Abstract
The ongoing competition on cutting tools drive the tool manufactures to enhance the cutting tool quality through real-time condition monitoring and performance prediction. The digitization of tool within the manufacturing process, allows for the creation of an intelligent manufacturing system, to reduce the cycle time required for testing, and facilitate the categorization of tool performance as well as early warning signals of the change in the manufacturing processes. The system comprises two platforms such as feature extraction engine (FEE) and feature prediction engine (FPE). The FEE monitors real-time progression of operational behavior during wear with the tool–work interface signals. The accelerated tool wear testing applies a milling arrangement, incorporating a clock-testing workpiece that simulates an intermittent cutting process on hardened steel. The feature extraction engine uses the accelerated wear results to build a calibrated wear model as a reference tool for wear analysis and prediction. Flank wear lands were imaged using a Leica toolmaker’s microscope and used to calibrate the wear model, correlating the digital signal feature to the tool–work interface wear behavior. The imaged wear progression, force, and acoustic emission signal features were analyzed by statistical methods including applications of Spiro-Wilks, ANOVA, and Kruskal–Wallis evaluations. This confirms the experimental accuracy and provides the baseline for wear prediction driving the development of a feature prediction engine (FPE). The results are a significant reduction in the quality control cycle time and performance prediction. The experimental results indicate that the FEE correlates accurately across sensors and progression tracking of abrasive wear in the cutting tools, clearly distinguishing between machining cuts, signal noise, and signal anomalies.
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Abbreviations
- \( \sigma_{\text{y}} \) :
-
Yield strength of the PCD cutting insert (GPa)
- μ :
-
Population mean of the test samples
- E :
-
Young’s modulus of the PCD (GPa)
- F n :
-
Normal force exerted on the workpiece during machining (N)
- \( H_{\text{PCD}} \) :
-
Hardness of the PCD matrix (GPa)
- i :
-
Number of data points
- \( K_{\text{IC}} \) :
-
Fracture toughness of the PCD insert (MPa m1/2)
- \( l_{c} \) :
-
Cutting distance traversed (m)
- l :
-
Number of hidden units
- L :
-
Levene’s statistic (L)
- k :
-
Number of sets from which the data come
- m :
-
Work hardening factor
- n :
-
Size of the sample
- N :
-
Total number of sampled cases
- P :
-
Weighted sums in a nonlinear model
- s :
-
Standard deviation of the experimental data
- t :
-
“t” factor
- \( V_{\text{abrasive}} \) :
-
Volume lost from the cutting insert (m3)
- x :
-
Sample mean
- w :
-
Neuron weights
- Z :
-
Array of the mean and median values of the sampled case i from group j
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Acknowledgements
The authors wish to thank Mr. Jonathan Waite University of Cape Town, Cape Town, South Africa for the important contributions made to this research in the initial PCD clock-testing experiments. Further thanks go to Element 6 for the supply of PCD inserts and AE sensors. This project was supported by fund NRF Grant: Incentive Funding for Rated Researchers (IPRR)–South Africa through Reference: IFR150204113619 and Grant No: 96066.
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Appendix 1: Computation of Volumetric Wear Using the Principles Cylindrical Wedge
Appendix 1: Computation of Volumetric Wear Using the Principles Cylindrical Wedge
A wedge is cut from a cylinder by slicing with a plane that intersects the base of the cylinder. The volume of a cylindrical wedge can be found by noting that the plane cutting the cylinder passes through the three points illustrated above (with b > R), so the three-point form of the plane gives the equation
Solving for z is given as
The value of “a” is given as
The volume of cylindrical wedge is given as the integral of rectangular areas along the x-axis
Hence, \( V = \frac{2}{3}hR^{2} \).
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Kuppuswamy, R., Airey, K.A. (2018). Intelligent PCD Tool Testing and Prediction of Performance. In: Pande, S., Dixit, U. (eds) Precision Product-Process Design and Optimization. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-8767-7_7
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