{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YBFBEM3ZFAWJ7TOJXCHWAVECJ5","short_pith_number":"pith:YBFBEM3Z","schema_version":"1.0","canonical_sha256":"c04a123379282c9fcdc9b88f6054824f7f9a060c1e1336bdaa4b1909c39ca4ab","source":{"kind":"arxiv","id":"1808.02518","version":2},"attestation_state":"computed","paper":{"title":"Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kincho H. Law, Max Ferguson, Ronay Ak, Yung-Tsun Tina Lee","submitted_at":"2018-08-07T18:57:41Z","abstract_excerpt":"Quality control is a fundamental component of many manufacturing processes, especially those involving casting or welding. However, manual quality control procedures are often time-consuming and error-prone. In order to meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. Recently, Convolutional Neural Networks (CNNs) have shown outstanding performance in both image classification and localization tasks. In this article, a system is proposed for the identification of casting defects in X-ray images, based"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1808.02518","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-07T18:57:41Z","cross_cats_sorted":[],"title_canon_sha256":"a50a58753943bc576329afd9b1675c388dcb9e8c9f556d0d0de843ae7154eee4","abstract_canon_sha256":"3ee0200460174fbcc5c7eb01da83c6e8807de4f9feff4edc8867537043a4c77d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:37.151330Z","signature_b64":"M6OAOJs8DncWY61JYmx6L/VuuGwrvMSP0K0PF1WRjABCaeeSBf1DIP9Fw3tBmKmI2HBRKFubQxKSbpYBTE79AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c04a123379282c9fcdc9b88f6054824f7f9a060c1e1336bdaa4b1909c39ca4ab","last_reissued_at":"2026-05-18T00:06:37.150952Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:37.150952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kincho H. Law, Max Ferguson, Ronay Ak, Yung-Tsun Tina Lee","submitted_at":"2018-08-07T18:57:41Z","abstract_excerpt":"Quality control is a fundamental component of many manufacturing processes, especially those involving casting or welding. However, manual quality control procedures are often time-consuming and error-prone. In order to meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. Recently, Convolutional Neural Networks (CNNs) have shown outstanding performance in both image classification and localization tasks. In this article, a system is proposed for the identification of casting defects in X-ray images, based"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.02518","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1808.02518","created_at":"2026-05-18T00:06:37.151008+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.02518v2","created_at":"2026-05-18T00:06:37.151008+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.02518","created_at":"2026-05-18T00:06:37.151008+00:00"},{"alias_kind":"pith_short_12","alias_value":"YBFBEM3ZFAWJ","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YBFBEM3ZFAWJ7TOJ","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YBFBEM3Z","created_at":"2026-05-18T12:33:04.347982+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5","json":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5.json","graph_json":"https://pith.science/api/pith-number/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/graph.json","events_json":"https://pith.science/api/pith-number/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/events.json","paper":"https://pith.science/paper/YBFBEM3Z"},"agent_actions":{"view_html":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5","download_json":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5.json","view_paper":"https://pith.science/paper/YBFBEM3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.02518&json=true","fetch_graph":"https://pith.science/api/pith-number/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/graph.json","fetch_events":"https://pith.science/api/pith-number/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/action/storage_attestation","attest_author":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/action/author_attestation","sign_citation":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/action/citation_signature","submit_replication":"https://pith.science/pith/YBFBEM3ZFAWJ7TOJXCHWAVECJ5/action/replication_record"}},"created_at":"2026-05-18T00:06:37.151008+00:00","updated_at":"2026-05-18T00:06:37.151008+00:00"}