Advanced model-based controller for cyber-physical shot peening process

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

doi:10.1007/s00170-021-07009-2

Cited by 7 publications

(9 citation statements)

References 25 publications

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“…The process model (F 1 ) for this subsystem links the pressure at the nozzle to the inlet air pressure with the pressure regulator as a manipulated variable. This development work is inherited from the previous study [14]. The media ow rate subsystem includes a media valve, an input voltage sensor, and an output voltage sensor.…”

Section: F(x Umentioning

confidence: 99%

“…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%

“…In the previous study, we proposed a single-input / single-output (SISO) MPC with a proxy model to partially automate the shot peening operation based on controlling inlet air pressure [10]. More speci cally, the proxy model was developed from a set of historical data using the extreme gradient boosting (XGBoost) ML algorithm [18] to translate the desired intensity to an air pressure reference set point at the nozzle [14,15]. The process model that links the inlet air pressure to the air pressure on the nozzle, was developed and deployed in the SISO MPC controller to automatically drive the dynamical process of the peening system to the reference set point, as such; the desired intensity is achieved [14].…”

Section: Introductionmentioning

confidence: 99%

“…More speci cally, the proxy model was developed from a set of historical data using the extreme gradient boosting (XGBoost) ML algorithm [18] to translate the desired intensity to an air pressure reference set point at the nozzle [14,15]. The process model that links the inlet air pressure to the air pressure on the nozzle, was developed and deployed in the SISO MPC controller to automatically drive the dynamical process of the peening system to the reference set point, as such; the desired intensity is achieved [14]. This proposed technology, however, is still not an optimal solution as it only performs control action based on inlet air pressure, whilst the media ow rate still needs to be pre-set and heavily relies on the operator's experiences.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, in this study, we consider the distributed model predictive control where the information can exchange among sub-processes locally but without solving a centralized MPC problem, to design MIMO for a smarter shot-peening machine. Our objectives are to (1) inherit the good work of our previous development of SISO MPC for controlling the air pressure [14], (2) apply the same methodology to develop the controller for media ow rate, and (3) develop sub-models that count on in uences of the future change in the air pressure to media ow rate model and vice-versa. In addition, the distributed MPC approach is useful as the local optimization problems can be much smaller than a centralized problem, thus, it can help to speed up the computational time to achieve real-time control [19].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Self Cite

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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: F(x Umentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

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

Nguyen

Teo

et al. 2021

Int J Adv Manuf Technol

Self Cite

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Peening intensity and coverage are vital measurement outputs to quantify the quality of a peening process in surface enhancement operation of metal parts. In practice, these parameters can only be measured offline upon process completion, which are not suitable for online tracking and operation. Instead, shot stream velocity can be used as a real-time monitoring parameter to bridge operational inputs to the outputs. As such, a robust and accurate shot stream velocity model is needed for real-time tracking. In this study, we propose a blended practical model for shot stream velocity to address the issues. The model is constructed using regression algorithm based on the blended candidate functions, which are developed from experimental data and nature of the particle-air flow inside the system. Obtained model is validated against experimental data for different conditions. Calculated velocities are in good agreement with the measurements. In addition, applications of the model in predicting the shot stream velocity for different peen types, different peening intensities and coverages. The obtained results are comparable to the measurement data. Furthermore, a single-input and single-output model-based control is developed from proposed shot stream velocity model. The developed control system is robust, accurate and reliable. It implies that the developed model can be used to provide necessary information as well as in optimal process control development to improve and accelerate the peening processes to reduce cost and time of actual productions.

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Smart vibratory peening: An approach towards digitalisation and integration of vibratory special process into smart factories

Gopinath,

Teoh,

Tagade

et al. 2023

Engineering Applications of Artificial Intelligence

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Advanced model-based controller for cyber-physical shot peening process

Cited by 7 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

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

Smart vibratory peening: An approach towards digitalisation and integration of vibratory special process into smart factories

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