Generative AI for Customized Design with Stora Enso
Stora Enso, a leader in renewable packaging, is looking for Generative AI solutions to streamline the process of creating new designs and optimizing them for production, transport, and specific use cases. Their vision is to provide sustainable, affordable, and customized 3D-printed designs for their clients with shorter lead times.
Today the process of creating 3D-printed customized designs is lengthy and takes a lot of trial and error, because the technology and material are still embryonic. A lot of prototyping is needed which leads to long lead times. Stora Enso aspires to shorten the design process and provide sustainable and affordable customized 3D-printed designs for their clients with the use of Generative AI.
Stora Enso envisions utilizing Generative AI to create packaging designs for unique and complicated products as well as designing furniture products with optimized design. Additionally, Stora Enso is interested in utilizing Generative AI for designing layouts for pre-defined spaces that will be filled with 3D-printed elements such as furniture. If you have deep knowledge of Generative AI in design or understanding 3D printing in manufacturing and engineering, Stora Enso is highly interested in hearing from you!
Your opportunity with Stora Enso
You get the opportunity to work with concrete industrial use cases for generative design and optimization that can be expanded in many ways. This can be a great entry point to a long collaboration in several different areas of Stora Enso businesses and innovation. We are looking to collaborate and share our industry & commercial knowledge enabling your company further opportunities within the industry.
Examples of solutions we’re interested in
Generative AI for customized packaging design
Using generative AI to create a packaging design for unique and complicated products. Input is the model of the product and output is design of the package.
Generative AI for space filling design
Designing layout for a pre-defined space that will be filled with 3D-printed elements such as furniture. Input is a model of the space and the output is a model of the filled space with 3D-printable objects and structures.
Generative AI for optimized designs
Designing furniture products with optimized design based on various parameters. Input is purpose, use factors, transport factors and production factors. Output is a range of feasible designs.
Submit your solution
Take your first step in signing your new partnership. Get in touch by October 8th!
Machine Learning for Aerodynamics with ScaniaScania wants solutions to speed up prediction and optimization of vehicle aerodynamic drag
Validating Energy Efficiency in Bearing Arrangements with SKFSKF is now looking for innovative solutions to measure energy consumption in bearing arrangements
High-frequency Edge Data Acquisition for Machine Learning with SKF
SKF is now looking for innovative solutions to support gathering data from machinery and tools
Bearing Unit with Wireless Power Transfer with SKFSKF seeks solutions to address the drawbacks associated with transmitting energy to motors of electric vehicles