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How Deep Learning is Implemented in Manufacturing Quality control 3

How Deep Learning is Implemented in Manufacturing Quality Co...

引用

3rd International conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Li, Dongjin Chen, Xinyan Tan, Peiwen Jia, Jie School of Electronic Information Shanghai Dian Ji University Shanghai201306 China

ISBN: (纸本)9798350365443

This study aims to explore the application and effect of deep learning technology in manufacturing quality control. The study deployed a convolutional neural network (CNN) model to analyze images and sensor data during the manufacturing process to achieve automatic detection and classification of defects. The experiments were conducted in a simulated high-speed production line environment, and a large amount of production data, including images and sensor readings, were collected and processed. The results showed that the accuracy of the deep learning model in identifying serious, moderate and minor defects reached 98%, 95% and 90% respectively, which was significantly higher than the traditional quality control system. The conclusion shows that deep learning technology can significantly improve the quality control accuracy and efficiency of the manufacturing industry, provide strong technical support for the field of intelligent manufacturing, and indicate broad application prospects in advanced manufacturing systems that require higher levels of automation and intelligence. © 2024 IEEE.

关键词： Smart manufacturing

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Filter Pruning Based on Coordination of Layers for Deep Neural Networks 43

Filter Pruning Based on Coordination of Layers for Deep Neur...

引用

43rd Chinese control conference, CCC 2024

作者： Luan, Xing Zhang, Liyong Dalian University of Technology School of Control Science and Engineering Dalian116024 China

ISBN: (纸本)9789887581581

Convolutional neural networks have achieved remarkable success in the field of computer vision. However, deploying them to edge devices is challenging due to their numerous parameters and computational complexity. In this paper, we propose a new network pruning method called Filter Pruning based on Coordination of Layers (FPCL), which reduces the size of the network by pruning some relatively unimportant filters while ensuring low accuracy loss. In order to maintain the coordination of the feature extraction by filters in the adjacent network layers, we take the influence of the filter on the loss function during the images of dataset passing through the network as the attribute of the filter, cluster filters in the adjacent layers, and then prune the filters in the cluster. During pruning, we ensure that filters from different layers within the same cluster are properly preserved. To avoid the loss of model generalization after pruning, we fine-tune the pruned model and then re-evaluate all filters, including the pruned ones, to prune again. The process of pruning and fine-tuning is iterated multiple times, with an increasing number of filters pruned each time, until the desired number of pruned filters is reached. To validate the effectiveness of the FPCL method, we pruned various models on multiple datasets. Compared to other advanced methods, the pruned model by FPCL achieves higher testing accuracy with fewer parameters. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Convolutional neural networks

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Ensemble-based Offline Data-driven Evolutionary Optimization for Operation Parameter of Blast Furnace 20

Ensemble-based Offline Data-driven Evolutionary Optimization...

引用

20th International conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ye, Qiucheng Cai, Hongxia Bian, Yunqi School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (纸本)9798350356328

Stable control of the iron-making process through the adjustment of operating parameters is essential to improve blast furnace productivity. However, due to the complex and dynamic nature of the reaction process, it is difficult to perform accurate modeling and simulation experiments for a blast furnace. This poses great difficulties in performing the evaluation of control strategies in real-time. Offline evolutionary optimization, as an optimization technique that relies only on historical data, enables the search for optimal operation parameters through the introduction of surrogates. Based on the characteristics of historical time-series data of the iron-making process, this study proposes an offline evolutionary optimization method based on advanced time-series surrogates. Ensemble strategy combining surrogates with different prediction time windows contributes robustness during the optimization process. Additionally, multiple sub-regions are maintained to enable extensive searching of the decision space with a multi-objective nature. The efficiency of the proposed pooling method is demonstrated through verification in actual production cases. © 2024 IEEE.

关键词： Blast furnaces

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Feedforward control of flat hybrid automata: a behavioral systems theory approach 63

Feedforward control of flat hybrid automata: a behavioral sy...

引用

63rd IEEE conference on Decision and control, CDC 2024

作者： Zahn, Frederik Kleinert, Tobias Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Karlsruhe Germany Rwth Aachen University Chair of Process Control Engineering Aachen Germany

ISBN: (纸本)9798350316339

Differential flatness has been defined in the literature for continuous time dynamical systems and for discrete time systems. We define flatness of automata from the perspective of behavioral systems theory, and synthesize a flatness definition for hybrid automata. These new definitions allow for feedforward trajectory generation for flat hybrid automata. Additionally, we provide necessary and sufficient conditions of hybrid automaton flatness for different types of continuous state constraints and switching conditions, and how to compute whether a hybrid automaton is flat. We illustrate feedforward trajectory generation based on our findings for a robotic manipulator example. © 2024 IEEE.

关键词： Continuous time systems

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advanced Model Performance Index in the Learning process of Class-Imbalanced Fatigue Classification 14

Advanced Model Performance Index in the Learning Process of ...

引用

14th IEEE International conference on Cyber Technology in automation, control, and Intelligent Systems, CYBER 2024

作者： Liu, Yanchen Qu, Zijia Lai, King Wai Chiu City University of Hong Kong Department of Biomedical Engineering Hong Kong

ISBN: (纸本)9798331506056

In artificial intelligence (AI), class imbalance poses a significant challenge in real-world classification tasks, such as weight training. In weight training, human body primarily operates in a state of non-fatigue, with only a small portion experiencing fatigue at the end of exercises. This consistent imbalance in sample quantities between fatigued and non-fatigued statuses poses a challenge for classifiers in discerning fatigue status. Traditionally, assessing AI training accuracy throughout model training process is common for achieving a desired classifier, but this method is inaccurate in class imbalance conditions. Therefore, we have proposed a new evaluation index for model training, accounting for class imbalance. In the Fatigue and Non-Fatigue classification tasks, we used a novel Model Performance Index (MPI) and evaluated its performance when training an AI network model. This index considers current model performance and class imbalance impacts simultaneously. In the experiments, this evaluation method more accurately captures the true capability of the training classifier compared to conventional metrics like accuracy. When the classifier exhibits superior performance, the MPI score tends to be higher and shows a tendency towards smoothness during the learning process. Conversely, when the model performance is inferior and influenced significantly by training data biases, the MPI score is lower, and exhibits oscillations. © 2024 IEEE.

关键词： Contrastive Learning

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Wiring Simulation of Electric control Cabinet Based on Industrial Robot 16th

Wiring Simulation of Electric Control Cabinet Based on Indus...

引用

16th International conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Zhao, Chaoyue Liu, Yida Lu, Zongxing School of Mechanical Engineering and Automation Fuzhou University No. 2 Xueyuan Road Fujian Fuzhou350116 China School of Advanced Manufacturing Fuzhou University Shuicheng Road Fujian Jinjiang362251 China

ISBN: (纸本)9789819965007

Cabinet wiring is a crucial process in the field of electrical engineering. Traditional wiring methods are labor-intensive, time-consuming, and prone to safety hazards. Consequently, robot wiring technology has emerged as a promising solution. This paper investigates how to simulate the cabinet wiring robot using Robot Studio software. The rise of robot wiring technology has brought about novel automation solutions to the field of electrical engineering, improving work efficiency and reducing safety risks. In this automated wiring process, we achieve cabinet automation through the control of upper and lower computers and validate the wiring effectiveness and feasibility of the robot through simulation. During the simulation process, we analyze the relevant parameters of setting reasonable robot offline programming to achieve optimal automation wiring results. This paper aims to provide a novel solution for cabinet wiring work and promote the development and application of automation technology in electrical engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robot programming

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A Small 3D Printed Robotic Arm for Teaching Industry 4.0 and Robotic Engineering 20

A Small 3D Printed Robotic Arm for Teaching Industry 4.0 and...

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IEEE 20th International conference on automation Science and Engineering (CASE)

作者： Surynek, Pavel Czech Tech Univ Fac Informat Technol Thakurova 9 Prague 16000 6 Czech Republic

ISBN: (纸本)9798350358513;9798350358520

Industrial robotic arms with at least six degrees of freedom, a key component of Industry 4.0, even small ones, are still relatively inaccessible to a wider range of users, experimenters, and educators due to their high cost and the need to ensure operational safety. Moreover, the industrial robotic arms are often closed systems, which makes these machines unsuitable for mechanical modifications and teaching robotics engineering. We therefore took a different path, namely we wanted to construct our own small desktop robotic arm with as much of our own creation as possible and everything as opensource so that students and other researchers could freely use our work and modify it. 3D printing technology is a perfect fit for us, which is currently so advanced that it makes 3D printing a viable manufacturing process for prototyping and manufacturing various products on a small scale, including small machines, and also mechanical parts of robotic arms. From our point of view, we consider 3D printing as a tool for making robotic engineering accessible to a wide group of students. In this article, we describe the design and construction of an affordable yet powerful robotic arm with 6 (+1 for the end effector) degrees of freedom, which we named Real Robot One (RR1 in short). In the design of the RR1, we emphasized the greatest possible use of locally produced parts (especially bearings and other non-printed parts). Since the RR1 is much smaller than industrial robotic arms, the safety costs are eliminated with our robot - it is completely safe to be in touch with the robot during operation. Our robot can serve as a teaching aid for preparing experts for Industry 4.0 as a small model of a production line consisting of these robots can be set up in a small laboratory.

关键词： robotic arm 3D-printing robotics engineering robotics teaching robot control Industry 4.0

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Approximated Explicit NMPC via Reinforcement Learning for Homomorphically Encrypted process control 63

Approximated Explicit NMPC via Reinforcement Learning for Ho...

引用

63rd IEEE conference on Decision and control, CDC 2024

作者： Dzurkova, Diana Valabek, Patrik Meszaros, Oliver Kaluz, Martin Klauco, Martin Institute of Information Engineering Automation Mathematics Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (纸本)9798350316339

This research proposes a novel approach to generating explicit, nearly-optimal (suboptimal) control policies in the form of neural networks with a structure that allows further mathematical operations within homomorphic encryption frameworks. The novelty of this paper also lies in presenting a reinforcement learning pathway to train the explicit control law without the necessity of prior model knowledge. A Deep Deterministic Policy Gradient algorithm is used to train the neural network, with the objective function adopted from nonlinear model predictive control. This paper presents a generalized methodology to train the control policy and evaluate it in a homomorphic encryption setup. Particular results are presented based on a software-in-the-loop simulation setup, where specifics like communication delays and computational overheads are considered. © 2024 IEEE.

关键词： Reinforcement learning

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Monitoring And control System for pH and Temperature of Water Quality on The Vertical Mud Crab Cultivation

Monitoring And Control System for pH and Temperature of Wate...

引用

2023 International conference on advanced Mechatronics, Intelligent Manufacture and Industrial automation, ICAMIMIA 2023

作者： Zein, Muhammad Ikhsanuddin Mujiyanti, Ir. Safira Firdaus Widya Pratama, Ir. I Putu Eka Darmawan, Tiffany Rachmania Lokeswara, Rajendra Institut Teknologi Sepuluh Nopember Departement of Instrumentation Engineering Surabaya Indonesia

ISBN: (纸本)9798350309225

The success parameters of mud crab cultivation can be seen from the temperature, pH, and ammonia levels contained in the water. Basically the monitoring process for the 3 parameters mentioned above is done manually. Therefore, in this final project, a control and monitoring system will be created with parameters of temperature, pH, and ammonia levels to help cultivators to create an effective and efficient system. The water quality values are as follows, temperature with an optimum value of 29 °C and with a range of 25-35 °C, with an optimum value of 25 ppt and with 20-28 ppt, pH with an optimum value of 7 and with a range of 7-8 and for an optimum value of ammonia in mud crab 0.25 ppm. At the time of making the tool, sensor validation and system performance testing were carried out in controlling the parameters of temperature, pH, and ammonia levels. The on-off control system is used in the system to activate the relay so that the pump and heater can be controlled according to the set-point that has been input in the application. © 2023 IEEE.

关键词： Water quality

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Design of Efficient Orienting Machinery System for Oil Bottle Position Selection Line with Fuzzy Logic control

Design of Efficient Orienting Machinery System for Oil Bottl...

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2023 International conference on advanced Mechatronics, Intelligent Manufacture and Industrial automation, ICAMIMIA 2023

作者： Aliffianto, Lutfir Rahman Budiprayitno, Slamet Priananda, Ciptian Weried Wicaksono, Ilham Agung Adhim, Fauzi Imaduddin Wardhana, Mochamad Raihan Dwi Rahayu, Lucky Putri Musthofa, Arif Suwondo, Heru Institut Teknologi Sepuluh Nopember Electrical Automation Engineering Surabaya Indonesia Pt Puspetindo Gresik Indonesia

ISBN: (纸本)9798350309225

In the oil filling production process, there are obstacles at the oil bottle position orientation selection stage, namely the position of the oil bottle which is still upside down or dropped. The cause of the obstacle is when detecting the orientation of the bottle with a tilted or dropped position, and also the unstable speed of the conveyor. These problems can affect the next production process, resulting in maintenance on the machine and production targets not being achieved. In solving this problem, an oil bottle position orientation detection is built with fuzzy logic control, the detection uses a combination of 3 infrared sensors. The placement of the three infrared sensors adjusts the shape of the bottle in order to detect the orientation of the bottle position, fuzzy logic control is obtained through the voltage output of the three sensors combined so that it can classify the orientation of the bottle position whether upside down, down, or in the same direction. If the orientation of the bottle position is detected to be upside down, it will move the stepper motor as a bottle orienting, and if the orientation of the bottle position is detected to have fallen, it will move the servo motor as a bottle sorter. Designing a system with fuzzy logic control can reduce the presence of inverted or dropped bottle positions, and also the tilted bottle position does not affect the detection of bottle position orientation. Assuming the entry of the bottle is intentionally upside down and falling, the orienting system can achieve 42% success and the sorting system can achieve 90% success, therefore the system can reduce the occurrence of maintenance caused by the position of the bottle upside down or falling. © 2023 IEEE.

关键词： Fuzzy logic

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