Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning

Günther, Johannes; Pilarski, Patrick M.; Helfrich, Gerhard; Shen, Hao; Diepold, Klaus

doi:10.1016/j.mechatronics.2015.09.004

Cited by 148 publications

(87 citation statements)

References 31 publications

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“…Tasks included pushing a block, spooning material into a bowl, scooping with a spatula, and hanging a loop of rope on a hook. Günther, Pilarski, Helfrich, Shen, and Diepold [116,117] designed a DNN to automatically create meaningful feature vectors. The network was able to extract lowdimensional features from high-dimensional camera images of welds in a laser welding process.…”

Section: Examples In Recent Researchmentioning

confidence: 99%

Deep learning in robotics: a review of recent research

2017

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Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.

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Section: Examples In Recent Researchmentioning

confidence: 99%

Deep learning in robotics: a review of recent research

2017

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“…Many other disciplines of artificial intelligence, such as the processing of natural language or robotics, whose intelligent behavior presupposes a broad knowledge base, are based on this. There are various industrial applications where ML algorithms are applied with promising results [5]: In [6] an ML algorithm is implemented to control the process parameter power in a laser welding process. The experimental results for a particular setup show that the algorithm generates stable solutions and is suitable for a real-time and dynamic control mechanism.…”

Section: In Production Planning and Controlmentioning

confidence: 99%

Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems

Kuhnle¹,

Lanza²

2018

Machine Learning for Cyber Physical Systems

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Cyber Physical Production Systems (CPPS) provide a huge amount and variety of process and production data. Simultaneously, operational decisions are getting ever more complex due to smaller batch sizes (down to batch size one), a larger product variety and complex processes in production systems. Production engineers struggle to utilize the recorded data to optimize production processes effectively. In contrast, CPPS promote decentralized decision-making, so-called intelligent agents that are able to gather data (via sensors), process these data, possibly in combination with other information via a connection to and exchange with others, and finally take decisions into action (via actors). Modular and decentralized decision-making systems are thereby able to handle far more complex systems than rigid and static architectures. This paper discusses possible applications of Machine Learning (ML) algorithms, in particular Reinforcement Learning (RL), and the potentials towards an production planning and control aiming for operational excellence.

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“…Recently, it has emerged as a new engineering discipline [15]. The application of cognitive control was proposed by Professor Simon Haykin in 2012, and at present, cognitive control is still in an early stage of development [16]. However, the concept of cognitive control has been successfully applied on the cognitive radio and the cognitive radar [17].…”

Section: Overview Of Cognitive Controlmentioning

confidence: 99%

A cognitive control approach to interference mitigation in communications-based train control (CBTC) co-existing with passenger information systems (PISs)

Wang

Hua-shen

2017

J Wireless Com Network

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As a key component of urban rail transit systems, communications-based train control (CBTC) is an automated train control system using train-ground communications to ensure efficient operation of rail vehicles. In addition to CBTC systems, passenger information systems (PISs) are adopted in urban rail transit systems to improve quality of service (QoS) offered to customers. The interference between CBTC systems and PISs is an important factor impacting QoS of both CBTC systems and PISs. With recent advances in cognitive dynamic systems, in this paper, we take a cognitive control approach to interference mitigation considering the co-existence of CBTC systems and PISs. In our cognitive control approach, the notion of information gap is adopted to quantitatively describe effects of interference on CBTC. The wireless channel is modeled as a finite-state Markov chain with multiple state transition probability matrices, which are derived from real field measurements. Simulation results show that the proposed cognitive control approach can significantly improve performance of CBTC train-ground communications under interference from co-existing PISs.

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Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning

Cited by 148 publications

References 31 publications

Deep learning in robotics: a review of recent research

Deep learning in robotics: a review of recent research

Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems

A cognitive control approach to interference mitigation in communications-based train control (CBTC) co-existing with passenger information systems (PISs)

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