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       Computer
numerical control (CNC) technology has been one of manufacturing’s major
developments in the past 50 years. Generating the part program, or tool path,
for the product to be manufactured by CNC machines is considered very
important. Therefore, many techniques have been developed to generate CNC part
programs, for example using developed software and computer aided design/manufacturing
(CAD/CAM) systems. Machine vision is concerned with utilizing existing
technology in the most effective way to provide a degree of independency in
specific applications. In fact, the human visual system is extremely complex
and cannot be simulated at all, but some of its properties can be simulated.
Building a computer vision system that emulates the essential functionality of
the human system may seem a speciously small objective to the uninformed, but
in fact it takes a great deal of work computer vision has been recently used in
many applications in the field of production engineering. These applications
include sheet metal cutting, lace cutting, feature recognition, reverse
engineering, surface texture, dimensional measurements, defect analysis, tool
wear assessment and classification of machined features (Eladawi,
2013).

       When it
comes to the MTS CNC Simulators, which are employed in production as well as in
vocational training and further education. Every effort has been made to meet
the complex requirements in both these fields of application by including
appropriate new functions in the current software, which makes the MTS Simulators
one of the most efficient systems on the market today. This high performance is
accompanied by increased clarity and convenience of operation, due to the new user
interface and function keys (Arthaya, 2014).

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On the other hand, an operation training simulation system for a
computer numerical control (CNC) machine includes a physical control device for
inputting control and operation commands, a software simulator connected with
the physical control device, a transmission equipment connecting the physical
control device with the software simulator and a simulation display
electrically connected to the software simulator. The software simulator
generates machining and operation simulations based on the inputted control and
operation conditions through the physical control device, and the simulation
display visualizes the machining and operation simulations generated by the
software simulator. Therefore, using the operation training simulation system
for CNC machine not only eliminates the consumptive materials cost but also
solve the personal incorrect problem. Consequently, the learning efficiency of
the trainees and the adaptation to machine are raised in this way (patents,
2016).

 

 

5.1 Problems and
methods of simulation

           

         some important issues such as NC
program interpretation, edge finding, tool length measurement and collision
detection in virtual machine have infrequently been integrated yet. These
issues are key factors in emulating a physical intelligent machine tool by a
smart virtual one in reflecting the coordinate system mappings, machine
movement, and collision prevention algorithm. The main was introducing that the
development of a smart virtual vertical three-axis machining center simulator.
In result, that was showed in Fig 8,
Dynamic toolpath movement, solid cutting simulation, tool exchange, collision
detection, and coolant effect have also been successfully developed to facilitate
a smart virtual machine tool (Kadir, et al., 2011).

 

 

 

 

    

 

 

Fig 8: Progress of turning operation
in the simulation window.

 

        The
covariate effect of the subject possible prior knowledge in the field. most
experimental designs have the main effects which are caused by the experiment’s
treatment and then there are variate effects caused by typically uncontrollable
factors. The main methods were 1) System Definition 2) Model Formulation 3)
Model Translation 4) Verification & Validation (Yip-Hoi,
2013).

         when
new machinery or more modern production systems introduced, a nonnegligible
uncertainty is always generated, especially on the most of veteran workers. It
is the responsibility of the factory manager to carefully manage these
improvements, since an inappropriate worker updating could have a negative reflection
on the application times. The main method was descripted that the implemented
EMC2 version and the simulator of a common commercial CAM software (Edge CAM).
In result, open architecture of EMC2 allows the user to execute automatic
procedures, it has been possible to capture pictures in specific moments during
machining processes, at the same points at different depths (S, et al.,
2010).

         According
to Mr. Sato, the simulation makes possible to analyze causes of inaccuracy
without developing any prototype, and the model for the simulation can be
applied to controller tuning. Thus,
using feed drive system which can be designed systematically by using the
proposed feed drive simulator, and parameter identification module of proposed
feed drive simulator can systematically identify all the parameters for the
simulations (Sato, 2013). Showed in Fig 9.

 

 

 

   

 

 

Fig 9: GUI of friction parameter identification function

 

 

 

 

 

 

 

 

 

 

5.2 Challenges and
future development of simulation

 

       Training
simulator can be used as a base model to develop training simulators for setup
procedures on other CNC equipment. The development of similar simulators is
expected to enrich the learning experience of the student suffering engineering
education at the university. Also, another area of work may be more extensive
testing of the effectiveness of the training simulator as compared to
traditional classroom based training. This effort explores the teaching
effectives of the simulator from an absolute stand point and does not compare
the training simulator against other methods. (Song, et al.,
2016)
This is justifiable as the simulator was designed to overcome resource and
distance education constraints.  However,
it would be interesting to study the statistical difference in knowledge gain
that results from the use of the training simulator as opposed to the knowledge
gain that results from classroom based and other traditional approaches to
training. (Yip-Hoi, 2013)

     On the other hand, Yung-Chou Kao also
addressed that dynamic toolpath movement, solid cutting simulation, tool
exchange, collision detection, and coolant effect have also been successfully
developed to facilitate a smart virtual machine tool. Future development will
focus on the incorporation of machining geometric errors reduction through
cutting force estimation and assembly errors

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