{"id":"06999","slug":"novel-anomaly-detection-in--06999","source":{"id":"06999","dataset":"techtransfer","title":"Novel Anomaly Detection in Additive Manufacturing","description_":"<p>University of Pittsburgh researchers have developed a novel artifact to monitor the quality of an additive manufacturing process and detect anomalies in-situ. This novel artifact, specifically designed for the Laser Powder Bed Fusion additive manufacturing (LPBF-AM) process, can detect mid-process anomalies in a cost effective and timely manner without the need for specialist systems. This novel artifact could aid manufacturer decision-making in real time to account for anomalies, reduce wastage, decrease the risk of safety failures, and improve production workflows.<p><img src=\"https://s3.us-east-1.amazonaws.com/static.tto.c8e.ai/upitt/attachments/06999/0EMVv00000RN4g5.png\"></p></img>A novel robust anomaly sensitive artifact has been developed to detect errors during LPBF-AM processes. The artifact is produced alongside any large complex part being manufactured and will break prematurely or belatedly in the presence of anomalies in the process allowing for real time detection.  </p><p><h2>Description</h2>LPBF-AM is used to produce complex, customizable metal parts that cannot be made using traditional machining techniques. LPBF-AM is a layer-by-layer manufacturing process that can take hours, and often days to complete. This longer timeframe increases the risk of degradation of the laser performance, affecting the melting behavior of the powder bed and resulting in anomalies (e.g., defects or variations in material properties) in the final product. These anomalies are only detected after manufacturing process is complete. In high value, safety critical sectors like aerospace, medical devices, and automative parts, these anomalies result in costly wastage of materials and production time as a new part must be manufactured. Identifying anomalies during the LPBF-AM process in real time could save production companies time and money.</p><p><h2>Applications</h2>•\tQuality control in additive manufacturing \r<br>•\tResearch tool to study manufacturing process dynamics</p><p><h2>Advantages</h2>Defects in parts due to anomalies in the LPBF-AM process are often only detected during post-production testing, resulting in costly wastage. Detecting anomalies during production can allow for timely intervention resulting in lower rates of failure and improving manufacturing efficiency. Current methods to detect anomalies in real time including thermophile sensors and optical power meters are too complex and costly for widespread use. Cheaper and accessible alternatives are required. \r<br>\r<br>This novel artifact is an affordable alternative. Using cameras, and designed to break in situ during the production process in the presence of an anomaly, any breakage is easily detected in real time. The artifact can detect anomalies including excess residual stresses, material-related defects, and laser power fluctuations without the need for advanced sensors or post-process inspections. Production lines could be easily adapted to integrate this technology.</p><p><h2>Invention Readiness</h2>Novel artifacts have been designed. These artifacts undergo consistent and noticeable breakages in the presence of anomalies. Some variation in breakage time occurs depending on location and orientation of the artifact in the build chamber. Standardized placement strategies for artifacts are currently being explored to overcome this variation.</p><p><h2>IP Status</h2>Patent Pending</p><p><h2>Related Publication(s)</h2><p>Nguyen, D. S., Garner, S., &amp; To, A. C. (2025). A test artifact for rapid evaluation of material ductility variation in laser powder bed fusion. Additive Manufacturing, 109, 104878. <a target=\"_blank\" href=\"https://doi.org/10.1016/j.addma.2025.104878\">https://doi.org/10.1016/j.addma.2025.104878</a></p></p>","tags":["Engineering","Material Science"],"file_number":"06999","collections":[],"meta_description":"Real-time LPBF anomaly sensor: affordable breakable artifact signals defects, enabling immediate intervention and reduced waste.","image_url":"https://s3.us-east-1.amazonaws.com/static.tto.c8e.ai/upitt/attachments/06999/0EMVv00000RN4g5.png","apriori_judge_output":"{\"scores\":{\"novelty\":4.0,\"potential_impact\":4.0,\"readiness\":2.0,\"scalability\":3.0,\"timeliness\":3.0},\"weighted_score\":3.05,\"risks\":[\"Early-stage concept (artifact design) with limited field validation.\",\"Potential fragility and integration challenges in diverse LPBF systems.\",\"Breakage-based detection may not capture all anomaly types or could cause downtime when artifacts fail.\"],\"one_sentence_take\":\"Strong novelty and potential impact with a practical, low-cost detection concept, but readiness is low and execution risk high for broad scalability.\"}","lead_inventor_name":"Albert To","lead_inventor_dept":"Mechanical Engineering and Materials Science","technology_type":"Engineering Technology","technology_subtype":"Material Science","therapeutic_areas":[],"therapeutic_indications":[],"custom_tags":[],"all_tech_innovators":["Sina Nejati Eghteda","Dinh Son Nguyen","Albert Chi Fu To"],"date_submitted":"2024-12-06"},"highlight":{},"matched_queries":null,"score":0.0}