1. “Use of contour signatures and classification methods to optimize the tool life in metal machining” Enrique Alegrea, Rocío Alaiz-Rodrígueza, Joaquín Barreirob and Jonatan Ruiz Presented By: Kurt Hendricks 16 Oct 2009 Tool Life Paper Published in the Estonian Journal of Engineering Volume 15 Issue 1 (January 2009)

2. Introduction Purpose: Define a new procedure which improves decisions about tool replacement Optimize tool life because tool replacement contributes greatly to the cost of production – Replacing a tool more often than necessary can be as costly as using a worn tool Tool Life Paper

3. References Tool Life Paper

4. Design Relates to the design parameters used to estimate tool life 2 types of methods for determining tool wear – Direct Methods Follow threshold values set by standards (ISO3685) Determined by complex measurements – Alternative Methods As simple as an operator’s experience; sight and sound Indirect measurement of tool wear areas Tool Life Paper

5. Design Problems – Threshold measurements are difficult to obtain and standard values are conservative – Indirect measurements have poor precision and reliability Solution: Combine them – Use “computer vision” to more precisely measure for the already defined standard Tool Life Paper

6. Design Parameters Computer vision is a well developed technology – Take digital images and process them for information Signature – represent a contour using a one dimensional function; basically a vector with many elements, each giving the location of a region Tool Life Paper

7. Design Parameters After the image is processed to binary, it is reduced to just a perimeter The starting point of the signature is the most upper right corner Tool Life Paper

8. Design Parameters Two criterion set up by the ISO standard – V BB is the average width of the wear band on tool – V BC max is the maximum width of wear band Measured using vector describing distance of each element to the centroid – Two vectors are used 40 element 100 element Tool Life Paper

9. Design The principle behind this setup is the standard set forth to measure tool wear (ISO 3865) – Basically, the more effective direct approach will be taken to describe tool wear – Making the measurements easier and faster will give the improvement of optimizing tool use that hasn’t yet been achieved by direct measurement Tool Life Paper

10. Experiment Image acquisition and processing equipment – Black and White camera and digitalization card used to take and process the images Machining – CNC Parallel lathe and rhombic tungsten carbide inserts Cutting Speeds: 140 – 200 m/min (460 – 660 ft/min) Feed rate: 0.2 mm/rev (0.008 in/rev) Cutting depth: 2 mm (0.080 in) – 4340 and 4140 Steel cylinders 90 mm in diameter and 250 mm in length (about 3.5” and 10”) Tool Life Paper

11. Results Classification of results – V BB was chosen over V BC max – K-NN meaning k nearest neighbor Statistical random sampling with Euclidean distance – MLP meaning multilayer perceptron Based on number of nodes and training cycles – Both classifications come from the neural networks Authors making results compatible to other research done for comparison and validation Tool Life Paper

12. Results Classification techniques are not discussed in the paper, but the output of the analysis is a number representing error. For example 5.3% percent error is the lowest value achieved using the 100 signature vector and occurs at 30 nodes and 300 cycles Tool Life Paper

13. Results Notice that with the 40 signature vector the error actually comes down to 5.1% Tool Life Paper

14. Results The K-NN values are also pre presented but are marginally l lower than the MLP method – A complete comparison: 0 is failure and 1 is still good K-NN accepts failure when tool is actually still good. MLP is the superior classification Tool Life Paper

15. Conclusions Computer Vision can use digital images to accurately calculate tool wear – 40 elements in the signature proved more precise than adding elements to the signature vector – Using the average width of wear band gives greater accuracy than using the maximum width – Certain classifications work better than others (MLP better than K-NN) Tool Life Paper

16. Conclusions This has a very important application in all machining operations – Digital image and analysis without completely stopping machine – Extending use of tool; No changing when still good – Removes uncertainty or need for years of experience The technology of computer imaging is well developed already – Applying it to machining is in and of itself a huge advancement across all of industry Tool Life Paper

17. Use of contour signatures and classification methods to optimize the tool life in metal machining” Alegre, E., Alaiz-Rodríguez, R., Barreiro, J., & Ruiz, J. (2009). Use of contour signatures and classification methods to optimize the tool life in metal machining. Estonian Journal of Engineering, 15(1), 3-12. http://search.ebscohost.com.erl.lib.byu.edu, doi:10.3176/eng.2009.1.01 Tool Life Paper Presented By: Kurt Hendricks 16 Oct 2009 Questions?