219 Human and Robot Allocation Method for Flexible Hybrid Manufacturing System

At present, part remanufacturing technology is gaining more interest from the military and industries due to the benefits of cost reduction as well as time and energy savings. This paper presents the research on one main component of part remanufacturing technology, which is part repairing. Traditionally, part repairing is done in the repair department using welding processes. However, the limitations of the traditional welding process are becoming more and more noticeable when accuracy and reliability are required. Part repairing strategies have been developed utilizing a hybrid manufacturing system in which the laser-aided deposition and CNC cutting processes are integrated. Part repairing software is developed in order to facilitate the users. The system and the software elevate the repairing process to the next level, in which accuracy, reliability, and efficiency can be achieved. The concept of the repairing process is presented in this paper, and verification and experimental results are also discussed.

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A Study on Operator Allocation Method Considering the Productivity and the Training Effect in Labor-Intensive Manufacturing System

2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) ◽

10.1109/ieem44572.2019.8978613 ◽

2019 ◽

Author(s):

H. Haraguchi

Keyword(s):

Manufacturing System ◽

Training Effect ◽

Allocation Method ◽

Operator Allocation

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Evaluate the Performance of a Hybrid Manufacturing System Using GI/G/1 Network

Journal of Industrial and Intelligent Information ◽

10.12720/jiii.3.4.352-355 ◽

2015 ◽

Vol 3 (4) ◽

Author(s):

Ping-Yu Chang

Keyword(s):

Manufacturing System ◽

Hybrid Manufacturing

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Optimal control of a two-stage hybrid manufacturing system model

Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304) ◽

10.1109/cdc.1999.832819 ◽

2003 ◽

Cited By ~ 15

Author(s):

C.G. Cassandras ◽

Qinjia Liu ◽

K. Gokbayrak ◽

D.L. Pepyne

Keyword(s):

Optimal Control ◽

Manufacturing System ◽

System Model ◽

Hybrid Manufacturing ◽

Two Stage

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AIMS – A Metal Additive-hybrid Manufacturing System: System Architecture and Attributes

Procedia Manufacturing ◽

10.1016/j.promfg.2015.09.021 ◽

2015 ◽

Vol 1 ◽

pp. 273-286 ◽

Cited By ~ 24

Author(s):

Guha Manogharan ◽

Richard Wysk ◽

Ola Harrysson ◽

Ronald Aman

Keyword(s):

System Architecture ◽

Manufacturing System ◽

Hybrid Manufacturing ◽

Metal Additive

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A Proposal of Resource Allocation Method Based on Combinatorial Double Auction Technique in Crowdsourced Manufacturing

2020 International Symposium on Flexible Automation ◽

10.1115/isfa2020-9638 ◽

2020 ◽

Author(s):

Daisuke Kokuryo ◽

Yoshiaki Harada ◽

Toshiya Kaihara ◽

Nobutada Fujii

Keyword(s):

Resource Allocation ◽

Internet Of Things ◽

Computational Experiment ◽

Manufacturing System ◽

Cloud Service ◽

Smart Manufacturing ◽

Double Auction ◽

Allocation Method ◽

Production Resources

Abstract With the development of the IoT (Internet of Things), smart manufacturing system with cloud service and computing techniques has gained worldwide attention. Crowdsourced manufacturing system is a production styles that connects among different companies and factories to share the production resources. In this system, it is important to distribute resources appropriately to increase the productivity. In this proceeding, a resource allocation method based on combinatorial double auction technique is proposed. In the computational experiment, a characteristic of the proposed resource allocation method is evaluated.

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Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System

Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-5225-2944-6.ch008 ◽

2018 ◽

pp. 162-187 ◽

Cited By ~ 3

Author(s):

Ömer Faruk Yılmaz ◽

Mehmet Bülent Durmuşoğlu

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Manufacturing System ◽

Optimization Problems ◽

Multiple Objectives ◽

Hybrid Manufacturing ◽

Nsga Ii ◽

Multi Objective ◽

Comprehensive Information ◽

Manufacturing Environments

Problems encountered in real manufacturing environments are complex to solve optimally, and they are expected to fulfill multiple objectives. Such problems are called multi-objective optimization problems(MOPs) involving conflicting objectives. The use of multi-objective evolutionary algorithms (MOEAs) to find solutions for these problems has increased over the last decade. It has been shown that MOEAs are well-suited to search solutions for MOPs having multiple objectives. In this chapter, in addition to comprehensive information, two different MOEAs are implemented to solve a MOP for comparison purposes. One of these algorithms is the non-dominated sorting genetic algorithm (NSGA-II), the effectiveness of which has already been demonstrated in the literature for solving complex MOPs. The other algorithm is fast Pareto genetic algorithm (FastPGA), which has population regulation operator to adapt the population size. These two algorithms are used to solve a scheduling problem in a Hybrid Manufacturing System (HMS). Computational results indicate that FastPGA outperforms NSGA-II.

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Automatic Toolpath Generation for Multi-Axis Surface Machining in a Hybrid Manufacturing System

Volume 2: 29th Design Automation Conference, Parts A and B ◽

10.1115/detc2003/dac-48780 ◽

2003 ◽

Cited By ~ 1

Author(s):

Jianzhong Ruan ◽

F. W. Liou

Keyword(s):

Manufacturing System ◽

Cutting Tools ◽

Deposition Process ◽

Machining Process ◽

Hybrid Manufacturing ◽

Cnc Machine ◽

Surface Machining ◽

Visibility Map ◽

Cutter Path ◽

Cutter Path Generation

In a multi-axis hybrid manufacturing system, it is necessary to utilize a machining process to improve surface accuracy and guarantee overall geometry after the deposition process. Due to the complexity of the multi-axis system, it is necessary to find proper orientations of cutting tools for the CNC machine to finish surface machining. This paper presents an algorithm to find collision-free surface machining toolpath for a given workpiece. The concept of the 2-D visibility map and its properties are discussed. The algorithm to compute the 2-D visibility map is presented. With the help of the 2-D visibility map, an optimal a collision free tool approaching direction can be easily decided. Also the type of the surface machining toolpath for different types of surfaces is decided based on topological information and the machining toolpath (CL data for milling tool). The developed planning scheme has been tested via machine simulations and has shown that it can be effectively applied to cutter-path generation for multi-axis surface machining.

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