Holistic process monitoring with machine learning classification methods using internal machine sensors for semi-automatic drilling

Hintze, Wolfgang; Romanenko, Denys; Molkentin, Lukas; Koettner, Lars; Mehnen, Jan

doi:10.1016/j.procir.2022.05.094

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…We will test the ability of n ∈ [1, 2, 3] agents to learn optimal fixturing positions, whilst examining the presence of an equilibrium position for a 2-player game. A drilling position is selected on the wing panel based on the framework outlined in [25], and a payout graph is constructed for 2 agents and a trained response for 1-3 agents is created in a similar manner to the multi-armed bandit model [18]. In figure 5a, the action numbers for each agent correspond to a fixture on the panel in figure 7, where the payout is the outcome from the Gaussian reward function.…”

Section: A Repeated Matrix Game For Fixture Placementmentioning

confidence: 99%

Decision Making For Multi-Robot Fixture Planning Using Multi-Agent Reinforcement Learning

Canzini,

Noguera,

Pope

et al. 2024

Preprint

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Within the realm of flexible manufacturing, fixture layout planning allows manufacturers to rapidly deploy optimal fixturing plans that can reduce surface deformation that leads to crack propagation in components during manufacturing tasks. The role of fixture layout planning has evolved from being performed by experienced engineers to computational methods due to the number of possible configurations for components. Current optimisation methods commonly fall into sub-optimal positions due to the existence of local optima, with data-driven machine learning techniques relying on costly to collect labelled training data. In this paper, we present a framework for multi-agent reinforcement learning with team decision theory to find optimal fixturing plans for manufacturing tasks. We demonstrate our approach on two representative aerospace components with complex geometries across a set of drilling tasks, illustrating the capabilities of our method; we will compare this against state of the art methods to showcase our method’s improvement at finding optimal fixturing plans with 3 times the improvement in deformation control within tolerance bounds.

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Section: A Repeated Matrix Game For Fixture Placementmentioning

confidence: 99%

Decision Making For Multi-Robot Fixture Planning Using Multi-Agent Reinforcement Learning

Canzini,

Noguera,

Pope

et al. 2024

Preprint

View full text Add to dashboard Cite

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Klassifizierung von Prozesszuständen beim Bohren

Flehmke,

Romanenko,

Rosenthal

et al. 2023

Zeitschrift Für Wirtschaftlichen Fabrikbetrieb

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Etwa ein Drittel aller Nietbohrungen in der Flugzeugstrukturmontage werden unter Einsatz semi-automatischer Bohrmaschinen gefertigt. Diese Maschinen können mit internen Sensoren ausgerüstet werden, um Prozessdaten aufzuzeichnen. In diesem Beitrag werden zuverlässige und effiziente Methoden zur Klassifizierung von Prozesszuständen basierend auf internen Maschinendaten identifiziert. Diese können für die Implementierung einer intelligenten Prozessüberwachung oder zur Anomaliedetektion verwendet werden.

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Holistic process monitoring with machine learning classification methods using internal machine sensors for semi-automatic drilling

Cited by 2 publications

References 9 publications

Decision Making For Multi-Robot Fixture Planning Using Multi-Agent Reinforcement Learning

Decision Making For Multi-Robot Fixture Planning Using Multi-Agent Reinforcement Learning

Klassifizierung von Prozesszuständen beim Bohren

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