Self-learning Processes in Smart Factories: Deep Reinforcement Learning for Process Control of Robot Brine Injection

Andersen, Rasmus Eckholdt; Madsen, Steffen; Barlo, Alexander; Johansen, Sebastian Blegebrønd; Nør, Morten; Bøgh, Simon

doi:10.1016/j.promfg.2020.01.023

Cited by 20 publications

(5 citation statements)

References 3 publications

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“…The needle-based injection process is widely employed in meat processing, in which brine is injected into the muscle using needles under pressure ( Andersen et al, 2019 ). Additionally, the injection of brine can improve the flavor and juiciness of meat products ( Xiong, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

Effect of Different Brine Injection Levels on the Drying Characteristics and Physicochemical Properties of Beef Jerky

Kim¹,

Shin²,

Lee³

et al. 2022

Food Sci Anim Resour

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Section: Introductionmentioning

confidence: 99%

Effect of Different Brine Injection Levels on the Drying Characteristics and Physicochemical Properties of Beef Jerky

Kim¹,

Shin²,

Lee³

et al. 2022

Food Sci Anim Resour

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“…Beyond that, the motivation for applying deep RL is often an inaccurate mapping of conventional methods that cannot adequately cope with non-linearities (as in Lu et al (2016)) or relies too much on error-prone expert knowledge (as in Mazgualdi et al (2021)). With their adaptive and non-discretised action space, deep RL can thus avoid waste, especially in sensitive processes, and keep processes stable, which might be problematic with static or human-based process modelling (Andersen et al 2019).…”

Section: Process Controlmentioning

confidence: 99%

Deep reinforcement learning in production systems: a systematic literature review

Panzer

Bender

2021

International Journal of Production Research

130

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Shortening product development cycles and fully customisable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable high throughputs and provide a high adaptability and robustness to process variations and unforeseen incidents. To overcome these challenges, deep Reinforcement Learning (RL) has been increasingly applied for the optimisation of production systems. Unlike other machine learning methods, deep RL operates on recently collected sensor-data in direct interaction with its environment and enables real-time responses to system changes. Although deep RL is already being deployed in production systems, a systematic review of the results has not yet been established. The main contribution of this paper is to provide researchers and practitioners an overview of applications and to motivate further implementations and research of deep RL supported production systems. Findings reveal that deep RL is applied in a variety of production domains, contributing to data-driven and flexible processes. In most applications, conventional methods were outperformed and implementation efforts or dependence on human experience were reduced. Nevertheless, future research must focus more on transferring the findings to real-world systems to analyse safety aspects and demonstrate reliability under prevailing conditions.

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“…The advantageous use of deep learning methods within the concept of smart manufacturing can happen on many different levels. A good example of this is presented in Andersen et al (2019), where deep reinforcement learning is used for industrial robots to cope with natural variations in the brine injection process during the production of a meat product. The prospect of deep learning application in the field of robotics is further emphasized in Wang et al (2020a) where it is used in a multi-robot scenario.…”

Section: Use Of Deep Learning In the Context Of Industry 40mentioning

confidence: 99%

Computational Intelligence in the Context of Industry 4.0

Hosŏvský

Piteľ

Trojanová

et al. 2021

Implementing Industry 4.0 in SMEs

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Industry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

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Self-learning Processes in Smart Factories: Deep Reinforcement Learning for Process Control of Robot Brine Injection

Cited by 20 publications

References 3 publications

Effect of Different Brine Injection Levels on the Drying Characteristics and Physicochemical Properties of Beef Jerky

Effect of Different Brine Injection Levels on the Drying Characteristics and Physicochemical Properties of Beef Jerky

Deep reinforcement learning in production systems: a systematic literature review

Computational Intelligence in the Context of Industry 4.0

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