Intelligente Arbeitsvorbereitung in der Cloud/Smarter operations planning and scheduling in the cloud - Cloud application reduces setup and non-productive times by the use of virtual machine tools

2016 ◽  
Vol 106 (01-02) ◽  
pp. 77-82
Author(s):  
G. Rehage ◽  
F. Isenberg ◽  
R. Reisch ◽  
J. Weber ◽  
B. Jurke ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Machine Tools ◽  
Distributed Simulation ◽  
Cloud Service ◽  
Planning And Scheduling ◽  
Operations Planning ◽  
Manufacturing Operations ◽  
Cloud Application ◽  
Cognitive Information ◽  
Selection Of

Auf dem Weg zu Industrie 4.0 wird die Arbeitsvorbereitung zunehmend von kognitiver Informationstechnik unterstützt. Der Beitrag präsentiert die bisherigen Ergebnisse des Forschungsprojekts „Intelligente Arbeitsvorbereitung auf Basis virtueller Werkzeugmaschinen“. Projektziel ist eine Cloud-Dienstleistungsplattform zur Reduzierung der Rüst- und Nebenzeiten durch eine intelligente Planung. Hierzu zählen unter anderem die Auswahl und Validierung alternativer Maschinen sowie die automatische Optimierung der Einrichtungsparameter durch verteilte Simulationen.   On the way to industry 4.0, the operations planning and scheduling will be aided by cognitive information systems. This contribution presents the previous findings of a research project called “Smart operations planning and scheduling on the basis of virtual machine tools” (translated from German). The aim of the project is the development of a cloud service for the smart planning of manufacturing operations; that will reduce the setup and non-productive times of machine tools. This is achieved by the automatic selection of alternative CNC machines, as well as the optimization of setup parameters via distributed simulation.

Selection of machine tools — the user

1971 ◽  
Vol 50 (3) ◽  
pp. 93
Author(s):  
K.C. Bairstow
Keyword(s):  
Machine Tools ◽  
Selection Of

Linear electric motors in feed drives of multi-purpose machine tools

2020 ◽  
pp. 47-52
Author(s):  
A.A. Mahov ◽  
O.G. Dragina ◽  
P.S. Belov ◽  
S.L. Mahov
Keyword(s):  
Electric Motor ◽  
Machine Tools ◽  
Electric Motors ◽  
Linear Motors ◽  
Machining Center ◽  
Feed Drives ◽  
Selection Of

The possibility of using linear feed drives along the X and the Y axes in the portal-milling machining center is shown. The calculations of force indicators of drives, feed drives of traverse and carriage for two modes, as well as the selection of Siemens linear motors are given. Keywords milling machining center, drive, feed, linear electric motor. [email protected]

An OSGi-Based Adaptation Access of Machine Tools in the Cloud Manufacturing Environment

2021 ◽  
Author(s):  
Xiaobin Li ◽  
Chao Yin
Keyword(s):  
Information Acquisition ◽  
Machine Tools ◽  
Universal Access ◽  
Cloud Manufacturing ◽  
Manufacturing Environment ◽  
Access Method ◽  
Manufacturing Enterprises ◽  
Cloud Application ◽  
Key Enabling Technologies ◽  
Manufacturing Mode

Abstract Machine tools (MTs) are the core manufacturing resources for discrete manufacturing enterprises. In the cloud manufacturing environment, MTs are massive, heterogeneous, widely dispersed and highly autonomous, which makes it difficult for cloud manufacturing mode to be deeply applied to support the networked collaboration operation among manufacturing enterprises. Realizing universal access and cloud application of various MTs is an essential prerequisite to solve the above problem. In this paper, an OSGi-based adaptation access method of MTs is proposed. First, the MTs information description model in the cloud manufacturing environment is built. Then, an OSGi-based adaptation access framework of MTs is constructed, and key enabling technologies, including machine tool information acquisition and processing, Bundle and Subsystem construction, are studied. Finally, an application case is conducted to verify the effectiveness and feasibility of the proposed method.

Hierarchical Decomposition of a Manufacturing Work Cell to Promote Monitoring, Diagnostics, and Prognostics

2017 ◽  
Author(s):  
Brian A. Weiss ◽  
Guixiu Qiao
Keyword(s):  
Machine Tools ◽  
Health Management ◽  
Control Strategies ◽  
Industrial Robot ◽  
Work Cell ◽  
Critical Elements ◽  
Functional Relationships ◽  
Manufacturing Operations ◽  
Robot Systems ◽  
And Control

Manufacturing work cell operations are typically complex, especially when considering machine tools or industrial robot systems. The execution of these manufacturing operations require the integration of layers of hardware and software. The integration of monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) can aid manufacturers in maintaining the performance of machine tools and robot systems by providing intelligence to enhance maintenance and control strategies. PHM can improve asset availability, product quality, and overall productivity. It is unlikely that a manufacturer has the capability to implement PHM in every element of their system. This limitation makes it imperative that the manufacturer understand the complexity of their system. For example, a typical robot systems include a robot, end-effector(s), and any equipment, devices, or sensors required for the robot to perform its task. Each of these elements is bound, both physically and functionally, to one another and thereby holds a measure of influence. This paper focuses on research to decompose a work cell into a hierarchical structure to understand the physical and functional relationships among the system’s critical elements. These relationships will be leveraged to identify areas of risk, which would drive a manufacturer to implement PHM within specific areas.

Computerized Selection of Optimum Machining Conditions for a Job Requiring Multiple Operations

1978 ◽  
Vol 100 (3) ◽  
pp. 356-362 ◽  
Author(s):  
S. S. Rao ◽  
S. K. Hati
Keyword(s):  
Nonlinear Programming ◽  
Mathematical Programming ◽  
Machine Tools ◽  
Machining Conditions ◽  
Coupling Constraints ◽  
Programming Techniques ◽  
Mathematical Programming Problems ◽  
The Individual ◽  
The Cost ◽  
Selection Of

The problem of determining the optimum machining conditions for a job requiring multiple operations has been investigated. Three objectives, namely, the minimization of the cost of production per piece, the maximization of the production rate and, the maximization of the profit are considered in this work. In addition to the usual constraints that arise from the individual machine tools, some coupling constraints have been included in the formulation. The problems are formulated as standard mathematical programming problems, and nonlinear programming techniques are used to solve the problems.

scholarly journals Modified Flower Pollination based Task Scheduling in Cloud Environment using Virtual Machine Migration

2019 ◽  
Vol 8 (9) ◽  
pp. 1856-1860
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Resource Selection ◽  
Heuristic Algorithms ◽  
Past History ◽  
Fitness Function ◽  
Task Type ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Selection Of

Cloud computing allows users to use resources pay per use model by the help of internet. Users are able to do computation dynamically from different location by using internet resources. The major challenging task in cloud computing is efficient selection of resources for the tasks submitted by users. A number of heuristics and meta-heuristics algorithms are designed by different researchers. The most critical phase is the selection of appropriate resource and its management. The selection of resource include to identify list of authenticated available resources in the cloud for job submission and to choose the best resource. The best resource selection is done by the analysis of several factors like expected time to execute a task by user, access restriction to resources, and expected cost to use resources. In this paper, cloud architecture for resource selection is proposed which combines these factors and make the effective resource selection. In this paper a modified flower pollination algorithm is proposed to migrate the task on efficient virtual machine. The selection of the efficient virtual machine is calculated by the fitness function. By calculating the fitness function, the modified FPA algorithm is used to take the decision regarding VM migration is required to improve the resource efficiency or not. In this paper Virtual machine mapper maps the task as per knowledge base i.e. past history of the virtual machine, task type whether computational or communicational based. The results are compared with the existing meta-heuristic algorithms.

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