Treble Technologies
Assembly Line
Treble Technologies secures €11M for its cloud-based sound simulation platform
Treble Technologies, a company specialising in sound simulation technology, announced that it has raised €11M in a Series A round. The funding round was led by KOMPAS VC, with participation from Frumtak Ventures, the European Investment Bank, Omega Venture Partners, strategic partners St. Gobain & L-Acoustics, as well as experienced angel investors. The Icelandic company will use the funds to expand its team, enhance R&D efforts, forge new enterprise clients, and tap into new markets.
Powered by its proprietary simulation technology, the company’s platform allows users to perform highly accurate sound simulations for buildings, cars, and even immersive reality applications. According to the company, it is more than 100x faster than existing sound simulation solutions, enabling a wide range of industries to bring down costs.
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.