essential to effective manufacturing

 

Title of Task Write a research essay to discuss some CNC machine time existing prediction methods/techniques
Deadline 5-8 days
Word Count 4,000 words (+/- 10%)

 
Task brief
Accurate machine time estimation is essential to effective manufacturing. Currently, machine time estimation methods or techniques are vastly practiced in the industry.
Note:
The existing research was conducted previously as indicated below. The red text highlights the existing work which will account for 100% plagiarism. The task is to rewrite each provided (8) links. The links to the article are provided they are all checked and working, in case of broken link then please ask. You may delete the red highlighted text once finished as its there for your better understanding. When rewriting the authors prediction methods/techniques focus on the following:

  • Why the author did the research/what problem he had?
  • What background research/Literature he conducted.
  • His machine time prediction methods/techniques.
  • Results & Conclusion, whether the machine time prediction methods/techniques was success or failed etc.

There is no need for any further research as all the links are provided, if it is necessary then feel free to do so. No references are needed. Plz use the below template to complete the work.

1.0 Existing Methods/Techniques

1) Feature-based formula for time estimation
Link for this article: https://link.springer.com/content/pdf/10.1007/BF01350820.pdf
When rewriting this article focus on the following and for the rest of the links:

  • Why the author did the research/what problem he had?
  • What background research/Literature he conducted.
  • His machine time prediction methods/techniques.
  • Results & Conclusion, whether the machine time prediction methods/techniques was success or failed etc.

Yang and Lin (1997) proposed a milling time estimation based on formulas, as the machining time is directly related to the volume of the material removed the estimated time can be computed based on the formula in figure 4.
The variable p defines a feature such as a length, width, and height of a through the slot, K is the value which is dependent on the type of the machining operation. The drawback of such an approach is only limited features can be estimated, time-consuming for predicting complex geometry, etc.
2) Feature-based time estimation
Link for this article: https://drive.google.com/file/d/10Nn6D9JBdL09kde6u60FIzLWks-qclek/view?usp=sharing
Changqing et al. (2013) set up a machine time estimation based on the machining feature. The model works by combining factors such as part geometry information, process plan, machine characteristics, and NC program. The framework is divided into three levels namely data preparation level where data such as geometry information, NC program information, and data for machining time estimation are being prepared. The information is then passed onto the data level where every three individual modules are integrated into an information chain through the machine and feature coding. Finally, the data calculation level is where machine time is calculated using information from the data level and the geometry process.
To test the system the authors developed a test part on CATIA CAD software, the CAD model was introduced to the system from where the information on the geometry, NC programming, etc, were extracted. The machining time calculation is then based on the extracted files, figure 5 shows the process of this estimation.
3) Machine time estimation algorithm based on machine characteristics
Link for this article: https://drive.google.com/file/d/1GtWwPf6amT-ujwqELWuaawXwl83RWBBx/view?usp=sharing
An algorithm was developed by So et al. (2007) which estimates machining time for 5 axes high-speed machining. The authors first considered the factors which influence the machining speed such as:   Feed angle: the cutter change in direction from one position to another successive NC blocks, it’s defined by the formula as shown in figure 6.
 
 
 
 
Processing speed: is where the machine speed is controlled by the formula. The formula defines the number of NC blocks which can be processed by the machine tool based on the command federate (Fc).
 
The ratio of rotational to translation motion: The movement of the five-axis machine may differ in terms of rotational and translational motion, as they are being powered by the combination of the servo motor and gear ratio. Hence, the authors have defined the rotational and translational motion in terms of a ratio, so the resulting speed of the machine is not affected.
 
To estimate the machining time, a schematic diagram of the algorithm is illustrated in figure 7. The estimation of machine time for the whole NC data by the formula is shown in the below figure.
 
 
 
 
 
 
 
 
 
 
4) Mechanistic approach to predict real machining time
Link for this article: https://drive.google.com/file/d/14XIS5lR6OhRWf8cj5ts2I6czqjWDqqeG/view?usp=sharing
Coelho et al. (2010) approach to estimate machine time was based on the NC program which was generated by CAM software and then by using a variable called machine response time (MRT) which represents a real CNC machine. Firstly, an experiment is conducted on a CNC machine using the NC program to obtain the MRT feature of it. The MRT is calculated using the below formula, where  is the segment length and  is the real feed rate.
 
Once the MRT data is obtained, software is used which executes the algorithm. The framework of the software and the user interface is shown in figure 8.
 
5) Estimation of CNC machining time using neural computing
Link for this article: https://drive.google.com/file/d/11yMMVVDs4LrOuhPJqeo1QJFXpQudytet/view?usp=sharing
Saric et al. (2016) created a neural computing technique that would function just like a human brain. For this experiment, the authors choose a vertical CNC machining, where a model vector was defined as in the below equation.
Where X and Y represent the input and output variables respectively. The input variable data from the actual production process was selected and defined with max and min values as shown in the table below. The experiment was investigated with different algorithms for estimating the output variable Y gives the machining time.
 
 
 
 
 
 
 
6) Time estimation based on material removal rate
Link for this article: https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0135618#downloadfiles
Othmani et al. (2008) have developed a method based on material removal rates, which allows the user to calculate the time of a machining workpiece. The authors have divided the machining time Tm into five parts. The term tc defines the tool movement at work federate; tr is the movements of the tool at rapid federate; tchain is the time taken for tool change; tchar is the time for tool rotation and ta is the auxiliary time.
 
7) NC machine time estimation model for machining sculptured surfaces
Link for this article: https://drive.google.com/file/d/19U9TxlxRL21eVJgirKS9-9vV5PQqijZq/view?usp=sharing
 
write this article focus on the following and for the rest of the links:

  • Why the author did the research/what problem he had?
  • What background research/Literature he conducted.
  • His machine time prediction methods/techniques.
  • Results & Conclusion, whether the machine time prediction methods/techniques was success or failed etc.

 
8) TIME PREDICTING APPARATUS OF NUMERICALLY CONTROLLED MACHINE TOOL
Link for this article: https://drive.google.com/file/d/1sERi4Fh68TxGCA29M-A925t3FNn3nUNb/view?usp=sharing
write this article focus on the following and for the rest of the links:

  • Why the author did the research/what problem he had?
  • What background research/Literature he conducted.
  • His machine time prediction methods/techniques.
  • Results & Conclusion, whether the machine time prediction methods/techniques was success or failed etc.

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