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54th CIRP Conference on Manufacturing Ssystems, CMS 2021

作者： Karagiannis, Panagiotis Xanthakis, Vangelis Apostolopoulos, George Michalos, George Makris, Sotiris Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras26504 Greece

This paper presents a robotized cell that has been developed to automate the quality control process. The combination of innovative software and hardware helps the operators to efficiently perform quality control on parts from the white goods industry. automation tools, such as robots and mechatronic devices have been integrated on a backend platform which controls and manages the different operations of the resources. These tools perform the quality control on the part, allowing the backend platform to receive the respective signals and ensure its correct functionality. A demonstrator has been setup and described as a use case of the proposed quality control mechanism. © 2021 The Author(s).

关键词： Quality control

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An Augmented Reality Framework for Visualization of Internet of Things Data for Process Supervision in Factory Shop-Floor

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Procedia CIRP 2022年 107卷 1162-1167页

作者： Siatras Vasilis Nikolakis Nikos Alexopoulos Kosmas Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras 26504 Greece

Modern industrial environments provide a high level of digitalization, enabling great volumes of information to be used for shopfloor monitoring and control purposes. In cases where human operators participate in the production process, it is important to support operator’s intervention with Internet of Things (IoT) data and empower process supervision with constant information over Key Performance Indicators (KPIs). Towards this end, the proposed framework exploits Augmented Reality (AR) for the provision of an IoT data visualization solution to support KPIs based process supervision in the smart factory environment. The solution utilizes IoT-enabled components, in order to receive dynamic information over the status of sensors and resources spread across the factory shop-floor. The AR application facilitates the need for visualization of information available on the network, on the actual shopfloor components, which offer transparency and ease the supervision of production processes. The development of the AR and cloud API has been based on CSharp and Java, in a reusable framework that can be reused for similar industrial cases. The reconfigurability and reuse of the AR solution is supported by a template configuration, allowing industry practitioners to configure application features and external connections without any additional software development. The solution was validated on a thermoforming process supervision case of a white goods production system. The main goal was to use the AR framework to support operators during the process parameters design phase, and dynamic supervision of the shopfloor environment. It is demonstrated that the proposed system can be used for KPIs monitoring of the production cycles and for empowering process supervision and optimization.

关键词： Augmented Reality Data Visualization Process Supervision Internet of Things Digitalization of industry

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A Human Robot Collaborative Cell for automating NDT inspection processes

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Procedia CIRP 2022年 115卷 214-219页

作者： Katerina Bakopoulou George Michalos Konstantinos Mparis Christos Gkournelos Nikos Dimitropoulos Sotiris Makris University of Patras Department of Mechanical Engineering and Aeronautics Laboratory for Manufacturing Systems and Automation 26500 Patras Greece

This paper discusses a human robot collaborative application for the automation of NDT inspection processes of large parts. The system includes a movable structure incorporating a cobot with an additional axis for higher reachability and a control system that can generate robot scanning trajectories for large surfaces. The preliminary analysis indicates that the system can undertake the monotonous and time-consuming scanning process, allowing the NDT experts to focus on the review and interpretation of scanning results. A case study in the steel production sector is used to evaluate the benefits of such systems.

关键词： NDT ROS human robot collaboration offline program generation robot programming steel industry Flask UI HMI

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A decision support method for knowledge-based Additive Manufacturing process selection 54

A decision support method for knowledge-based Additive Manuf...

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54th CIRP Conference on Manufacturing Ssystems, CMS 2021

作者： Bikas, Harry Porevopoulos, Nikolas Stavropoulos, Panagiotis Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras26504 Greece

Additive Manufacturing (AM) technologies and materials are more mature than ever;however, industrial AM use is still low. Lack of knowledge among potential users is a key barrier to AM uptake. There is therefore a significant need for methods and tools that will enable potential users to effectively identify the most appropriate materials and subsequently select the AM process that best fits their techno-economic requirements. This work presents a method for assisting potential users in the evaluation and process selection for AM. The method comprises four distinct Steps. Step 1 regards material selection, Step 2 examines AM process suitability, and Step 3 searches for suitable machines. The combined output of Step 1, Step 2, and Step 3 consists of several alternative paths, which are subsequently evaluated and classified in Step 4, based on multiple user-defined criteria. © 2021 The Author(s).

关键词： 3D printers

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Multi-Body dynamic simulation of a machining robot driven by CAM

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Procedia CIRP 2022年 107卷 764-769页

作者： Panagiotis Stavropoulos Christos Gerontas Harry Bikas Thanassis Souflas Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras 26504 Greece

The use of industrial robots for milling processes has been targeted by many industries lately, as they are able to significantly increase the flexibility and lower the production cost over conventional CNC machines. However, the serial, open kinematic chain of industrial robots leads to a compromised structural stiffness, which in turn affects machining accuracy. Apart from that, the dependency of robot dynamic behavior on its posture requires time and cost intensive simulations and experiments to determine its dynamic behavior over its whole working envelope by using traditional methods. To this end, this work aims to develop an integrated model using the Multi-Body Simulation method for a machining robot, considering the elastic behavior of both the joints and the links. The links are modelled through the Finite Elements Method and the Craig-Bampton method is used to reduce the size of the model and enhance the computational efficiency. Moreover, the simulation is linked with a commercial CAM software to enable a rapid evaluation of the expected deflections over the programmed toolpaths and assist the process planning stage. Finally, a case study on a feed rate scheduling algorithm based on the simulation results is presented to showcase the capabilities of the developed model.

关键词： Robot Machining Multi-Body Simulation Component Mode Synthesis Finite Elements Method

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Integrating AR based operator support features for reconfigurable production systems

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Procedia CIRP 2022年 106卷 276-281页

作者： Angelos Christos Bavelos Dimosthenis Dimosthenopoulos Thodoris Togias Niki Kousi Sotiris Makris Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras 26504 Greece

Augmented Reality (AR) applications have seen a rise in industry in the last few years, due to technological advancements in many of its fundamental areas, such as display and sensing technologies. AR has been used as a driver for supporting human operators in fully manual and semi-automated systems, proving its usefulness in areas such as operation and reconfiguration instructions, safety and resource status awareness. This paper presents an AR application that integrates a set of support features based on the latest advances in AR. The application covers all the aforementioned areas of interest, by including digital instructions, malfunction alerts and resource state visualization in one unified framework. An Asset Administration Shell (AAS) framework has been developed for integration and data exchange between the application and the overall industrial network. The developed application has been validated in an industrial-components-manufacturing case study. Nevertheless, its modularity allows for the adaption in other varying industrial sectors as well.

关键词： Augmented Reality digital instructions operator support asset administration shell alerts reconfigurable production components manufacturing

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China -Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task. © 2024 IEEE.

关键词： Convolutional neural networks

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Operator training framework for hybrid environments: An Augmented Reality module using machine learning object recognition

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Procedia CIRP 2022年 106卷 102-107页

作者： George Apostolopoulos Dionisis Andronas Nikos Fourtakas Sotiris Makris Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras 26504 Greece

As market demands are characterized by more customized products with shorter lifecycles, it is obligatory for modern operators to manage recurrent product or manufacturing system changes. In contrary to previous years, adaptation to such changes prerequires memorization of more information, and familiarization with more complex systems and resources in a shorter period of time. This manuscript presents a novel operator training framework based on Augmented Reality (AR) technology. More specifically, intuitive instructions enhanced with machine learning-based physical object detection are used for making steeper learning curves and providing hands-on experience to operators. The implemented application also supports a walkthrough mode where users can get familiarized with Information and Communication Technologies (ICT) data streams besides fenceless Human-Robot coexistence in collaborative schemes. An automotive case study is used for evaluating the performance of the training framework through a Human-Robot Collaboration (HRC) assembly scenario.

关键词： Augmented Reality Machine learning Human Centered Design Training Assembly Human Robot Collaboration

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Physics-inspired Neural Networks for Parameter Learning of Adaptive Cruise control systems

arXiv

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arXiv 2023年

作者： Apostolakis, Theocharis Ampountolas, Konstantinos Automatic Control and Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

This paper proposes and develops a physics-inspired neural network (PiNN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems in automotive industry. To emulate the core functionality of stock ACC systems, which have proprietary control logic and undisclosed parameters, the constant time-headway policy (CTHP) is adopted. Leveraging the multi-layer artificial neural networks as universal approximators, the developed PiNN serves as a surrogate model for the longitudinal dynamics of ACC-engaged vehicles, efficiently learning the unknown parameters of the CTHP. The PiNNs allow the integration of physical laws directly into the learning process. The ability of the PiNN to infer the unknown ACC parameters is meticulously assessed using both synthetic and high-fidelity empirical data of space-gap and relative velocity involving ACC-engaged vehicles in platoon formation. The results have demonstrated the superior predictive ability of the proposed PiNN in learning the unknown design parameters of stock ACC systems from different car manufacturers. The set of ACC model parameters obtained from the PiNN revealed that the stock ACC systems of the considered vehicles in three experimental campaigns are neither L2 nor L∞ string stable. © 2023, CC BY-NC-ND.

关键词： Adaptive cruise control

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A powder delivery system for large-scale DED machines

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Procedia CIRP 2022年 109卷 617-622页

作者： Panagiotis Stavropoulos Harry Bikas Theodoros Bekiaris Laboratory for Manufacturing Systems & Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras 26504 Greece

Additive Manufacturing (AM) technologies are constantly increasing in terms of applications and market volume; especially metal AM volume has more than doubled in the last 2 years. However, AM machines still remain hampered by their limited build volume dimensions and relatively low build rates, poor quality and uncertainty of final part mechanical properties. A key process aspect for the majority of the AM processes is the deposition rate, deposition rate control and the achievable deposition “spot”. The present study is focused on the improvement of the configuration and design of the process head and the delivery system of a DED machine emphasizing on optimum deposition rate through the implementation of a process model and CFD simulations.

关键词： Additive Manufacturing (AM) Directed Energy Deposition (DED) Computational fluid dynamics (CFD)

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