Sensitivity Analysis and Monte Carlo Based Uncertainty Quantification of the In-process Modal Parameters in Milling

H. Cherukuri; M. Hashemitaheri; T. Khan; T.T. Le

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Sensitivity Analysis and Monte Carlo Based Uncertainty Quantification of the In-process Modal Parameters in Milling

M. Hashemitaheri, T.T. Le, T. Khan, H. Cherukuri

Numerical Analysis · Submitted 14 Jul 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Also, the performance of the algorithm is evaluated on an empirical data set and its ability to improve the stability boundary is verified.

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arXiv ID2407.10202 (version 1)

Pith ID2SWBB3NW, Pith's permanent record of this paper and its review

CitationH. Cherukuri, M. Hashemitaheri, T. Khan, T.T. Le. "Sensitivity Analysis and Monte Carlo Based Uncertainty Quantification of the In-process Modal Parameters in Milling." arXiv:2407.10202 (2024). https://pith.science/paper/2SWBB3NW

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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.21 · human participants 0.02 · controlled comparison 0.11 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.32 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

The material removal rates during milling operations are affected by the selection of the cutting depth and spindle speed. Poor selection of these parameters can result in chatter or suboptimal material removal rates. Stability Lobe Diagrams (SLDs) are the well-known approach to selecting appropriate chatter-free values for these parameters. The Physics-based stability lobe diagram is usually generated using the structural dynamics and the cutting parameters. However, since the machine dynamics are measured in the static state of the machine (zero speed), the generated SLD is not reliable as the machine behavior may vary during the cutting operations. Besides, measuring structural dynamics parameters under cutting conditions is difficult and needs new equipment. This study proposes a new approach to determining in-process structural dynamics parameters based on a multivariate Newton-Raphson method. The physics-based model is combined with empirical records to extract reliable structural dynamics parameters inversely. Some examples based on synthetic data are presented to illustrate this inverse approach. Also, the performance of the algorithm is evaluated on an empirical data set and its ability to improve the stability boundary is verified. Furthermore, the sensitivity analysis is performed to quantify the exposure of the SLD to the changes in each structural dynamics parameter.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	The material removal rates during milling operations are affected by the selection of the cutting depth and spindle speed.	background 1.00	0.08	0.02	asserted 1.00
s1	Poor selection of these parameters can result in chatter or suboptimal material removal rates.	background 1.00	0.06	0.02	hedged possibility 0.76
s2	Stability Lobe Diagrams (SLDs) are the well-known approach to selecting appropriate chatter-free values for these parameters.	background 1.00	0.03	0.03	asserted 1.00
s3	The Physics-based stability lobe diagram is usually generated using the structural dynamics and the cutting parameters.	background 1.00	0.07	0.03	asserted 0.99
s4	However, since the machine dynamics are measured in the static state of the machine (zero speed), the generated SLD is not reliable as the machine behavior may vary during the cutting operations.	background 0.67	0.09	0.04	asserted 0.52
s5	Besides, measuring structural dynamics parameters under cutting conditions is difficult and needs new equipment.	background 0.64	0.07	0.02	asserted 1.00
s6	This study proposes a new approach to determining in-process structural dynamics parameters based on a multivariate Newton-Raphson method.	contribution 0.97	0.89	0.20	asserted 1.00
s7	The physics-based model is combined with empirical records to extract reliable structural dynamics parameters inversely.	method 0.97	0.85	0.13	asserted 1.00
s8	Some examples based on synthetic data are presented to illustrate this inverse approach.	contribution 0.47	0.90	0.04	asserted 0.99
s9	Also, the performance of the algorithm is evaluated on an empirical data set and its ability to improve the stability boundary is verified.	result 0.74	0.95	0.31	asserted 1.00
s10	Furthermore, the sensitivity analysis is performed to quantify the exposure of the SLD to the changes in each structural dynamics parameter.	method 0.53	0.94	0.11	asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.89.

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arXiv listing abstract v1

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

The material removal rates during milling operations are affected by the selection of the cutting depth and spindle speed. Poor selection of these parameters can result in chatter or suboptimal material removal rates. Stability Lobe Diagrams (SLDs) are the well-known approach to selecting appropriate chatter-free values for these parameters. The Physics-based stability lobe diagram is usually generated using the structural dynamics and the cutting parameters. However, since the machine dynamics are measured in the static state of the machine (zero speed), the generated SLD is not reliable as the machine behavior may vary during the cutting operations. Besides, measuring structural dynamics parameters under cutting conditions is difficult and needs new equipment. This study proposes a new approach to determining in-process structural dynamics parameters based on a multivariate Newton-Raphson method. The physics-based model is combined with empirical records to extract reliable structural dynamics parameters inversely. Some examples based on synthetic data are presented to illustrate this inverse approach. Also, the performance of the algorithm is evaluated on an empirical data set and its ability to improve the stability boundary is verified. Furthermore, the sensitivity analysis is performed to quantify the exposure of the SLD to the changes in each structural dynamics parameter.

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