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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Classify six synthetic surface patterns<\/h2>\n<p>Train an image classifier on 600 steel-like images generated by Artelnics. The six pattern labels are crazing, inclusion, patches, pitted surface, rolled-in scale and scratches.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>600<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>40,000<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>120<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>6<\/strong><span>Model outputs<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Review the current model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-steelsurfacedefects-licensed-20261006.zip\">Download model and data (ZIP)<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Industrial challenge<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li>\n<\/ul>\n<section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Industrial challenge<\/h2><p>Manufacturing teams can use a reproducible image workflow to study class confusion and preprocessing. This example demonstrates image classification using generated textures; it does not measure defect detection on inspected steel.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value-card\"><h3>Six pattern classes<\/h3><p>Inspect errors between synthetic categories.<\/p><\/div><div class=\"ndb-value-card\"><h3>Repeatable generation<\/h3><p>Retain the seed-based source generator.<\/p><\/div><div class=\"ndb-value-card\"><h3>Transfer evaluation<\/h3><p>Use real acquisition data for industrial validation.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Quality engineering<\/span><span>Computer vision<\/span><span>Manufacturing<\/span><\/div><div class=\"ndb-note\">Synthetic image-classification demonstration, not validated industrial inspection.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The dataset contains 100 generated 200 \u00d7 200 grayscale images per class. The generation script and CC BY 4.0 notice are included in the project package. These are synthetic patterns, not the former NEU surface-defect photographs.<\/p><p>Source: Artelnics Synthetic Steel Surface Defects. Dataset license: CC-BY-4.0. The downloadable ZIP includes the adapted data and attribution notices.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Dataset measure<\/th><th scope=\"col\">Saved value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Analysis unit<\/th><td>generated image<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>600<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>2<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>40,000<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>6<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>360<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>120<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>120<\/td><\/tr><tr><th scope=\"row\">Unused roles<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Field<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Categories<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">image<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">Class<\/th><td>Target<\/td><td>Categorical<\/td><td>crazing, inclusion, patches, pitted_surface, rolled-in_scale, scratches<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-steelsurfacedefects-t0-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-steelsurfacedefects-t0-s2-20261006\" role=\"img\" aria-label=\"Target class distribution pie chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-steelsurfacedefects-t0-s2-20261006.png\" alt=\"Target class distribution pie chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"crazing\",\"inclusion\",\"patches\",\"pitted_surface\",\"rolled-in_scale\",\"scratches\"],\"marker\":{\"colors\":[\"#b5dff4\",\"#6abfea\",\"#209fdf\",\"#1879aa\",\"#115375\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[16.670000076293945,16.670000076293945,16.670000076293945,16.670000076293945,16.670000076293945,16.670000076293945]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Target class distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"height\":500}}<\/script><\/div><figcaption>Target class distribution pie chart. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"ndb-note\">The saved project assigns rows to training, validation and testing as shown above. This is internal record-level evaluation; it does not demonstrate separation by subject, device, site or acquisition batch.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The model has 40000 encoded inputs and 6 outputs. No architecture-selection experiment is recorded in this project. The diagram shows the topology used by the saved model.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>200 \u00d7 200 \u00d7 1<\/td><td>200 \u00d7 200 \u00d7 1<\/td><td><\/td><\/tr><tr><th scope=\"row\">Convolutional<\/th><td>200 \u00d7 200 \u00d7 1<\/td><td>200 \u00d7 200 \u00d7 8<\/td><td>ReLU<\/td><\/tr><tr><th scope=\"row\">Pooling<\/th><td>200 \u00d7 200 \u00d7 8<\/td><td>100 \u00d7 100 \u00d7 8<\/td><td><\/td><\/tr><tr><th scope=\"row\">Flatten<\/th><td>100 \u00d7 100 \u00d7 8<\/td><td>80000<\/td><td><\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>80000<\/td><td>128<\/td><td>ReLU<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>128<\/td><td>6<\/td><td>Softmax<\/td><\/tr><\/tbody><\/table><\/div><p>Output values are uncalibrated model scores. Use the output encodings and decision rule documented with this project; do not assume independent sigmoid scores sum to one.<\/p><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-steelsurfacedefects-t1-s1-20261006.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-steelsurfacedefects-t1-s1-20261006.png\" alt=\"Synthetic steel surface defect classification \u2014 initial network architecture\"><\/a><figcaption>Topology of the saved current model; no architecture selection is recorded.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses Adam with CrossEntropy.<\/p><h3>Adaptive moment estimation results<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>200<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:06<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum epochs number<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.035<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.657<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-steelsurfacedefects-t2-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-steelsurfacedefects-t2-s2-20261006\" role=\"img\" aria-label=\"Adaptive moment estimation error history\"><\/div><img 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded for this version. The validation subset guides fitting where a training report is present; it is distinct from the held-out test rows.<\/p><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The figures and tables below refer to the current project\u2019s saved testing analysis. The subset uses testing role 2; it contains 120 source records.<\/p><p>The multiclass decision uses the largest output score. The confusion matrix gives class prevalence and per-class errors; binary sensitivity, specificity, precision and a single ROC AUC are not interchangeable with this multiclass accuracy.<\/p><h3>Confusion table<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted crazing<\/th><th scope=\"col\">Predicted inclusion<\/th><th scope=\"col\">Predicted patches<\/th><th scope=\"col\">Predicted pitted_surface<\/th><th scope=\"col\">Predicted rolled-in_scale<\/th><th scope=\"col\">Predicted scratches<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual crazing<\/th><td>21 (17.5%)<\/td><td>1 (0.8%)<\/td><td>0<\/td><td>2 (1.7%)<\/td><td>0<\/td><td>0<\/td><td>24 (20.0%)<\/td><\/tr><tr><th scope=\"row\">Actual inclusion<\/th><td>0<\/td><td>10 (8.3%)<\/td><td>12 (10.0%)<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>22 (18.3%)<\/td><\/tr><tr><th scope=\"row\">Actual patches<\/th><td>0<\/td><td>0<\/td><td>16 (13.3%)<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>16 (13.3%)<\/td><\/tr><tr><th scope=\"row\">Actual pitted_surface<\/th><td>0<\/td><td>7 (5.8%)<\/td><td>0<\/td><td>10 (8.3%)<\/td><td>0<\/td><td>0<\/td><td>17 (14.2%)<\/td><\/tr><tr><th scope=\"row\">Actual rolled-in_scale<\/th><td>0<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>22 (18.3%)<\/td><td>0<\/td><td>22 (18.3%)<\/td><\/tr><tr><th scope=\"row\">Actual scratches<\/th><td>0<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>0<\/td><td>19 (15.8%)<\/td><td>19 (15.8%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>21 (17.5%)<\/td><td>18 (15.0%)<\/td><td>28 (23.3%)<\/td><td>12 (10.0%)<\/td><td>22 (18.3%)<\/td><td>19 (15.8%)<\/td><td>120 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Test measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Testing records<\/th><td>120<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>81.67%<\/td><\/tr><tr><th scope=\"row\">Largest-class baseline<\/th><td>20.00%<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Open the downloaded project in Neural Designer, inspect the dataset roles and preprocessing, then review the saved task report. Use the same input schema and category order when calculating outputs. The ZIP contains the exact current .nd, its source data and the applicable dataset notices.<\/p><p>Workflow: source measurements \u2192 schema and availability checks \u2192 model output \u2192 domain review. Keep model versions, validation evidence and incoming-data monitoring together.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-steelsurfacedefects-licensed-20261006.zip\">Download model, data and licenses (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Scope and limitations<\/h2><p>A random split of images from the same generator measures within-generator performance. Validate with independently labelled real surfaces, different acquisition conditions and lot-separated testing before production inspection. Similar appearance does not establish the physical cause of a defect.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li>Artelnics Synthetic Steel Surface Defects. Artelnics. Not assigned<\/li><li>Dataset terms: Creative Commons Attribution 4.0 International. 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