Siemens has introduced significant artificial intelligence (AI) enhancements to its Simcenter Testlab software, revolutionizing the efficiency and quality of physical modal testing and analysis processes for engineering teams. The new version of Simcenter Testlab brings AI-assisted automation across multiple physical testing workflows, enabling engineers to execute tests with greater speed, improved data consistency, and earlier in the product lifecycle.
The updates focus on modal analysis, a crucial process for understanding the vibration characteristics of mechanical structures. The AI-assisted capabilities automate complex tasks such as mode selection and validation, making the modal analysis process up to seven times faster and allowing one operator to perform tasks that once required a team. The software also introduces intelligent sensor placement and automated hit selection, automating the traditionally manual task of impact data verification and enabling engineers to concentrate on analysis and interpretation.
The update also introduces new automation features for transfer path analysis (TPA), a method used to predict noise, vibration, and harshness (NVH) transmission in product assemblies. The automated data preparation sequence cuts the end-to-end TPA process time by up to 40%, simplifying the workflow and making advanced vibration analysis more accessible across engineering teams.
Other notable updates include a first-to-market solution for automated component model extraction that complies with ISO 20270 standards, enabling engineers to perform automated measurement and calculation of blocked forces on component test benches. The release also features updates to test data management and validation tools, including seamless integration with Simcenter SCADAS RS hardware and support for data export in universal or third-party formats.
According to Jean-Claude Ercolanelli, Senior Vice President at Siemens Digital Industries Software, the updates illustrate the company’s approach to integrating AI to transform how teams conduct, manage, and interpret physical testing. The changes have the potential to significantly impact engineering practices, from design and development to physical testing, and make advanced vibration analysis more accessible across engineering teams. The increasing role of AI-driven automation in engineering test environments is expected to bring about potential changes in how teams approach physical testing, analysis, and data management across industries.