Linköping University
Canvas Category Consultancy : Research : Academic
Assembly Line
Remanufacturing Initiation for Original Equipment Manufacturers
Remanufacturing is an industrial process in which a core – a used, discarded, or broken product – is transformed into a product with a like-new specification and condition. However, to this date, remanufacturing activities on the market are few compared to manufacturing. There are several types of remanufacturers; the least common type is the original equipment remanufacturer, an original equipment manufacturer that not only manufactures new products but also remanufactures cores of its own products. Remanufacturing is potentially becoming a more widely used industrial process for original equipment manufacturers, and increased remanufacturing activities can positively contribute to the environment. The contribution comes from a reduction of raw material and energy consumption compared to manufacturing. Therefore, remanufacturing has the potential to decouple environmental impact from economic growth, thus contributing to more sustainable societies. However, assessing the benefits of remanufacturing does not directly correlate to growth within the remanufacturing industry. To encapsulate the environmental, social, and economic benefits of remanufacturing, manufacturers need to be aware of how remanufacturing can be initiated and implemented in practice. Therefore, the objective of this dissertation is to develop support measures for original equipment manufacturers to initiate profitable remanufacturing.
This research takes a stand in case study and transdisciplinary research where the initiation of profitable remanufacturing is studied at two original equipment manufacturers. The research study developed knowledge of how remanufacturing could be incorporated into existing operations at original equipment manufacturers. In parallel, financial assessments based on cost-benefit analysis were built to measure how well the case companies could perform remanufacturing. For the case study research, seven remanufacturing scenarios were developed, ranging from centralised remanufacturing performed by the original equipment manufacturer to decentralised performed at multiple locations using a retail network. Which scenario is preferable depends on, for example, risk-consciousness, cooperation between actors, and volume targets. However, given ideal circumstances, remanufacturing in-house in a centralised scenario was shown to be the most beneficial for the investigated original equipment manufacturer since the fewer middle hands and economies of scale also potentially enable lower costs.
Next-generation sustainable electronics are doped with air
Semiconductors are the foundation of all modern electronics. Now, researchers at Linköping University have developed a new method where organic semiconductors can become more conductive with the help of air as a dopant. The study, published in the journal Nature, is a significant step towards future cheap and sustainable organic semiconductors.
Semiconductors based on conductive plastics instead of silicon have many potential applications. Among other things, organic semiconductors can be used in digital displays, solar cells, LEDs, sensors, implants, and for energy storage. To enhance conductivity and modify semiconductor properties, so-called dopants are typically introduced.
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.