Uncertainty analysis of force coefficients during micromilling of titanium alloy

Gözü, Erman

doi:10.1007/s00170-017-0567-8

Cited by 21 publications

(3 citation statements)

References 22 publications

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“…Prediction refers to the use of existing knowledge, experience, and scientific methods to estimate the future environment and to estimate and evaluate the future development trend of things based on known factors in the past and present [19]. The predictions in planning and scheduling are mainly divided into two categories, namely, prediction before production and prediction in production.…”

Section: Prediction Of Planned Schedulingmentioning

confidence: 99%

Model construction of planning and scheduling system based on digital twin

Wang

2020

Int J Adv Manuf Technol

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The emergence of uncertainty factors will exert a strong influence on the accuracy of workshop scheduling. Digital twin' technology and planning and scheduling are integrated to form a planning and scheduling system based on digital twin, which can effectively use the concept of twin to carry out all-around management and control of the uncertain factors in production activities so that the planning and scheduling scheme in the production process can accurately guide the actual production. Firstly, the system establishes management and control mechanism of planning and scheduling system based on digital twin. Second, it establishes a digital twin model of the planning and scheduling system for the management and control mechanism. Finally, the key technology of the planning and scheduling system is expounded, which mainly includes the perception and collection of total production factor information and the planning and scheduling prediction. In order to verify the validity of the scheme, the planning and scheduling system suited for the enterprise is designed and developed in combination with the frame workshop.

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Section: Prediction Of Planned Schedulingmentioning

confidence: 99%

Model construction of planning and scheduling system based on digital twin

Wang

2020

Int J Adv Manuf Technol

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“…[26][27][28]. Monte Carlo is also used to draw a large number of samples within a distribution defined based on probability parameters [29].…”

Section: Data Simulation Using the Monte-carlo Techniquementioning

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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“…G€ oz€ u and Karpat [11] studied application of Bayesian inference to predict cutting force in micromilling of Titanium alloy TiAl4V. The Metropolis-Hasting algorithm of MCMC was used to identify probability distributions of the cutting and ploughing forces coefficients based on experimental measurements and a mechanistic model of micromilling.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Sequential Prediction of Cutting Force Using Kienzle Model in Orthogonal Turning Process

Salehi

Schmitz

Copenhaver

et al. 2018

Journal of Manufacturing Science and Engineering

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Probabilistic sequential prediction of cutting forces is performed applying Bayesian inference to Kienzle force model. The model uncertainties are quantified using the Metropolis algorithm of the Markov chain Monte Carlo (MCMC) approach. Prior probabilities are established and posteriors of the models parameters and force predictions are completed using the results of orthogonal turning experiments. Two types of tools with chamfer (rake) angles of 0 deg and −10 deg are tested under various cutting speed and feed per revolution values. First, Bayesian inference is applied to two force models, Merchant and Kienzle, to investigate the cutting force prediction at the low feed values for the 0 deg rake angle tool. Second, the results of the posteriors of the Kienzle model parameters are used as prior probabilities of the −10 deg rake angle tool. The simulation results of the 0 deg and −10 deg tool rake angle are compared with the experiments which are obtained under other cutting conditions for model verification. Maximum prediction errors of 7% and 9% are reported for the tangential and feed forces, respectively. This indicates a good capability of the Bayesian inference for model parameter identification and cutting force prediction considering the inherent uncertainty and minimum input experimental data.

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Uncertainty analysis of force coefficients during micromilling of titanium alloy

Cited by 21 publications

References 22 publications

Model construction of planning and scheduling system based on digital twin

Model construction of planning and scheduling system based on digital twin

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

Probabilistic Sequential Prediction of Cutting Force Using Kienzle Model in Orthogonal Turning Process

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