Model Predictive Control in Milling based on Support Vector Machines

Ay, Muzaffer; Stemmler, Sebastian; Schwenzer, Max; Abel, Dirk; Bergs, Thomas

doi:10.1016/j.ifacol.2019.11.462

Cited by 18 publications

(8 citation statements)

References 11 publications

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“…They manipulated the feed velocity in order to achieve a constant force in this highly dynamic process. Later, a black box model (support vector regression (SVR)) was added to consider non-linearities of machining centers [7,8].…”

Section: Manufacturingmentioning

confidence: 99%

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Review on model predictive control: an engineering perspective

Schwenzer

Bergs

et al. 2021

Int J Adv Manuf Technol

Self Cite

319

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Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

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Section: Manufacturingmentioning

confidence: 99%

“…To apply linear control even to non-linear systems successive lineraization can be used, e.g. [6,8,63,80,97,147], or model switching, e.g. [95].…”

Section: Controller Design and Tuningmentioning

confidence: 99%

Review on model predictive control: an engineering perspective

Schwenzer

Bergs

et al. 2021

Int J Adv Manuf Technol

Self Cite

319

View full text Add to dashboard Cite

show abstract

“…SVM may be also applied to model and control the trajectory [92] and several MT parameters, such as the tool velocity, the roughness generation (90% of accuracy) and the part features (e.g., diameter) prediction (97% of accuracy), as demonstrated in Reference [93]. The models are accurate enough to provide useful conclusions applicable to the current industrial practices.…”

Section: Modelingmentioning

confidence: 99%

Recent Advances on Machine Learning Applications in Machining Processes

2021

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This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

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“…This is crucial for the cutting application due to the destructive nature of the tasks being executed. Although this approach is not new ( Potoc̆nik and Grabec, 2002 ), it has been the subject of continued and recent exploration due to its applicability to a wide range of tasks ( Williams et al, 2017 ; Nagabandi et al, 2018 ; Ay et al, 2019 ; Chen et al, 2019 ; Mitsioni et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…More closely related to our case study of the battery cutting application, Ay et al (2019) explored optimization of the milling process feed velocity based on a support-vector machine dynamic model. In this case, improvements in both productivity and modeling accuracy were achieved over an existing empirical model-based method.…”

Section: Introductionmentioning

confidence: 99%

Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

Rastegarpanah

Hathaway

2021

Front. Robot. AI

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The control of the interaction between the robot and environment, following a predefined geometric surface path with high accuracy, is a fundamental problem for contact-rich tasks such as machining, polishing, or grinding. Flexible path-following control presents numerous applications in emerging industry fields such as disassembly and recycling, where the control system must adapt to a range of dissimilar object classes, where the properties of the environment are uncertain. We present an end-to-end framework for trajectory-independent robotic path following for contact-rich tasks in the presence of parametric uncertainties. We formulate a combination of model predictive control with image-based path planning and real-time visual feedback, based on a learned state-space dynamic model. For modeling the dynamics of the robot-environment system during contact, we introduce the application of the differentiable neural computer, a type of memory augmented neural network (MANN). Although MANNs have been as yet unexplored in a control context, we demonstrate a reduction in RMS error of ∼21.0% compared with an equivalent Long Short-Term Memory (LSTM) architecture. Our framework was validated in simulation, demonstrating the ability to generalize to materials previously unseen in the training dataset.

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Model Predictive Control in Milling based on Support Vector Machines

Cited by 18 publications

References 11 publications

Review on model predictive control: an engineering perspective

Review on model predictive control: an engineering perspective

Recent Advances on Machine Learning Applications in Machining Processes

Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

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