Method for edge chipping monitoring based on vibration polar coordinate image feature analysis Article Swipe
Zhenxi Jiang
,
Fuji Wang
,
Wenping Mou
,
Shaowei Zhu
,
Rao Fu
,
Zhiyong Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3749158/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3749158/v1
Cutting edge chipping is a randomly-happened but critical tool failure, which could seriously affect machining quality without effectively and timely treatment especially in the automated machining process. Accurately and timely monitoring chipping is the first key step for properly handling edge chipping issues, while there are still gaps between academic research and industrial application as chipping initiation and propagation are always stochastic especially for milling complex structural parts with mass of ever-changing cutting conditions. This paper proposes a chipping monitoring method for automated milling process based on cutting vibration polar coordinate image features and residual neural network (ResNet) model, which lies on the change of cutting vibration signals that reveal just after an event of edge chipping has occurred. With the creatively proposed polar coordinate transform method, cutting vibration signals are first preprocessed for real-time chipping detection. The preprocessed signals with easily distinguished shape features of polar coordinate images, are then employed for developing a ResNet50 based classification model. And consequently, the cutting edge chipping could be monitored in almost real time. Experimental validation for real-time chipping detection has been conducted with the proposed method. The accuracy of No. 5 ~ 8 experiments are 92%, 88%, 83% and 84%, respectively. More importantly, the method is suited for edge chipping monitoring for milling complex structural parts, where effects of constantly changing cutting conditions have been eliminated.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3749158/v1
- https://www.researchsquare.com/article/rs-3749158/latest.pdf
- OA Status
- green
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389849153
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389849153Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-3749158/v1Digital Object Identifier
- Title
-
Method for edge chipping monitoring based on vibration polar coordinate image feature analysisWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-16Full publication date if available
- Authors
-
Zhenxi Jiang, Fuji Wang, Wenping Mou, Shaowei Zhu, Rao Fu, Zhiyong YuList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3749158/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-3749158/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-3749158/latest.pdfDirect OA link when available
- Concepts
-
Enhanced Data Rates for GSM Evolution, Computer vision, Feature (linguistics), Vibration, Polar coordinate system, Polar, Artificial intelligence, Image (mathematics), Computer science, Pattern recognition (psychology), Mathematics, Acoustics, Physics, Geometry, Linguistics, Astronomy, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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22Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2167218164, https://openalex.org/W2958618010, https://openalex.org/W2321274876, https://openalex.org/W3037998911, https://openalex.org/W3028108692, https://openalex.org/W3024839313, https://openalex.org/W4288045586, https://openalex.org/W3116183085, https://openalex.org/W2463378445, https://openalex.org/W3010834209, https://openalex.org/W2892848815, https://openalex.org/W2800876756, https://openalex.org/W3009567396, https://openalex.org/W2918033326, https://openalex.org/W4250929268, https://openalex.org/W2800416900, https://openalex.org/W2774268753, https://openalex.org/W2285945267, https://openalex.org/W2141663819, https://openalex.org/W2967036568, https://openalex.org/W3154788462, https://openalex.org/W2938021361 |
| referenced_works_count | 22 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5033187103 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I27357992 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.224217 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |