Development of a stability intelligent control system for turning

Frumuşanu, Gabriel; Constantin, Ionuţ; Marinescu, V.; Epureanu, Alexandru

doi:10.1007/s00170-012-4074-7

Cited by 24 publications

(13 citation statements)

References 23 publications

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“…Mei et al [119] varied the rake and clearance angles of the cutting tool in order to eliminate the onset of chatter. Recently, Frumusanu et al [120] presented a stability intelligent control technique for turning. The technique involved online monitoring of the cutting force signal.…”

Section: Chatter Suppression/control Techniquesmentioning

confidence: 99%

A review of chatter vibration research in turning

Siddhpura

Paurobally

2012

International Journal of Machine Tools and Manufacture

394

150

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Section: Chatter Suppression/control Techniquesmentioning

confidence: 99%

A review of chatter vibration research in turning

Siddhpura

Paurobally

2012

International Journal of Machine Tools and Manufacture

394

150

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“…To do so, they installed a linear variable differential transformer and acoustic emission sensors on the lathe machine, demonstrating that on-line workpiece quality monitoring and control can be performed with Ethernet and Internet connections. Along the same lines, Frumusanu et al [9] presented a stability control system for in-process modification, to avoid chatter vibrations and to achieve the highest process performance in turning operations on an open CNC transversal lathe. By doing so, they were able to maintain performance and to avoid chatter vibrations for a fixed turning process.…”

Section: Data Acquisition Platforms In Iot Solutionsmentioning

confidence: 99%

Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution

Garrido-Labrador

Puente-Gabarri²,

Ramírez-Sanz

et al. 2020

Applied Sciences

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The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.

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“…Lin and Hu [21] proposed an approach in which the feed-rate and spindle speed were varied to suppress chatter. Frumusanu et al [22] developed a stable intelligent control technique for turning and the technique involved online monitoring of the cutting force signal. Emad Al-Regib et al [23] presented a method for programming spindle speed variation for machine tool chatter suppression and verified the effectiveness experimentally.…”

Section: Machining Vibrationmentioning

confidence: 99%