| 1. |
- Yue, C., et al.
(författare)
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Research progress on machining deformation of thin-walled parts in milling process
- 2022
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Ingår i: Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica. - : Chinese Society of Astronautics. - 1000-6893. ; 43:4
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Tidskriftsartikel (refereegranskat)abstract
- With the development of machine tool machining performance and tool cutting performance, thin-walled parts can be machined with high efficiency and precision, leading to their wide use in the aerospace field. The complex structure and low rigidity of thin-walled parts make them easy to deform in the milling process. Therefore, accurate prediction and control of thin-walled parts deformation is an urgent technical problem to be solved in the field of machining. Through classification of thin-walled parts and analysis of the processing technologies, the factors causing the deformation of thin-walled parts are summarized, and the most basic calculation model of milling force is briefly introduced. Research on deformation prediction and control methods of thin-walled parts at home and abroad is reviewed. It is found that deformation of thin-walled parts is predicted by using the elastic-plastic theory and the numerical simulation method, and deformation of thin-walled parts is controlled by process optimization, auxiliary support technology, high-speed cutting technology and numerical control compensation technology. Based on update and iteration of data-driven digital twins, deformation prediction and control of the actual processing of thin-walled parts are realized. The theoretical framework of deformation prediction and control of thin-walled parts processing is built based on digital twins. Development and application prospects of digital twinning deformation prediction and control in thin-walled parts processing are also discussed.
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| 2. |
- Adamson, Göran, 1958-, et al.
(författare)
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Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems
- 2017
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Ingår i: Journal of manufacturing systems. - : Elsevier. - 0278-6125 .- 1878-6642. ; 43, s. 305-315
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Tidskriftsartikel (refereegranskat)abstract
- Modern distributed manufacturing within Industry 4.0, supported by Cyber Physical Systems (CPSs), offers many promising capabilities regarding effective and flexible manufacturing, but there remain many challenges which may hinder its exploitation fully. One major issue is how to automatically control manufacturing equipment, e.g. industrial robots and CNC-machines, in an adaptive and effective manner. For collaborative sharing and use of distributed and networked manufacturing resources, a coherent, standardised approach for systemised planning and control at different manufacturing system levels and locations is a paramount prerequisite.In this paper, the concept of feature-based manufacturing for adaptive equipment control and resource-task matching in distributed and collaborative CPS manufacturing environments is presented. The concept has a product perspective and builds on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard. Distributed control is realised through the use of networked and smart FB decision modules, enabling the performance of collaborative run-time manufacturing activities according to actual manufacturing conditions. A feature-based information framework supporting the matching of manufacturing resources and tasks, as well as the feature-FB control concept, and a demonstration with a cyber-physical robot application, are presented.
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| 3. |
- Syberfeldt, Anna, et al.
(författare)
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Dynamic operator instructions based on augmented reality and rule-based expert systems
- 2016
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271 .- 2212-8271. ; 41, s. 346-351, s. 346-351
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Tidskriftsartikel (refereegranskat)abstract
- Augmented reality is currently a hot research topic within manufacturing and a great potential of the technique is seen. In this study, we aim to increase the knowledge of the adaptation and usability of augmented reality for the training of operators. We propose an approach of using dynamic information content that is automatically adjusted to the individual operator and his/her learning progress for increased efficiency and shorter learning times. The approach make use of the concept of expert systems from the field of artificial intelligence for determine the information content on-line. We develop a framework called "Augmented Reality Expert System" (ARES) that combines AR and expert systems. A proof-of-concept evaluation of the framework is presented in the paper and possible future extensions are discussed.
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| 4. |
- Wang, Baicun, et al.
(författare)
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Toward human-centric smart manufacturing : A human-cyber-physical systems (HCPS) perspective
- 2022
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 63, s. 471-490
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Tidskriftsartikel (refereegranskat)abstract
- Advances in human-centric smart manufacturing (HSM) reflect a trend towards the integration of human-in-the loop with technologies, to address challenges of human-machine relationships. In this context, the human-cyberphysical systems (HCPS), as an emerging human-centric system paradigm, can bring insights to the development and implementation of HSM. This study presents a systematic review of HCPS theories and technologies on HSM with a focus on the human-aspect is conducted. First, the concepts, key components, and taxonomy of HCPS are discussed. HCPS system framework and subsystems are analyzed. Enabling technologies (e.g., domain technologies, unit-level technologies, and system-level technologies) and core features (e.g., connectivity, integration, intelligence, adaptation, and socialization) of HCPS are presented. Applications of HCPS in smart manufacturing are illustrated with the human in the design, production, and service perspectives. This research offers key knowledge and a reference model for the human-centric design, evaluation, and implementation of HCPS-based HSM.
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| 5. |
- Zhang, Dan, et al.
(författare)
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On performance enhancement of parallel kinematic machine
- 2013
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Ingår i: Journal of Intelligent Manufacturing. - : Springer. - 0956-5515 .- 1572-8145. ; 24:2, s. 267-276
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a spatial three degrees of freedom (DOF) parallel kinematic machine enhanced by a passive leg and a web-based remote control system. First, the geometric model of the parallel kinematic machine is addressed. In the mechanism, a fourth kinematic link-a passive link connecting the base center to the moving platform center-is introduced. Each of the three parallel limbs is actuated by one prismatic joint, respectively. The additional link has three passive DOF, namely two rotations around x and y axes and one translation along z axis. With the existence of this link, the unwanted motion of the tool (located in the moving platform) is constrained. The fourth link also enhances the global stiffness of the structure and distributes the torque from machining. With the kinematic model, a web-based remote control approach is applied. The concept of the web-based remote manipulation approach is introduced and the principles behind the method are explored in detail. Finally, a remote manipulation is demonstrated to the proposed structure using web-based remote control concept.
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| 6. |
- Schmidt, Bernard, 1981-, et al.
(författare)
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Big data in maintenance decision support systems : aggregation of disparate data types
- 2016
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Ingår i: Euromaintenance 2016 Conference Proceedings. - 9786188260108 ; , s. 503-512
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- There is need to obtain reliable information on current and future asset health status to support maintenance decision making process. Within maintenance two main sources of data can be distinguished: Computerized Maintenance Management System (CMMS) for asset registry and maintenance work records; and Condition Monitoring Systems (CM) for direct asset components health state monitoring. There are also other sources of information like SCADA (Supervisory Control and Data Acquisition) for process and control monitoring that can provide additional contextual information leading to better decision making. However data produced acquired and processed and in those system are of disparate types, nature and granularity. This variety includes: event data about failures or performed maintenance work mostly descriptions in unstructured natural language; process variables obtained from different types of sensors and different physical variables from transducers, acquired with different sampling frequencies. Indeed, condition monitoring data are so disparate in nature that maintainers deal with scalars (temperature) through waveforms (vibration) to 2D thermography images and 3D data from machine geometry measuring. Integration and aggregation of those data is not a trivial task and requires modelling of knowledge about those data types, their mutual dependencies and dependencies with monitored processes. There are some attempts of standardisation that try to enable integration of CBM data from different sources. The conversion of those amount of data in meaningful data sets is required for better machine health assessment and tracking within the specific operational context for the asset. This will also enhance the maintenance decision support system with information on how different operational condition can affect the reliability of the asset for concrete contextual circumstances.
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| 7. |
- Schmidt, Bernard, 1981-, et al.
(författare)
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Context Awareness in Predictive Maintenance
- 2016
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Ingår i: Current Trends in Reliability, Availability, Maintainability and Safety<em></em>. - Cham : Springer. - 9783319235974 - 9783319235967 ; , s. 197-211
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Bokkapitel (refereegranskat)abstract
- Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.
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| 8. |
- Schmidt, Bernard, 1981-, et al.
(författare)
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Next Generation Condition Based Predictive Maintenance
- 2014
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Ingår i: Proceedings of The 6<sup>th</sup> International Swedish Production Symposium 2014. - 9789198097412
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Konferensbidrag (refereegranskat)abstract
- Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and make decisions upon this prediction. The main aim of the presented research is to achieve an improvement in condition based Predictive Maintenance through the Cloud-based approach with usage of the largest information content possible. The objective of this paper is to outline the first steps of a framework to handle and process maintenance, production and factory related data from the first life-cycle phase to the operation and maintenance phase.
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| 9. |
- Adamson, Göran, 1958-, et al.
(författare)
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A Cloud Service Control Approach for Distributed and Adaptive Equipment Control in Cloud Environments
- 2016
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271 .- 2212-8271. ; 41, s. 644-649, s. 644-649
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Tidskriftsartikel (refereegranskat)abstract
- A developing trend within the manufacturing shop-floor domain is the move of manufacturing activities into cloud environments, as scalable, on-demand and pay-per-usage cloud services. This will radically change traditional manufacturing, as borderless, distributed and collaborative manufacturing missions between volatile, best suited groups of partners will impose a multitude of advantages. The evolving Cloud Manufacturing (CM) paradigm will enable this new manufacturing concept, and on-going research has described many of its anticipated core virtues and enabling technologies. However, a major key enabling technology within CM which has not yet been fully addressed is the dynamic and distributed planning, control and execution of scattered and cooperating shop-floor equipment, completing joint manufacturing tasks.In this paper, the technological perspective for a cloud service-based control approach is described, and how it could be implemented. Existing manufacturing resources, such as soft, hard and capability resources, can be packaged as cloud services, and combined to create different levels of equipment or manufacturing control, ranging from low-level control of single machines or devices (e.g. Robot Control-as-a-Service), up to the execution of high level multi-process manufacturing tasks (e.g. Manufacturing-as-a-Service). A multi-layer control approach, featuring adaptive decision-making for both global and local environmental conditions, is proposed. This is realized through the use of a network of intelligent and distributable decision modules such as event-driven Function Blocks, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system’s integration to the CM cloud service management functionality is also described.
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| 10. |
- Givehchi, Mohammad, et al.
(författare)
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Knowledge-based Operation Planning and Machine Control by Function Blocks in Web-DPP
- 2013
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Ingår i: Advances in Sustainable and Competitive Manufacturing Systems. - Heidelberg : Springer. - 9783319005560 - 9783319005577 ; , s. 665-679
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Konferensbidrag (refereegranskat)abstract
- Today, the dynamic market requires manufacturing firms to possess high degree of adaptability and flexibility to deal with shop-floor uncertainties. Specifically, targeting SMEs active in the machining and metal cutting sector who normally deal with complex and intensive process planning problems, researchers have tried to address the subject. Among proposed solutions, Web-DPP elaborates a two-layer distributed adaptive process planning system based on function-block technology. Function-block enabled machine controllers are one of the elements of this system. In addition, intensive reasoning based on the features data of the products models, machining knowledge, and resource data is needed to be performed inside the function blocks in machine controller side. This paper reports the current state of design and implementation of a knowledge-based operation planning module using a rule-engine embedded in machining feature function blocks, and also the design and implementation of a common interface (for CNC milling machine controller and its specific implementation for a specific commercial controller) embedded in the machining feature function blocks for controlling the machine. The developed prototype is validated through a case-study.
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