant, an alternative solution which relies solely on product quality measurements is greatly desirable in the manufacturing industry. In this paper, a minimal-sensing framework for machine anomaly detection in multistage manufacturing processes based on product quality measurements is introduced. This framework, which relies on product quality data along with products' manufacturing routes, allows the detection of variations in the quality of the products and is able to pinpoint the machine which is the cause of anomaly. A moving window is applied to the data, and a statistical metric is extracted by comparing the performance of a machine to its peers. This approach is expanded to work for multistage processes. The proposed method is validated using a dataset from a real-world manufacturing process and additional simulated datasets. Moreover, an alternative approach based on Bayesian Networks is provided and the performance of the two proposed methods is evaluated from an industrial implementation perspective. The results showed that the proposed similarity-based approach was able to successfully identify the root cause of the quality variations and pinpoint the machine that adversely impacted the product quality.

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Section: Bayesian Network As An Alternative Methodsmentioning

confidence: 99%

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

Ardakani

Lee

2018

Machines

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“…One way to overcome this problem is to reduce the input space through dimension reduction techniques. Variancebased global sensitivity analysis is one possible approach for dimension reduction where the sensitivity of each variable (original input variable, auxiliary variable, distribution parameter) is estimated and if the sensitivity is less than a pre-defined threshold, it can be assumed deterministic at its mean value [28].…”

Section: Inclusion Of Model Errorsmentioning

confidence: 99%

Reliability analysis under epistemic uncertainty

Nannapaneni

Mahadevan

2016

Reliability Engineering & System Safety

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“…Using the similar data set collected at the UC Berkeley Mechanical Engineering Machine Shop [9], Ak et al [10] built an empirical prediction model using an ensemble neural networks (NNs) approach to estimate the energy consumption for a computer numerical control (CNC) milling machine tool. In addition to pure data-driven techniques, Nannapaneni et al [11] proposed a hybrid framework using Bayesian networks to aggregate the uncertainty from multiple sources for the purpose of uncertainty quantification (UQ) in the prediction of performance of a manufacturing process. The mathematical equations are used to calculate the conditional probability relationships between parent and child nodes in the network.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

Park

Lechevalier

et al. 2017

Smart and Sustainable Manufacturing Systems

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This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

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Performance evaluation of a manufacturing process under uncertainty using Bayesian networks

Cited by 54 publications

References 33 publications

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

Reliability analysis under epistemic uncertainty

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

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