WALTHAM, MA, USA, May 5, 2026 — Engineering and manufacturing organizations continue to face challenges when managing large repositories of 3D models, including manual shape comparison, inconsistent similarity assessments, and the time‑consuming navigation of extensive part libraries. Traditional CAD search and manual similarity methods rely heavily on metadata, naming conventions, or subjective human judgment—approaches that do not scale and often produce inconsistent results.
AMC Bridge’s latest technology demonstration, Similar Parts Search, explores how combining artificial intelligence (AI) with advanced 3D geometric processing can address these challenges and significantly improve the discovery, reuse, and evaluation of structurally similar parts across engineering workflows. By leveraging learned geometric representations of 3D models, the POC enables consistent, geometry‑aware similarity detection across large repositories, independent of how parts are named or categorized.
The Similar Parts Search technology demonstration combines Graph Neural Networks (GNNs) with advanced 3D geometric processing to enable more accurate and meaningful similarity detection within large collections of engineering models. This approach allows the system to learn and leverage rich geometric characteristics of 3D parts, going beyond simplistic or manual shape comparisons.
Shifting similarity detection from subjective judgment and metadata‑based rules to learned, geometry‑driven representations, the demonstration reflects a broader industry move toward scalable, data‑driven decision‑making in engineering and manufacturing.
By automatically preparing uploaded 3D models for analysis and generating persistent digital profiles for each part, the system supports scalable, reusable similarity searches across datasets. This foundation enables the technology to be extended into custom, production‑ready solutions that can integrate with existing CAD, PLM, ERP, or supply‑chain systems, helping organizations unlock additional value from their engineering data.
The demonstration leverages domain‑specific datasets, established expertise in geometric algorithms, and high‑quality engineering model collections, including the Mechanical Components Benchmark (MCB)—an open‑source dataset developed by Purdue University and distributed under the MIT License—showcasing how AI‑driven geometric intelligence can enhance part discovery and comparison processes across heterogeneous model libraries.
The POC highlights how AI‑based geometric similarity search can deliver tangible operational and economic benefits across engineering, manufacturing, and supply‑chain domains. By enabling faster identification of previously manufactured or equivalent parts, Similar Parts Search supports quicker manufacturing cost and lead‑time estimation while promoting the reuse of validated process knowledge.
At the same time, the prototype demonstrates the potential to reduce part proliferation and stock variation by minimizing near‑duplicate components across product lines. Improved similarity detection also contributes to lower warehouse and inventory holding costs through consolidation and reuse strategies, simplifies supply and procurement management via greater standardization, and enhances design reuse—helping engineering teams shorten development cycles and reduce the risk of redundant part creation.
By pairing AI‑driven similarity metrics with intuitive visual validation, the demonstration supports human‑in‑the‑loop decision‑making, enabling engineers and decision‑makers to confidently evaluate why parts are considered similar and how they compare in real‑world usage.
To see the current functionality of the Similar Parts Search technology demonstration, watch a short demo video.
If you are interested in learning more about the demonstrated technologies and how they can be utilized for your organization’s needs, please contact us to discuss the details.
About AMC Bridge
AMC Bridge is a trusted software technology partner for engineering, manufacturing, and construction enterprises, whether they are actively pursuing AI-driven digital transformation or only beginning to recognize its potential. We help organizations to move beyond experimentation and achieve consistent ROI by delivering production-ready software and end-to-end solutions for their transformation journey.
We design, build, and integrate enterprise-grade software – applications, workflow extensions for CAD/PLM/BIM, data integrations, AI-enabled features – and deploy them with monitoring and lifecycle management so they remain reliable over time. Our services include assessing data readiness; preparing and unifying product and project data; and embedding AI into the workflows teams use every day. With 25+ years of industrial software expertise and deep ecosystem partnerships, including Aras, Autodesk, Bentley, Dassault Systemes, PTC, Siemens, Tech Soft 3D, and others, we empower enterprises to move confidently from experimentation to operational AI at scale. For more information, visit amcbridge.com.
