Automated String Art Creation: Integrated Advanced Computational Techniques and Precision Art Designing Article Swipe
Spoorthi Singh
,
Nidarsh D. Hegde
,
Mohammad Zuber
,
Yuvraj Singh Nain
,
Vishnu G. Nair
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3530970
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3530970
The Thread Art Machine project automates the traditional, labour-intensive process of string art creation by integrating advanced computational and manufacturing techniques. Utilizing CAD models in Fusion 360, CNC machining, 3D printing, and Arduino programming, the machine precisely sets threads and nails to form intricate string art patterns. Key components of the machine include adaptive algorithms for real-time thread tension adjustments and transposed convolution layers for image processing, which enhance both the accuracy and aesthetic quality of the final artwork. The project faced challenges such as material selection, CAD design, and hardware-software interfacing, all of which were addressed through iterative design and validation processes. A convolutional neural network (CNN) was employed to process grayscale images, extracting and reconstructing features using pooling and deconvolution techniques, with the model achieving stable performance over multiple epochs. The machine’ s calibration system, involving LDR sensors and laser alignment, ensures precision in thread placement. The resulting string art pieces, derived from famous images, demonstrate the machine’ s capability to capture key features through varying string densities, offering a blend of mathematical precision and artistic abstraction. Validation through training and testing revealed consistent performance, with minimal overfitting, as indicated by flat training and validation loss and accuracy curves.
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All OpenAlex metadata
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https://openalex.org/W4406524409Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3530970Digital Object Identifier
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Automated String Art Creation: Integrated Advanced Computational Techniques and Precision Art DesigningWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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Spoorthi Singh, Nidarsh D. Hegde, Mohammad Zuber, Yuvraj Singh Nain, Vishnu G. NairList of authors in order
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https://doi.org/10.1109/access.2025.3530970Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3530970Direct OA link when available
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Computer science, String (physics), Software engineering, Mathematics, Mathematical physicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.epochs. | 131 |
| abstract_inverted_index.images, | 113, 156 |
| abstract_inverted_index.include | 52 |
| abstract_inverted_index.machine | 35, 51 |
| abstract_inverted_index.minimal | 188 |
| abstract_inverted_index.network | 106 |
| abstract_inverted_index.pieces, | 152 |
| abstract_inverted_index.pooling | 119 |
| abstract_inverted_index.process | 9, 111 |
| abstract_inverted_index.project | 4, 80 |
| abstract_inverted_index.quality | 74 |
| abstract_inverted_index.sensors | 139 |
| abstract_inverted_index.system, | 136 |
| abstract_inverted_index.tension | 58 |
| abstract_inverted_index.testing | 183 |
| abstract_inverted_index.threads | 38 |
| abstract_inverted_index.through | 97, 166, 180 |
| abstract_inverted_index.varying | 167 |
| abstract_inverted_index.accuracy | 71, 199 |
| abstract_inverted_index.adaptive | 53 |
| abstract_inverted_index.advanced | 16 |
| abstract_inverted_index.artistic | 177 |
| abstract_inverted_index.artwork. | 78 |
| abstract_inverted_index.creation | 13 |
| abstract_inverted_index.employed | 109 |
| abstract_inverted_index.features | 117, 165 |
| abstract_inverted_index.material | 85 |
| abstract_inverted_index.multiple | 130 |
| abstract_inverted_index.offering | 170 |
| abstract_inverted_index.revealed | 184 |
| abstract_inverted_index.training | 181, 194 |
| abstract_inverted_index.Utilizing | 21 |
| abstract_inverted_index.achieving | 126 |
| abstract_inverted_index.addressed | 96 |
| abstract_inverted_index.aesthetic | 73 |
| abstract_inverted_index.automates | 5 |
| abstract_inverted_index.grayscale | 112 |
| abstract_inverted_index.indicated | 191 |
| abstract_inverted_index.intricate | 43 |
| abstract_inverted_index.involving | 137 |
| abstract_inverted_index.iterative | 98 |
| abstract_inverted_index.patterns. | 46 |
| abstract_inverted_index.precisely | 36 |
| abstract_inverted_index.precision | 144, 175 |
| abstract_inverted_index.printing, | 30 |
| abstract_inverted_index.real-time | 56 |
| abstract_inverted_index.resulting | 149 |
| abstract_inverted_index.Validation | 179 |
| abstract_inverted_index.algorithms | 54 |
| abstract_inverted_index.alignment, | 142 |
| abstract_inverted_index.capability | 161 |
| abstract_inverted_index.challenges | 82 |
| abstract_inverted_index.components | 48 |
| abstract_inverted_index.consistent | 185 |
| abstract_inverted_index.densities, | 169 |
| abstract_inverted_index.extracting | 114 |
| abstract_inverted_index.machining, | 28 |
| abstract_inverted_index.placement. | 147 |
| abstract_inverted_index.processes. | 102 |
| abstract_inverted_index.selection, | 86 |
| abstract_inverted_index.transposed | 61 |
| abstract_inverted_index.validation | 101, 196 |
| abstract_inverted_index.adjustments | 59 |
| abstract_inverted_index.calibration | 135 |
| abstract_inverted_index.convolution | 62 |
| abstract_inverted_index.demonstrate | 157 |
| abstract_inverted_index.integrating | 15 |
| abstract_inverted_index.performance | 128 |
| abstract_inverted_index.processing, | 66 |
| abstract_inverted_index.techniques, | 122 |
| abstract_inverted_index.techniques. | 20 |
| abstract_inverted_index.abstraction. | 178 |
| abstract_inverted_index.interfacing, | 91 |
| abstract_inverted_index.mathematical | 174 |
| abstract_inverted_index.overfitting, | 189 |
| abstract_inverted_index.performance, | 186 |
| abstract_inverted_index.programming, | 33 |
| abstract_inverted_index.traditional, | 7 |
| abstract_inverted_index.computational | 17 |
| abstract_inverted_index.convolutional | 104 |
| abstract_inverted_index.deconvolution | 121 |
| abstract_inverted_index.manufacturing | 19 |
| abstract_inverted_index.reconstructing | 116 |
| abstract_inverted_index.machine’ | 133, 159 |
| abstract_inverted_index.labour-intensive | 8 |
| abstract_inverted_index.hardware-software | 90 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.80505775 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |