A blended empirical shot stream velocity model for improvement of shot peening production

Nguyen, Van Bo; Teo, Augustine; Ba, Te; Aramcharoen, Ampara; Ahluwalia, K. S.; Kang, Chang Wei

doi:10.1007/s00170-021-07972-w

Cited by 3 publications

(1 citation statement)

References 25 publications

(33 reference statements)

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“…Smart and advanced manufacturing has become a hot research topic recently. In particular, for peening operation, the huge amount of historical data generated in actual operations and trials with physics background knowledge can be useful for model development in the model-based control system design (e.g., AI or ML model [14,15], empirical process model [16,17]).…”

Section: Introductionmentioning

confidence: 99%

A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

Nguyen

Teo

et al. 2022

Int J Adv Manuf Technol

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In practical peening operation, the values of inlet air pressure and media ow rate are manually preset to acquire desired intensity requirements. The operator often needs to perform intensive experimental trials to determine a set of operational inputs for actual production. Obtaining these operational parameters is often time-consuming and labor-intensive. Thus, in this study, we propose an optimal distributed model predictive control for the multiple inputs / multiple outputs system to address the issues. In the newly developed system, control actions of inlet air pressure and voltage are optimally obtained with the anticipation of the predictive future states of the plant models, while reference values of air pressure at the nozzle and media owrate are determined using a proxy model. The dynamical plant models include an air pressure model and a media owrate model, which are developed based on measurement data and physics-based knowledge using the sparse identi cation of nonlinear dynamics algorithm. The proxy model is developed from the measurement data of the intensity, pressure, and media owrate using a deep machine-learning algorithm. The control performance is demonstrated using on-site controls at the physical machine for different operational scenarios. The obtained measurement results exhibit a favorable control performance in stability, robustness, and accuracy. The measurement intensity is consistent with the target setting value; the difference is smaller than the industrial threshold of ± 0.01mmA for all random tests. In another word, all target setting intensity can be achieved without the need of performing trials to determine the operational parameters. It also suggests that the developed control system can be deployed to the physical machine for actual production.

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Section: Introductionmentioning

confidence: 99%

A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

Nguyen

Teo

et al. 2022

Int J Adv Manuf Technol

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Investigation of the correlation between nozzle structure and particle motion during shot peening using the PIV method

Fan,

Guo,

Gong

et al. 2024

Int J Adv Manuf Technol

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A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

Nguyen

Teo²,

Ba³

et al. 2022

Preprint

Self Cite

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In practical peening operation, the values of inlet air pressure and media flow rate are manually preset to acquire desired intensity requirements. The operator often needs to perform intensive experimental trials to determine a set of operational inputs for actual production. Obtaining these operational parameters is often time-consuming and labor-intensive. Thus, in this study, we propose an optimal distributed model predictive control for the multiple inputs / multiple outputs system to address the issues. In the newly developed system, control actions of inlet air pressure and voltage are optimally obtained with the anticipation of the predictive future states of the plant models, while reference values of air pressure at the nozzle and media flowrate are determined using a proxy model. The dynamical plant models include an air pressure model and a media flowrate model, which are developed based on measurement data and physics-based knowledge using the sparse identification of nonlinear dynamics algorithm. The proxy model is developed from the measurement data of the intensity, pressure, and media flowrate using a deep machine-learning algorithm. The control performance is demonstrated using on-site controls at the physical machine for different operational scenarios. The obtained measurement results exhibit a favorable control performance in stability, robustness, and accuracy. The measurement intensity is consistent with the target setting value; the difference is smaller than the industrial threshold of ± 0.01mmA for all random tests. In another word, all target setting intensity can be achieved without the need of performing trials to determine the operational parameters. It also suggests that the developed control system can be deployed to the physical machine for actual production.

show abstract

A blended empirical shot stream velocity model for improvement of shot peening production

Cited by 3 publications

References 25 publications

A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

Investigation of the correlation between nozzle structure and particle motion during shot peening using the PIV method

A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

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