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cycle time optimization of Deterministic timed Weighted Marked Graphs by Transformation

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2017年第4期25卷 1318-1330页

作者： He, Zhou Li, Zhiwu Giua, Alessandro Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Aix Marseille Univ Univ Toulon CNRS ENSAMLSIS Marseille France Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China Univ Cagliari DIEE I-09124 Cagliari Italy

timed marked graphs, a special class of Petri nets, are extensively used to model and analyze cyclic manufacturing systems. Weighted marked graphs are convenient to model automated production systems, such as robotic work cells or embedded systems, and reduce the size of the model. The main problem for designers is to find a tradeoff between minimizing the cost of the resources and maximizing the system's throughput (also called cycle time). It is possible to apply analytical techniques for the cycle time optimization problem of such systems. The problem consists in finding an initial marking to minimize the cycle time (i.e., maximize the throughput) while the weighted sum of tokens in places is less than or equal to a given value. We transform a weighted marked graph into several equivalent marked graphs and formulate a mixed integer linear programming model to solve this problem. Moreover, several techniques are proposed to reduce the complexity of the proposed method. We show that the proposed method can always find an optimal solution.

关键词： cycle time optimization mixed integer linear programming problem (ILPP) timed Petri net (PN) weighted marked graph

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Investigation on robotic cells design improvement in the welding process of body in white

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2024年第2期8卷 322-333页

作者： Xia, Qi Zhang, Bangcheng Zhang, Xiyu Li, Lei Wu, Chen Changchun Univ Technol Sch Mechatron Engn Changchun Peoples R China Changchun Inst Technol Sch Mechatron Engn Changchun Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun Peoples R China FAW Mold Mfg Co Ltd Changchun Peoples R China Changchun Faway Tong Yang Automobile Components Co Changchun Peoples R China

Issues about cycle time optimization is of great importance in the field of automotive production, the industrial robots are widely used in the welding process of automobiles, but there is little research on the optimization of intra station rhythm during the design phase. By conducting research on workstation with industrial robot processing as key process, this paper carries out analysis from the selection of equipment layout within the workstation, planning production rhythm, and the facility performance analysis within the workstation. The finding shows the cycle time within the workstation has been reduced by 12 s. This article aims at improving the rhythm of robotic cells in complex production environment, and raising production efficiency of workstation. The robot path is optimized by using intelligent algorithms, the human machine collaborative work has been validated in virtual scenes, some digital design is adopted for modelling and simulating, the designed workstation has been verified from multiple perspectives, and finally achieve the workstation design of applying industrial robots in the production scenario.

关键词： Auto production line Welding robot cycle time optimization Simulation

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Concept for a Low-Cost Implementation of Automatic cycle time Measurements in Learning Factories 14th

Concept for a Low-Cost Implementation of Automatic Cycle Tim...

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14th International Conference on Learning Factories (CLF)

作者： Hentsch, Maximilian Kuhn, Christian Kuenster, Nils Dorka, Frithjof Palm, Daniel Reutlingen Univ ESB Business Sch Alteburgstr 150 D-72762 Reutlingen Germany Fraunhofer Inst Mfg Engn & Automat Alteburgstr 150 D-72762 Reutlingen Germany

ISBN: (纸本)9783031653995;9783031654008

cycle time optimization is a fundamental skill for manufacturing planners to avoid bottlenecks and thus increase throughput of production. A learning factory, which replicates real-world manufacturing scenarios, provides an ideal environment for students to acquire this essential skill. Traditionally, cycle times in these scenarios have been manually recorded using stopwatches. This practice has become increasingly outdated with the proliferation of Industry 4.0 and Internet of Things systems that automatically take these measurements in industries, which the learning factories are designed to emulate. However, the high costs and implementation efforts associated with these systems can pose significant challenges for learning factories to adapt. To address these challenges, this paper proposes a cost-effective system for automatic cycle time measurements in learning factories. The system is composed of inexpensive and commercially available hardware such as microcontroller development boards, Radio-Frequency Identification (RFID) readers and a custom software based on open-source software that is free to use. It enables fast and economical retrofitting of existing production scenarios by equipping production stations with RFID readers and product trays with RFID tags. The solution not only enhances the realism of learning factories in terms of cycle time measurements but also introduces the students to key Industry 4.0 concepts like automation, digitalization, and real-time data tracking. By integrating this affordable system, learning factories can better align their practices with industry standards, thereby improving the training quality and preparing students more effectively for the future manufacturing environment.

关键词： Industry 4.0 Learning Factories cycle time optimization RFID Technology Automation and Digitalization in Manufacturing

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A Collaborative Robotics Application for the Assembly of Car Rear Lamps 1

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3rd International Symposium on Industrial Engineering and Automation

作者： Scalera, Lorenzo Lozer, Federico Geerinck, Julie Breda, Andreas Totis, Francesco Polo, Fabio Giusti, Andrea Gasparetto, Alessandro Univ Udine I-33100 Udine Italy Odisee Univ Appl Sci B-9000 Ghent Belgium Marelli Automot Lighting Italy SpA I-33028 Tolmezzo Italy Fraunhofer Italia Res I-39100 Bolzano Italy

ISBN: (数字)9783031704659

ISBN: (纸本)9783031704642;9783031704659

In this work we present the automation of the sticking of small components on car rear lamps, a swift and repetitive operation that is currently performed manually by an operator. The challenging automation of this process includes the delicate pick and place of tiny items in predefined positions on the rear lamps. Pick-and-place trajectories for the robot are planned and a custom end-effector is designed to accomplish the task. Furthermore, a safety approach is implemented to stop the robot in the event of a potential collision with the human operator. Finally, the impact of the cycle time on quantitative metrics for human-robot collaboration, as well as on the mechanical energy of the robot is evaluated. Experimental results on a UR5e manipulator show the feasibility of the operation, while meeting the robot constraints and the cycle time requirements.

关键词： collaborative robotics assembly trajectory planning speed and separation monitoring cycle time optimization

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OPTIMALMOULD | Cooling System influence in Injection Moulding cycle time optimization

OPTIMALMOULD | Cooling System influence in Injection Mouldin...

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2nd International Conference on Advanced Materials and Engineering Materials (ICAMEM 2012)

作者： Ramos, Carina Carreira, Pedro Bartolo, Paulo Alves, Nuno Polytech Inst Leiria Ctr Rapid & Sustainable Prod Dev Ctr Empresarial Marinha Grande P-2430028 Marinha Grande Portugal

ISBN: (纸本)9783037856666

Most works undertaken on injection moulding cooling time optimization aimed at reducing the time required to produce plastic parts, this way minimizing industrial costs. An optimal cooling system is proposed to improve the injection moulding design. A multi-objective genetic algorithm called NSGA-II is used, to obtain the optimal parameters in the optimal-mould design. This new design method is an efficient tool to get optimal parameters for injection moulding design. A real case study was carried out to test the optimization platform.

关键词： Cooling System cycle time optimization Injection Moulding Multi-objective optimization optimization Platform

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Multiscale computational fluid dynamics modeling and reactor design of plasma-enhanced atomic layer deposition

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COMPUTERS & CHEMICAL ENGINEERING 2020年 142卷

作者： Zhang, Yichi Ding, Yangyao Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA

Plasma-enhanced atomic layer deposition (PEALD) is one of the most widely adopted deposition methods in the semiconductor industry. It is chosen largely due to its superior ability to deliver ultra-conformal dielectric thin-films with high aspect-ratio surface structures, which are encountered more and more often in the novel design of metal-oxide-semiconductor field-effect transistors (MOSFETs) in the NAND (Not-And)-type flash memory devices. Compared with the traditional thermal ALD method, PEALD allows for lower operating temperature and speeds up the deposition process with the involvement of plasma species. Despite its popularity, the development of PEALD operation policies remains a complicated and expensive task, which motivates the construction of an accurate and comprehensive simulation model. While existing models have described the individual or partially coupled domains, none of these models has captured all three domains in the PEALD process: surface reaction, macroscopic gas transport, and plasma generation. In this work, a comprehensive multiscale computational fluid dynamics (CFD) model is developed for a remote PEALD reactor used in the deposition of HfO2 thin-films. First, a previously developed kinetic Monte-Carlo (kMC) model is adapted for the multiscale simulation to describe the surface reactions. Then, two macroscopic models, specifically tailored for the remote plasma reactor, are formulated to describe the dynamic behaviors of the plasma generation and bulk species transport domains, respectively. Additionally, an integrated message passing interface (MPI) scheme is built to couple and resolve the communication between different scales in the model. The model results are then validated with experimental data and an automated workflow is established for model calculations without human intervention. Finally, a set of baseline operating conditions derived from the model simulation results is proposed to guarantee the production of high-quality

关键词： Plasma-enhanced atomic layer deposition Microscopic modeling Kinetic Monte Carlo modeling Density functional theory Computational fluid dynamics cycle time optimization

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Machine learning-based modeling and operation of plasma-enhanced atomic layer deposition of hafnium oxide thin films

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COMPUTERS & CHEMICAL ENGINEERING 2021年 144卷

作者： Ding, Yangyao Zhang, Yichi Chung, Ho Yeon Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA

Plasma-enhanced atomic layer deposition (PEALD) has demonstrated its superiority at coating ultra-conformal high dielectric thin-films, which are essential to the fin field-effect transistors (FinFETs) as well as the advanced 3D V-NAND (vertical Not-AND) flash memory cells. Despite the growing research interest, the exploration of the optimal operation policies for PEALD remains a complicated and expensive task. Our previous work has constructed a comprehensive 3D multiscale computational fluid dynamics (CFD) model for the PEALD process and demonstrated its potential to enhance the understanding of the process. Nevertheless, the limitation of computational resources and the relatively long computation time restrict the efficient exploration of the operating space and the optimal operating strategy. Thus, in this work, we apply a 2D axisymmetric reduction of the previous 3D model of PEALD reactors with and without the showerhead design. Furthermore, a data-driven model is derived based on a recurrent neural network (RNN) for process characterization. The developed integrated data-driven model is demonstrated to accurately characterize the key aspects of the deposition process as well as the gas-phase transport profile while maintaining computational efficiency. The derived data-driven model is further validated with the results from a full 3D multiscale CFD model to evaluate model discrepancy. Using the data-driven model, an operational strategy database is generated, from which the optimal operating conditions can be determined for the deposition of Hafnium Oxide (HfO 2) thin-film based on an elementary cost analysis. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Plasma-enhanced atomic layer deposition Computational fluid dynamics Data-driven model Machine learning Recurrent neural network cycle time optimization

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optimization of Deterministic timed Weighted Marked Graphs

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2017年第2期14卷 1084-1095页

作者： He, Zhou Li, Zhiwu Giua, Alessandro Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China Univ Toulon & Var LSIS UMR 7296 ENSAM Aix Marseille UnivCNRS F-13397 Marseille France Univ Cagliari DIEE I-09124 Cagliari Italy

timed marked graphs, a special class of Petri nets, are extensively used to model and analyze cyclic manufacturing systems. Weighted marked graphs are convenient to model systems with bulk services and arrivals. We consider two problems of practical importance for this class of nets. The marking optimization problem consists in finding an initial marking to minimize the weighted sum of tokens in places, while the average cycle time is less than or equal to a given value. The cycle time optimization problem consists in finding an initial marking to minimize the average cycle time, while the weighted sum of tokens in places is less than or equal to a given value. We propose two heuristic algorithms to solve these problems. Several simulation studies show that the proposed approach is significantly more efficient than existing ones. Note to Practitioners-This paper is motivated by the optimization of manufacturing systems such as assembly lines and job-shops. The quantity of products which have to be stored or moved and the number and type of machines which operate the system have economical consequences. Therefore, the main problem for designers is to find a tradeoff between minimizing the cost of the resources and maximizing the system's throughput. To this end, we formalize and address two problems. First, we aim to minimize the use of resources required to meet a desired throughput. Second, we aim to maximize the throughput with a given set of available resources. However, the existing results fail to provide practically effective and computationally efficient methods to analyze and solve the problems in such systems. The proposed iterative heuristic algorithms can find a proper schedule for a system, which is significant and useful to manufacturing engineers.

关键词： cycle time optimization heuristic solution marking optimization timed Petri nets weighted marked graphs

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