Treble Technologies
Assembly Line
Treble Technologies secures €11M for its cloud-based sound simulation platform
Treble, an Icelandic sound simulation technology startup, has landed €11 million in Series A funding. This round follows the €8 million investment the company secured back in 2022. The round was led by KOMPAS VC which recently backed Optiml and Again. It also saw participation from Icelandic VC Frumtak Ventures, which recently invested in Sidekick Health and PLAIO, the European Investment Council (EIC), Omega, strategic partners St. Gobain & L-Acoustics, as well as experienced angel investors.
Designing sound for buildings, cars, and tech products has traditionally been an expensive undertaking because of a lack of easy-to-use and accurate simulation solutions that can be integrated into the design process. When buildings or products fail to meet the necessary acoustic standards, adjustments can be time-consuming and costly.
Data-Driven Design: Leveraging Synthetic Data for Engineering Simulations
A key feature in this recent chapter of the digitization of design is that synthetic data and digital twins have dramatically improved collaboration and communication among stakeholders involved in the product design process. Virtual replicas are far easier to share and visualize than their physical counterparts, and the results of these twins being used alongside synthetic data are far-reaching.
By harnessing the power of synthetic data and digital twins, developers gain deeper insights into product performance. The aviation industry demonstrates a perfect example of this. As a result of using digital twin technologies, Boeing recently saw a 40% improvement in first-time quality of its systems and parts.
Creating comprehensive digital twins that capture the complexity of physical systems may require significant computational resources and integration with IoT devices. At Treble Technologies, acoustic engineers achieve this through benchmark testing. Having successfully simulated a device’s performance in one complex real-life room, the same benchmarks such as geometry detail or boundary conditions can then be used to simulate other hypothetical rooms of similar complexity. To evaluate the authenticity of synthetic data, benchmark datasets comprising real-world data can be created.