Linköping University
Assembly Line
N-ink wins €1M from Voima Ventures Science Challenge for its IoT-transforming conductive polymers
Swedish deep technology company N-ink was announced as the winner of a science challenge by Voima Ventures, an early-stage investor based in Helsinki and Stockholm. N-ink was founded in 2020 by scientists at the Laboratory of Organic Electronics, Linköping University in Sweden.
The company provides high-performing, scalable conductive polymers that boost battery and solar cell performance and are useful in Printed Electronics, IoT and Bioelectronics. N-Ink addresses this challenge by formulating and supplying n-type inks with unprecedented performance. Its patented n-Inks are highly conductive, easy to handle, stable, printable, and on par with commercial p-type inks.
🚙 Application of optimized convolutional neural network to fixture layout in automotive parts
Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (≈ 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.