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2021, Turkish Journal of Computer and Mathematics Education (TURCOMAT)
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13 pages
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Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysing sensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. The proposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of the tools we can detect quality issues and develop an automated quality system using machine learning techniques.
IFAC-PapersOnLine, 2015
Manufacturing processes, such as machining, welding or tooling and assembly are increasingly automated to reduce the costs and furthermore to negate the decrease of a skilled labor force. Each process has its own key parameters that are required to be within a certain tolerance band in order to ensure product quality, such as e.g. surface finish. The application of intelligent automation allows the manufacturer to create an environment where the sensory systems that are inherently connected to intelligent components are utilized for manufacturing process monitoring purposes. In addition this framework is meant to be used in a multistage manufacturing environment to control the propagation of variations introduced at upstream stations. The widely available smart sensors in today's manufacturing industry can assist to reduce the number of direct dimensional measurement tools required for this purpose. In this paper, a generic framework for the design of the monitoring and decision making system is demonstrated with a simulated case study in milling, using the tracking of the force rate as a successful technique to detect certain events, such as tool breakage. Moreover, the data can be used for compensation in the next production or assembly stage for variation reduction in multistage manufacturing processes.
Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, 2021
Manufacturing Systems are considered complex engineering systems given the large number of integrated entities and their interactions. Unplanned events and disruptions that can happen at any time in real-word industrial environments increase the complexity of manufacturing production systems. In the fourth industrial revolution (so called Industry 4.0), the industrial sector is rapidly changing with emerging technologies like Cyber-Physical Production System (CPPS), Internet of Thing (IoT), Artificial Intelligence (AI), etc. However, the efficiency and reliability of these systems are still questionable in many circumstances. To address this challenge, an observation framework based on AI techniques aimed at elaborating predictive and reactive planning of the maintenance operations of CPPS is proposed in this paper. The proposed tool aims to improve the system's reliability and helps the maintenance supervisors to adjust maintenance decisions. In order to assess the performance of the proposed tool, a case study on an industrytype learning factory is considered. A proof of concept shows the efficiency of the framework.
1994
This paper describes sensor-based methodologies for novel real-time quality control strategy in manufacturing. Feasibility of the methodology is demonstrated in two manufacturing processes: turning and powder injection molding. In case of turning, the process is monitored using force and vibration sensors. The level of tool wear on the cutting tool is estimated using the information from these sensors. The quality of workpiece is maintained by adjusting the operating conditions and cutting tool position to compensate for the undesirable effects of the progressive tool wear. In case of powder injection molding, the product quality factors are obtained by visual inspection and process states are abstracted from the sensor data. The diagnostic knowledge is represented by a causality network and solved by network conversion method and abductive reasoning. The diagnostic solution provides a set of alternative hypothesis of disorders which explains the manifested quality factors and process states.
International Conference on Intelligent Systems (IS), 2018
The proliferation of Information and Communication Technologies allowed the development of new solutions to be applied at the shop-floor and all the tools which helps the manufacturers. Hence, new solutions such as cyber-physical production systems, data analytics and knowledge management were developed and proposed to solve the well-known issues, such as quality control in multistage manufacturing systems. However, those solutions can only have a small contribution in solving that issues compared to an optimized and fully integrated approach. To allow the development of a fully integrated environment, it is necessary to deliver a standard way to communicate and interact with the different functionalities. The proposed research aims to provide an integration layer, capable of translating the rules defined at the knowledge management level, structured as Decision Model and Notation rules, into an AutomationML based language. This allows the cyber-physical production system the ability to apply these rules near the shop-floor. This article presents the template defined to represent the rules in AutomationML as well as the infrastructure developed to receive the rules from the knowledge management, translate them and deliver to the cyber-physical production system. At the end of the article is presented a test bed where the solution is instantiated with rules focused on quality control.
THE 3RD FACULTY OF INDUSTRIAL TECHNOLOGY INTERNATIONAL CONGRESS 2021 INTERNATIONAL CONFERENCE: Enriching Engineering Science through Collaboration of Multidisciplinary Fields
Since the issue of productivity is increased, Industry 4.0 also promotes the issue of quality of product, processes, and services. However, studies show that recent quality initiatives are still facing the old problem and companies cannot get benefit from industry 4.0 environment to establish better quality. This paper proposes a framework of Cloud-based Quality Analyzer (CQA) which is designed specifically to perform quality analysis by reducing the dependency to the human quality engineer with respect to faster and more accurate information. Equipped by the standard quality analysis tools and some data mining algorithms, CQA can perform a wide-range quality-related activity from simple analysis to fully automated feedback loop quality control. Moreover, by adopting Cyber Physical System (CPS) technology, CQA is also expected to be able to realize real-time online quality control. However, since this is a conceptual framework, many technical details related to the data flow, configurability, security, the quality of service, connectivity and network aspect are still required for further investigation. From a quality engineering point of view, quality analysis techniques, data modeling, formal procedure of quality analysis, and more advanced data mining algorithms for extracting quality-related information are open for further research.
This paper describes the approach and implementation of a system that combines real industrial environments with a virtual copy of these components. The coupling elements for communication and data management are cyber-physical systems and active digital object memories. The idea of this approach is to create an assistance system that relies on these virtual digital object memories to ensure quality characteristics and to describe any information in a unified structured format. Up to a certain level of complexity, state changes and feature checks are done decentralized by each object memory, in the way of autonomous control.
International Journal of Mechanical Engineering and Robotics Research, 2020
This article illustrates a systematic approach for predicting tool wear in machining process through Cyber-Physical System (CPS) architecture using simple electronic components such as personal computers and low-cost sensors. The proposed Cyber-Physical structure consists of 5 steps; smart connection, data to information, feature extraction, awareness of issues and self-adjustment. We tried to install a big data analysis technology into CPS architecture to catch the usual/unusual state of the cutting tool from the spindle power consumption changes. The excessive repetitions of grooving would bring the trend changing of power consumption. To facilitate the statistical analysis, the correlation coefficient R was calculated from the single regression analysis between two different cycles of time-series power consumption. The correlation coefficient R also had a strong relation with the condition changes of tool wear and would become a powerful tool to catch the usual/unusual state of the cutting tool in the proposed CPS architecture. The health information obtained from the system can be used for higher level of management of cutting tool based on the condition monitoring free from the schedule-based maintenance.
53rd CIRP Conference on Manufacturing Systems, Chicago, IL, U.S., 2020
Manufacturing informatics aims to optimize productivity by extracting information from numerous data sources and making decisions based on that information about the process and the parts being produced. Manufacturing processes usually include a series of costly operations such as heat treatment, machining, and inspection to produce high-quality parts. However, performing costly operations when the product conformance to specifications cannot be achievable is not desirable. This paper develops a new machine learning-based informatics system capable of predicting the end product quality so that non-value-adding operations such as inspection can be minimized and the process can be stopped before completion when the part being manufactured fails to meet the design specifications.
Sensors
Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. The aim of this publication is to present the novel concept of support by artificial intelligence (AI) for quality control of AM of medical devices made of polymeric materials, based on the example of our own elbow exoskeleton. The methodology of the above-mentioned inspection process differs depending on the intended application of 3D printing as well as 3D scanning or reverse engineering. The use of artificial intelligence increases the versatility of this process, allowing it to be adapted to specific needs. This brings not only innovative scientific and technological solutions, but also a significant economic and social impact through faster operation, greater efficiency, and cost savings. The article also indicates the limitations an...
CIRP Annals, 2021
The paper presents two original and innovative contributions: 1) the model of machine learning (ML) based approach for predictive maintenance in manufacturing system based on machine status indications only, and 2) semi-Double-loop machine learning based intelligent Cyber-Physical System (I-CPS) architecture as a higher-level environment for ML based predictive maintenance execution. Considering only the machine status information provides rapid and very low investment-based implementation of an advanced predictive maintenance paradigm, especially important for SMEs. The model is validated in real-life situations, exploring different learning algorithms and strategies for learning maintenance predictive models. The findings show very high level of prediction accuracy.
2015
Cyber-Physical Systems (CPS) are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the internet. CyberPhysical Manufacturing Systems (CPMSs), relying on the newest and foreseeable further developments of computer science, information and communication technologies on the one hand, and of manufacturing science and technology, on the other hand, may lead to the 4th Industrial Revolution, frequently noted as Industry 4.0. CPMS consist of autonomous and cooperative elements and sub-systems that are getting into connection with each other in situation dependent ways, on and across all levels of production, from processes through machines up to production and logistics networks. Modeling their operation and also forecasting their emergent behavior raise a series of basic and application-oriented rese...
3C Empresa, 2022
Quality Control (QC) has recently emerged as a significant global trend among manufacturers, adopting intelligent manufacturing practices in view of Industry 4.0 requirements. Intelligent manufacturing is the process of enhancing production through the use of cutting-edge technologies, sensor integration, analytics, and the Internet of Things (IoT). The proposed paper mainly focuses on the study of the scope and the evolution of quality control techniques from conventional practices to intelligent approaches along with the state of art technologies in place. The challenges faced in building intelligent QC systems, in terms of security, system integration, Interoperability, and Humanrobot collaboration, are highlighted. Surface defect detection has evolved as a critical QC application in modern manufacturing setups to ensure high-quality products with high market demand. Further, the recent trends and issues involved in surface defect detection using intelligent QC techniques are discussed. The methodology of implementing surface defect detection on cement wall surfaces using the Haar Cascade Classifier is discussed.
Smart Structures and Systems, 2020
Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.
Advances in Data Mining. Applications and Theoretical Aspects
The purpose of this study was to develop an innovative supervisor system to assist the operators in an industrial manufacturing process to help discover new alternative solutions for improving both the products and the manufacturing process. This paper presents a solution for integrating different types of statistical modelling methods for a usable industrial application in quality monitoring. The two case studies demonstrating the usability of the tool were selected from a steel industry with different needs for knowledge presentation. The usability of the quality monitoring tool was tested in both case studies, both offline and online.
2018
Ye Yuan1,2,*, Guijun Ma3, Cheng Cheng2, Beitong Zhou2, Huan Zhao3, Hai-Tao Zhang1,2, Han Ding1,3,* 1State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, P.R. China. 2School of Automation, Huazhong University of Science and Technology, Wuhan 430074, P.R. China. 3School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P.R. China.
Journal of Manufacturing Systems, 2015
This paper presents the current status and the latest advancement of cyber-physical systems (CPS) in manufacturing. In order to understand CPS and its future potential in manufacturing, definitions and characteristics of CPS are explained and compared with cloud manufacturing concept. Research and applications are outlined to highlight the latest advancement in the field. CPS shows great promise in factories of the future in the areas of future trends as identified at the end of this paper.
The International Journal of Advanced Manufacturing Technology, 2023
Smart industries use modern technologies such as machine learning and big data to maintain supply chain management and increase productivity but still the main challenge faced during quality control as this might affect the production rate. Smart industries are completely based on supervised learning that enables better inspection and effectively controls the parameter involved in the production process. Smart industries choose the mechanism that improves production and assures maximum quality. The various kernel function is initially used to select and extract a parameter. Support vector machine (SVM) is a supervised learning approach used in manufacturing industries to evaluate quality control. The SVM model uses the kernel function, namely RBF, along with Neural Networks, in identifying the parameter involved in quality management and undergoes the classification process. SVM consists of C-SVM and V-SVM classifier models involved in the classification process and undergoes training to handle the multiple numbers of consequence aroused during manufacturing. The performance of SVM classifiers and RBF NNs is evaluated. Different kernel functions, such as polynomial, linear, sigmoid, RBF, and over-varying gamma coefficient values, are tested in the experimental evaluation concerned with the comparative analysis of the continuous quality control function of the SVM classifier. Experimental results demonstrate the superiority of the SVM classifier in terms of the estimated computational time (88.1%), F1-measure (89.4%), ROC (65%), and accuracy (94.6%). The goal of the proposed model is to monitor the manufacturing process and control fault occurrence.
Procedia Manufacturing, 2020
The 4 th industrial revolution promotes the automatic inspection of all products towards a zero-defect and high-quality manufacturing. In this context, collaborative robotics, where humans and machines share the same space, comprises a suitable approach that allows combining the accuracy of a robot and the ability and flexibility of a human. This paper describes an innovative approach that uses a collaborative robot to support the smart inspection and corrective actions for quality control systems in the manufacturing process, complemented by an intelligent system that learns and adapts its behavior according to the inspected parts. This intelligent system that implements the reinforcement learning algorithm makes the approach more robust once it can learn and be adapted to the trajectory. In the preliminary experiments, it was used a UR3 robot equipped with a Force-Torque sensor that was trained to perform a path regarding a product quality inspection task.
Advances in Mechanical Engineering, 2018
In today's highly competitive global market, winning requires near-perfect quality. Although most mature organizations operate their processes at very low defects per million opportunities, customers expect completely defect-free products. Therefore, the prompt detection of rare quality events has become an issue of paramount importance and an opportunity for manufacturing companies to move quality standards forward. This article presents the learning process and pattern recognition strategy for a knowledge-based intelligent supervisory system, in which the main goal is the detection of rare quality events. Defect detection is formulated as a binary classification problem. The l 1-regularized logistic regression is used as the learning algorithm for the classification task and to select the features that contain the most relevant information about the quality of the process. The proposed strategy is supported by the novelty of a hybrid feature elimination algorithm and optimal classification threshold search algorithm. According to experimental results, 100% of defects can be detected effectively.
Procedia CIRP, 2021
Smart manufacturing is the modern form of manufacturing that utilises Industry 4.0 enablers for decision making and resources planning by taking advantage of the available data. Therefore, the state of the art technologies are either replaced or improved using the newly introduced manufacturing paradigm. In practice, condition monitoring is an ongoing activity that preserves the manufacturing facility capability to deliver its production aims and decrease the production discontinuity as much as possible. Against this background, this paper discusses the state of the art condition monitoring and proposes a framework of fault detection and decision making at different levels namely component and station. The introduced framework relies on Virtual Engineering (VE) and Discrete Event Simulation (DES) in smart manufacturing environments. The application of the suggested methodology and its implementation is demonstrated in a case study of a battery module assembly line.
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