For this 8th loop of selection (first loop without Innosuisse support) we have 8 ideas that have been submitted on our platform.
Last week you already discovered part of them. Find more over these ideas here and which partners they’re looking for to win this selection and then make the feasibility study. Deadline to participate to the ideas and make the difference : 26.11.2024.
Feedback loop control for L-PBF and EHLA copper coatings on titanium
by Enrico Tosoratti (Head of the Metal Additive Manufacturing Team at inspire AG)
This project aims to improve the quality of copper coatings on titanium components applications by developing a real-time, automated in situ monitoring system. Using advanced sensors and AI-driven analysis, the system will optimize process parameters, detect and predict defects, and enhance material properties during the additive manufacturing process.
Target Partners:
- Industrial partners interested in coating technologies
- Industrial partners interested in defect detection and quality assurance in AM processes
- Industrial partners interested in developing and integrating real-time monitoring systems for manufacturing.

Tool-Workpiece Contact Detection for Milling Application
by Raoul Roth (Forschungszentrum at RhySearch)
The objective of this project is to automate tool-workpiece contact detection in high-precision milling operations, innovatively using measurement technologies such as spindle power, acoustic emission, and force measurement. Unlike traditional methods that rely on dimensional measurement, this approach enables real-time contact detection directly during milling, reducing machine downtime and enhancing precision for micrometric tolerances, especially in demanding micro-milling applications. By integrating these advanced technologies, the project introduces a novel solution for continuously measuring the relative tool and workpiece position, allowing immediate detection and correction of issues like tool wear or deflection, which is critical for maintaining high-quality standards in complex machining tasks.
Target Partners:
- Machine tool manufacturers and manufacturers of control system
- Users of milling machine tools from the watch and jewellery industry, optics, mould and tool making, micro-fluidics or mechanical engineering. For the latter, tolerances <5 um are the connecting element.

Use Cases and Cross-Company Benefits for Large Language Models
by Lukas Weiss (Group Leader Machine Concepts at inspire AG)
This project aims to explore and maximize the benefits of Large Language Models (LLMs), such as ChatGPT, for the manufacturing sector. LLMs can transform manufacturing operations by enhancing customer interaction, supporting machine operators, optimizing processes, and preserving institutional knowledge. Unlike traditional systems, LLMs offer multi-language support, are cost-effective, and can easily integrate with existing company data sources, from user manuals to R&D documentation. However, the challenge remains to adapt LLMs to manufacturing-specific needs and ensure data quality, system integration, and customized terminology understanding.
This project aims to unlock the potential of Large Language Models (LLMs) for manufacturing by developing practical use cases that improve customer interactions, support machine operators, and optimize processes. Through collaborative exploration and testing, the project will create tailored solutions that streamline operations, enhance efficiency, and capture valuable knowledge across companies.
Target Partners:
- Manufacturers, machinery suppliers, suppliers of these branches
- Suppliers of software solutions storing relevant data, or offering commercial solutions of LLMs and comparable software tools.

Bionanoparticle Coating for Surface Protection
by Sylvain Le Coultre (Professor at Haute Ecole spécialisée bernoise (BFH))
This project focuses on developing an eco-friendly superhydrophobic coating designed to protect surfaces by making them self-cleaning, corrosion-resistant, and anti-icing without using harmful fluorinated compounds like PFAS. Led by the ALPS Institute at Bern University of Applied Sciences (BFH), the coating is derived from renewable resources such as wood, and can be applied via simple spray or impregnation, making it scalable and cost-effective. Initial tests show promising results, offering a sustainable, durable solution for various industrial applications. The primary objective is to refine a high-performance, non-fluorinated hydrophobic coating made from wood derivatives that aligns with strict environmental regulations and meets industry needs for durability and effectiveness.
Target Partners:
- End-user companies in sectors such as construction and wood processing, as well as any industries needing surface protection from moisture and environmental exposure, are invited to collaborate.
- Companies interested in eco-friendly, non-toxic alternatives for surface treatment, with an emphasis on applications that meet modern environmental standards.
- The project seeks collaboration with industry partners eager to explore sustainable surface protection solutions and integrate environmentally-friendly practices.

Confidential project
by Timo Schudeleit (Group Lead Laser Material Processing at inspire AG)
Confidential, details on the platform.
Target Partners:
- Laser system manufacturers
- Industrial users in precision manufacturing
- Machine vision and sensor technology providers

Automated Surface Inspection for Watches
by Huseyn Gasimov, Intelec Artificial Intelligence GmbH
The primary objective of this proof of concept project is to develop a prototype system for inspecting the glass covering of watches for scratches and imperfections. Key goals include:
- Designing a prototype platform capable of tilting in two directions to enable capturing images of watch surfaces at various angles.
- Integrating a high-resolution camera and light source onto the platform
- Developing defect detection algorithms to identify scratches and digs on watch surfaces
- Conducting tests to assess the accuracy and reliability of the system
Target Partners:
We’re looking for :
- an advisor from academia who can advise the project team
- watch manufacturers, who would like to give advice from the industry perspective and test our results in their production.

Digital Knowledge base of visual defects to empower operators with autonomy
by Alban ROMANO (Cofounder at We Are Team Sàrl)
The project aims to develop a digital knowledge base for visual defect management—“Defauthèque”—to empower operators with autonomy. This tool will allow operators to report visual defects in real-time, access standardized acceptance criteria, and receive immediate feedback, effectively preserving and sharing quality control expertise within industries such as watchmaking, automotive, medical, and aerospace.
The proof of concept has to validate daily usability and estimate the benefits ( Decrease of Wait, Move, Disturbance, Limitate training Time… )
Target Partners:
We need multidiciplinary managers and operators of industrial companies : Minimum Quality, Quality Control & Production. Idealy we would have industrial & project engineer/technician working on NPI = New Product Introduction.

AI Meets No-Code: Simplifying Robotics for Smarter Manufacturing
by Michael Jakob, Haute école spécialisée bernoise (BFH)
This project aims to simplify robotic automation for flexible manufacturing by enhancing a no-code robotic interface with an AI assistant. This assistant will guide technicians, streamline complex programming tasks, and reduce setup time, allowing operators to program a wide range of tasks more efficiently, from assembly and palletizing to quality inspection.
Target Partners:
- Manufacturers: To help define use cases, system requirements, and validate the solution’s practicality.
- Technology providers: Machine manufacturers and integrators to support system design, integration, and testing.
- AI experts: To collaborate on developing the AI architecture for the assistant, ensuring intuitive guidance and error minimization.
