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Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model
2022
Foods
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A collaborative optimization model of the MF–EF–IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). ...
To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. ...
Then, a data-driven model of MF-EF-IF is established by using the DK-ELM method. ...
doi:10.3390/foods11233845
pmid:36496653
pmcid:PMC9740788
fatcat:z2yhkqeolvfjzde4256r266dpq
Introduction to the Special Issue "Advances in Computational Intelligence Applications in the Mining Industry"
2022
Minerals
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This is an exciting time for the mining industry, as it is on the cusp of a change in efficiency as it gets better at leveraging data [...] ...
They extract control rules from semi-autogenous grinding (SAG) mill operational data using decision trees. ...
[11] present a data-driven mineral prospectivity model to identify areas with higher discovery potential. ...
doi:10.3390/min12010067
fatcat:lzm2oly4vnf4xjus7bqn4mhfou
Machine Learning in Precision Manufacturing: A Collaborative Computer and Mechanical Engineering Perspective
2023
Dandao xuebao
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Precision manufacturing, characterized by the production of intricate components with stringent tolerances, has witnessed a paradigm shift with the integration of machine learning (ML) techniques. ...
This paper reviews the application of ML in key areas such as predictive maintenance, process optimization, quality control, and adaptive manufacturing strategies. ...
ML algorithms, including unsupervised learning techniques like clustering, are employed to analyze sensor data from grinding processes. ...
doi:10.52783/dxjb.v35.123
fatcat:bi2yx5iuwbhthnsr6i57gapzny
Learning to Shape by Grinding: Cutting-surface-aware Model-based Reinforcement Learning
[article]
2023
arXiv
pre-print
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This paper proposes a cutting-surface-aware Model-Based Reinforcement Learning (MBRL) method for robotic grinding. ...
Through evaluation and comparison by simulation and real robot experiments, we confirm that our MBRL method can achieve high data efficiency for learning object shaping by grinding and also provide generalization ...
With this grinding resistance theory F n = kV t /S g , F t = λkV t /S g , we construct CSDM g(•), using a data-driven approach, to predict the deviation of the cutting surface due to grinding resistance ...
arXiv:2308.02150v1
fatcat:mvwjfrhxfjd3fm5x3ptuu4yzmq
Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning
2021
Sensors
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Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. ...
The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. ...
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21041278
pmid:33670109
pmcid:PMC7916895
fatcat:ehzsevmddfg5zlyc2wms6yuhui
Prediction of Surface Roughness of Abrasive Belt Grinding of Superalloy Material Based on RLSOM-RBF
2021
Materials
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Secondly, an RBF neural network is trained by reinforcement learning of a self-organizing mapping method. ...
Firstly, the grinding system of the superalloy belt is introduced. The effects of the material removal process and grinding parameters on the surface roughness in belt grinding were analyzed. ...
Conclusions First of all, the use of reinforcement learning (RL) improved the SOM method. ...
doi:10.3390/ma14195701
pmid:34640122
pmcid:PMC8510046
fatcat:5liw3p46afar5d6nw4zq3cqcv4
Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments
2019
Complexity
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with deep neural networks and then build prediction model with deep learning neural networks for controller parameters of grinding robot. ...
The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. ...
Acknowledgments The work was supported by the National Science Foundation of China
Supplementary Materials The data sets provided are the images of block surface after grinding taken using vision ...
doi:10.1155/2019/5296123
fatcat:inz7xuanzbalhkk6wpv7x6c5ge
Interface architecture design for minimum programming in human-robot collaboration
2019
Zenodo
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The user interface is associated with use cases. ...
Then, FB, with embedded algorithms and knowledge and driven by events, is to provide a dynamic link to the relevant application interface (APIs) of the functional modules in terms of the case requirements ...
[11] developed a new reinforcement learning algorithm that enables collaborative learning between a robot and a human, by which the robot decides whether to use its performance or that induced by human ...
doi:10.5281/zenodo.2583524
fatcat:a7hv2e2runb7neppdbkf5waxfi
Analysis of Efficient and Fast Prediction Method for the Kinematics Solution of the Steel Bar Grinding Robot
2023
Applied Sciences
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Finally, the Nadam optimizer is used to optimize the calculation results of the example. ...
Aiming at the robotization of the grinding process in the steel bar finishing process, the steel bar grinding robot can achieve the goal of fast, efficient, and accurate online grinding operation, a multi-layer ...
In reference [23] , the authors do not consider reinforcement learning or trial and error methods. ...
doi:10.3390/app13021212
fatcat:bxousln5n5dflgfrljfqbp2x74
Table of Contents
2022
IEEE Robotics and Automation Letters
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Trzcinski Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning . . . . . .R. Cimurs, I. H. Suh, and J. H. ...
via Data-Driven Model Inversion . . . . . . . . . . . . . ...
doi:10.1109/lra.2022.3165102
fatcat:enjzebowe5hn7hsfwklc7nieuy
Table of Contents
2020
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
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of Industrial Grinding Circuits using Machine Learning Ravi kiran Inapakurthi, Srinivas Soumitri Miriyala, Suryanarayana Kolluri and Kishalay Mitra .......... 1921 Evolving Spiking Neurocontrollers for ...
Maps Wilson Tsakane Mongwe and Katherine Mary Malan .......... 1100 A Novel Algorithmic Trading Strategy Using Data-Driven Innovation Volatility You Liang, Aerambamoorthy Thavaneswaran and Md. ...
doi:10.1109/ssci47803.2020.9308155
fatcat:hyargfnk4vevpnooatlovxm4li
Special Issue on "Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes"
2021
Processes
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A reinforcement learning approach was used to find out optimal actions using online data in the process level, and a coarse model was developed to evaluate action values. ...
[13] , as one of the powerful model-free deep reinforcement learning algorithms, the deep deterministic policy gradient algorithm was used to track the target boost pressure under transient driving cycles ...
doi:10.3390/pr9040664
fatcat:utt4iwfzandw5jujch5aeclzwy
Artificial Intelligence : from Research to Application ; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2019)
[article]
2019
arXiv
pre-print
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The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students. ...
In this paper we apply Reinforcement Learning to the computer game Breakout and present a small software framework which can be used to implement a Reinforcement Learning algorithm. ...
Introduction Reinforcement Learning is a paradigm of machine learning. ...
arXiv:1903.08495v1
fatcat:3to4wspv3zc6dak6ogjpud5nkq
A Review of End-Effector Research Based on Compliance Control
2022
Machines
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This paper describes the design and research results of different end-effectors under impedance-based control, hybrid force/position control, and intelligent flexible control methods, respectively. ...
Under each control method, the structural characteristics and the optimized control scheme under different drives are introduced. ...
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/machines10020100
fatcat:d4rle63enfehfisxglyjly46eq
Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems
2022
Applied Sciences
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The aim of the present perspective paper is to provide an overview of novel applications of AI methods to industrial control systems on different levels, so as to improve the production systems' self-learning ...
capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production ...
[63] compared reinforcement learning with model predictive control algorithms. ...
doi:10.3390/app122110962
fatcat:7pyyi5vyrrelbcuzpkl5nvrbvq
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