Tooling systems with integrated sensors enabling data based process optimization

Bleicher, Friedrich; Ramsauer, Christoph; Leonhartsberger, Martin; Lamprecht, Matthias; Stadler, Philipp; Strasser, Dominik; Wiedermann, Clemens

doi:10.36897/jme/134244

Cited by 6 publications

(2 citation statements)

References 73 publications

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“…For this, it is necessary to develop sensors, capable of detecting a wide variety of factors, to acquire information about the state of processes and their components [5]. This, in turn, will enable multi-criteria optimization of processes and more efficient use of resources [6,7]. Machines and technological equipment will have to function in readiness for rapid reconfiguration (reconfiguration).…”

Section: Foundations Of Metrology 40mentioning

confidence: 99%

Towards Metrology 4.0 in Dimensional Measurements

Wieczorowski¹,

Trojanowska²

2023

Journal of Machine Engineering

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The paper presents the transformations taking place in length and angle metrology related to Metrology 4.0, a measurement strategy resulting from Industry 4.0. Metrology in an industrial conditions is gradually focusing more and more on advanced measurement systems. The coming reality will see the development of communication between systems and their components, as well as the individual sensors belonging to them. The Internet of Things and artificial intelligence as well as the possibility of using augmented or virtual reality will play a momentous role. The demand for these technologies results in the development of new specialized software and hardware solutions, the use and availability of which are diametrically different compared to the past. Also, the use of AI and cybersecurity in metrology is a topic that is receiving increasing attention. Metrology 4.0 is therefore becoming a very important part of the functioning of industry, changing the philosophy and organization of measurements carried out on the basis of new measurement techniques.

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Section: Foundations Of Metrology 40mentioning

confidence: 99%

Towards Metrology 4.0 in Dimensional Measurements

Wieczorowski¹,

Trojanowska²

2023

Journal of Machine Engineering

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“…Setting countermeasures by adapting cutting parameters or sending feedback to higher level monitoring systems is possible so far [13]. So even networks with decentral data processing or cloud services can be addressed [14] to enable data-based process optimization [15].…”

Section: Introductionmentioning

confidence: 99%

Primary Testing of an Instrumented Tool Holder for Brush Deburring of Milled Workpieces

Ramsauer¹,

Oswald²,

Schörghofer³

et al. 2022

Journal of Machine Engineering

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Brush deburring requires consistent contact pressure between brush and workpiece. Automating adjustments to control contact pressure has proven difficult, as the sensors available in machine tools are usually not suitable to observe the small amplitude signals caused by this low force process. Additionally, both the power consumption and the vibration signal caused by the process strongly depend on the workpiece surface features. This paper describes a test setup using an instrumented tool holder and presents the corresponding measurement results, aiming to quantify the axial feed of the brush. It also discusses the interpretation of different signal components and provides an outlook on the utilization of the data for tool wear estimation.

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Machine learning based operator assistance in roll forming

Hofmann,

Becker,

Kubik

et al. 2024

Prod. Eng. Res. Devel.

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This paper presents an approach for operator assistance in roll forming to overcome the challenges of progressive skilled labor shortage faced by manufacturers of profiled products. An introductory study proves the necessity and the willingness of the roll forming industry to use process data and machine learning based assistance for less experienced operators. A newly built framework contains the characterization of process behavior based on in-line collected data. To support operators during the setup and control of complex manufacturing processes, correlations between tool adjustments and process data are analyzed in a machine learning (ML) pipeline. Setup suggestions are directly provided to the operator for implementation and a feedback loop takes the results into account. To quantify the functionality of the newly developed Machine Learning based Operator Assistance (MLbOA), an exemplary roll forming process is investigated. The system localizes maladjustments in the setup of tool gaps caused by individual mechanical load behavior and offers corrective suggestions to operators with a mean absolute error of 1.26 ± 0.36 μm. This work demonstrates the potential of machine learning based assistance systems to enhance the resilience of manufacturing processes against the challenges posed by the shortage of skilled labor.

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Tooling systems with integrated sensors enabling data based process optimization

Cited by 6 publications

References 73 publications

Towards Metrology 4.0 in Dimensional Measurements

Towards Metrology 4.0 in Dimensional Measurements

Primary Testing of an Instrumented Tool Holder for Brush Deburring of Milled Workpieces

Machine learning based operator assistance in roll forming

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