Intelligent Vision Based Wear Forecasting on Surfaces of Machine Tool Elements

Niklas Burghardt; Tobias Schlagenhauf

RECORD

Intelligent Vision Based Wear Forecasting on Surfaces of Machine Tool Elements

Tobias Schlagenhauf, Niklas Burghardt

Machine Learning · Submitted 12 Jun 2021

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

The authors' main claim The authors show that they can do both, detect and prognose the evolution of a failure on the surface of a Ball Screw Drive.

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arXiv ID2106.06839 (version 2)

Pith IDS7REZRME, Pith's permanent record of this paper and its review

CitationNiklas Burghardt, Tobias Schlagenhauf. "Intelligent Vision Based Wear Forecasting on Surfaces of Machine Tool Elements." arXiv:2106.06839 (2021). https://pith.science/paper/S7REZRME

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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,900 input tokens.

What the abstract itself states, as probabilities: claims first 0.95 · states limitation 0.16 · human participants 0.03 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.26 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; show every role.

This paper addresses the ability to enable machines to automatically detect failures on machine tool components as well as estimating the severity of the failures, which is a critical step towards autonomous production machines. Extracting information about the severity of failures has been a substantial part of classical, as well as Machine Learning based machine vision systems. Efforts have been undertaken to automatically predict the severity of failures on machine tool components for predictive maintenance purposes. Though, most approaches only partly cover a completely automatic system from detecting failures to the prognosis of their future severity. To the best of the authors knowledge, this is the first time a vision-based system for defect detection and prognosis of failures on metallic surfaces in general and on Ball Screw Drives in specific has been proposed. The authors show that they can do both, detect and prognose the evolution of a failure on the surface of a Ball Screw Drive.

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	This paper addresses the ability to enable machines to automatically detect failures on machine tool components as well as estimating the severity of the failures, which is a critical step towards autonomous production machines.	background 0.62	0.18	0.09	asserted 0.92
s1	Extracting information about the severity of failures has been a substantial part of classical, as well as Machine Learning based machine vision systems.	background 1.00	0.04	0.03	asserted 1.00
s2	Efforts have been undertaken to automatically predict the severity of failures on machine tool components for predictive maintenance purposes.	background 1.00	0.08	0.03	asserted 0.97
s3	Though, most approaches only partly cover a completely automatic system from detecting failures to the prognosis of their future severity.	gap 0.96	0.08	0.04	asserted 0.95
s4	To the best of the authors knowledge, this is the first time a vision-based system for defect detection and prognosis of failures on metallic surfaces in general and on Ball Screw Drives in specific has been proposed.	contribution 0.81	0.44	0.22	asserted 0.98
s5	The authors show that they can do both, detect and prognose the evolution of a failure on the surface of a Ball Screw Drive.	contribution 0.50	0.95	0.81	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.85.

What was examined, and what was not

sources read

arXiv listing abstract v2

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

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S sections
T body sentences
C citations
R cross-paper relations

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

The paper

Original abstract

This paper addresses the ability to enable machines to automatically detect failures on machine tool components as well as estimating the severity of the failures, which is a critical step towards autonomous production machines. Extracting information about the severity of failures has been a substantial part of classical, as well as Machine Learning based machine vision systems. Efforts have been undertaken to automatically predict the severity of failures on machine tool components for predictive maintenance purposes. Though, most approaches only partly cover a completely automatic system from detecting failures to the prognosis of their future severity. To the best of the authors knowledge, this is the first time a vision-based system for defect detection and prognosis of failures on metallic surfaces in general and on Ball Screw Drives in specific has been proposed. The authors show that they can do both, detect and prognose the evolution of a failure on the surface of a Ball Screw Drive.

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