AI-based Framework for Deep Learning Applications in Grinding

Kaufmann, Tobias; Sahay, Sanjay K.; Niemietz, Philipp; Trauth, Daniel; Maas, Wolfgang; Bergs, Thomas

doi:10.1109/sami48414.2020.9108743

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…Merging different sensors' signals and correlating signal characteristics to tool wear and work piece quality can be used for training algorithms, as shown by Möhring in [39]. With high computational power for teaching and training, artificial intelligence and machine learning can be addressed [40]. Thus, simple rule engines for time-sensitive systems (Fig.…”

Section: Fig 2 Overview Of Benefits By Sensor Integrationmentioning

confidence: 99%

Tooling systems with integrated sensors enabling data based process optimization

Bleicher¹,

Ramsauer²,

Leonhartsberger³

et al. 2021

Journal of Machine Engineering

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Sensor integration into machining equipment has become an important factor for gaining deep process insights mainly driven by increasingly smaller and cheaper sensors and transmitters. Due to advances in microelectronics and communication technology, a broader field of applications in production processes and machine tools can be addressed using sensing devices and their implementation potentials. Ensuring a sensitive but robust data stream from close to the actual process allows not only reliable monitoring but also process and quality control based on sensor information. This paper provides an overview of the utilization of sensor data for the purpose of condition monitoring, model fitting and real-time control coping with stochastic effects. Examples of sensor integration in fields of injection molding, roll forming and heavy-duty milling comprise the state of the art of sensor implementation, data evaluation and possible feedback loops in the respective application scenarios.

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Section: Fig 2 Overview Of Benefits By Sensor Integrationmentioning

confidence: 99%

Tooling systems with integrated sensors enabling data based process optimization

Bleicher¹,

Ramsauer²,

Leonhartsberger³

et al. 2021

Journal of Machine Engineering

View full text Add to dashboard Cite

show abstract

“…For the prediction, sensors are often additionally integrated to measure the displacement of the working shaft [9] or to measure the acoustic emission [10]. An extension to predicting tool forces in automated grinding is the use of virtual sensors, whose measured values are predicted from physical quantities [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning

Dörr

Ott

Matthiesen

et al. 2021

Sensors

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Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development.

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Towards an Adaptive Production Chain for Sustainable Sheet-Metal Blanked Components

Niemietz

Kaufmann

Unterberg

et al. 2020

Lecture Notes in Production Engineering

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AI-based Framework for Deep Learning Applications in Grinding

Cited by 8 publications

References 21 publications

Tooling systems with integrated sensors enabling data based process optimization

Tooling systems with integrated sensors enabling data based process optimization

Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning

Towards an Adaptive Production Chain for Sustainable Sheet-Metal Blanked Components

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