State-of-the-art production

The company wanted a state-of-the-art production line, with minimum wastage and resource requirements. During manufacturing, multiple floor tiles are produced on a 2 x 2.4 meter hardened wood composite core, with a thinly applied veneer layer, before being cut to size. Heat and high pressure can cause blistering, bending, and other damage to multiple or single tiles during the production process.

Manual inspection of the boards is challenging due to the size and speed that the boards are produced. It’s virtually impossible for a person to consistently identify small but crucial defects with just a few seconds to inspect them, especially as a defect can be smaller than one square centimeter and protrude less than 0.5 millimeters from the wooden surface. To consistently identify such defects, an effective machine vision system is required to rapidly and accurately inspect the boards.

Inspecting the details with 3D scanning

Since the flooring has extremely low tolerances, the choice of hardware for data collection was critical. Minute height differences – as small as 0.03 millimeters on the veneer edges – had to be recorded and analyzed. The boards also had to be inspected while traveling on a conveyor belt. Therefore, a 3D scanning solution was deemed most appropriate.

A new vision solution

A new vision solution was developed to collect, analyze and store the data generated by the 3D board scanner. This provides a range of benefits. For instance, each board is marked with a unique identifier, to which the stored data is coupled. With this data, single tiles within a core panel can be automatically rejected if they have a defect, at a later stage in production. Previously, the whole panel was rejected, now, single tiles are discarded once the panel is cut into tiles. Additionally, machine operators are informed of any defects and can make an instant decision on how to proceed.

All measurement data is stored in a central database and is available for post analysis. Together with other process data, it can be used to detect and resolve systematic problems in production.

A collaborative partnership

What began as a vision system project, has now evolved into a long-term partnership. New measurements and features have been added and the solution has been fully integrated into the production process. Collaborative agile workflows enable the scanner to be rapidly adapted for parallel projects while small on-the-fly improvements can be quickly implemented.

A future-proof vision system

The new vision system provides objective inspection of products, with high traceability in the form of quantification of the measurements stored in an ordered and accessible format. Automated sorting of defect tiles means wastage is minimized. Furthermore, the need for manual inspection has been greatly reduced, freeing up resources in manufacturing. Finally, the ability to identify issues in production, provides an innovative tool for identifying production improvements and alerting staff about potential upcoming service requirements.

We provided R&D as a Service solution to Easymeet to develop.their meeting solutions for a secure, cloud based method of holding congresses, and union and shareholder meetings – where voting is critical.    

Taking on-site services to the cloud

Easymeet’s original business model for congresses was based on on-site delivery. Deploying anything from high-quality wifi to tablets, servers and a high-class digital support system onsite, Easymeet acts as a one stop shop for formal meetings. However, the demand for availability online required new technical solutions and business models. 

Securing live cast sensitive data remotely

Easymeet Online is delivered as Software as a Service, enabling customers to manage their digital meetings online by themselves. The solution supports managing the meeting agenda, attendance control, voting, document management, and video conferencing. Easymeet Online leverages cloud technology for flexibility and availability. 

R&D as a Service

We worked closely together with Easymeet to define requirements and the general architecture of Easymeet Online. The close collaboration continued throughout the development phase where Data Ductus R&D-as-a-Service was utilized.

Our experts on Cisco Webex video conferencing integrated it into the solution. We also assisted Easymeet with deployment to the cloud and maintenance of the solution. “This is a good example of when our 360° perspective of software development – in the form of R&D as a Service is utilized.“ says Carl Grönbladh, site manager at Data Ductus. 

Remote meetings made e

Easymeet’s customers can now create and manage their meetings without assistance. Functionality that was previously only available on-site during larger conferences can now be used for all meetings. Having the complete solution for video conferencing and meeting support functionality gives the customer a new way to arrange meetings. Conducting meetings remote can also save time and effort, as well as lower environmental impact. 

More information about Easymeet Online is available at the following link: https://www.easymeet.se/easymeet-online/ 

Analysis of iron ore phases and microstructures used to be done manually at the world’s largest iron ore producers. We collaborated with LKAB to automate analysis.

Machine learning: Automating iron pellet analysis for the LKAB mining and processing company   

LKAB’s core business is the mining and processing of iron ore for the steel industry. Over the years, LKAB has developed a unique market offering for its customers, namely blast furnace pellets. These have now become LKAB’s most vital product – with an annual production capacity at its plants in northern Sweden, of 28 million tons per annum. The main benefit of using pellets over standard iron ore products at steel mills, is lower furnace energy requirements. An addition benefit is that the pellets contain extra minerals, such as olivine, which provide improved high-temperature properties.  

Pellet inspection  

The R&D team at LKAB are continuously searching for improved ways to produce more effective as well as customized furnace pellets. Part of this process includes manually inspecting the formation of different iron oxide phases and microstructures in sample pellets. This is a time-consuming process that must be done by experts, who examine sample pellets embedded in epoxy, through an optical microscope. Some minerals can be identified by color and intensity, such as magnetite and hematite, while others are based on pellet surface texture. This process lends itself perfectly to machine learning.  

Automating inspection 

Our goal was to automate this process. The first step was to create a dataset of high-quality microscopy images of representative iron ore pellets. Experts at LKAB provided us with sample pellets embedded in epoxy and access to automated microscopes – each equipped with high-resolution digital cameras – at their facility. 

Creating an image library 

High-magnification images of the sample pellets were acquired, and multiple images were pieced together into large mosaic images to create a complete view of each pellet. Depending on the size of the pellet, the images ranged between 500 and 900 mega pixels.   

Training a pixel classifier 

Once the image library was created, the images were annotated in close collaboration with analysis experts at LKAB. Regions from each class were marked in a set of images and used to train the pixel Classifier. The Classifier was subsequently used to classify the images and the annotation was improved by correcting the classification. This was repeated until a satisfactory classification was achieved. The Classifier was then evaluated on a set of images that were not included in the training process. 

Intuitive pellet analysis 

The last step was to extract and quantify relevant information from the classified images and create a visual format that could be easily interpreted by all members of the pellet analysis team at LKAB. The result was a series of graphs depicting a mineral map, the microstructure and the mineral content of a pellet. 

Thanks to the automated mapping of sample pellets, experts can now dedicate their time to analyzing the results, rather than inspecting the pellets. This has speeded up the analysis process considerably. Furthermore, analysis is no longer based on one person’s viewpoint, it is now standardized across all pellet samples – based on the collective experience and advise of the R&D team.  

Optimizing pellet properties 

The automated and quantitative characterization of microstructures can be used to generate the necessary data to better understand the effects of pellet additives and the different process parameters. Additionally, it is now possible to automate a quantitative study of the reaction mechanisms in the blast furnace based on pellet microstructures. With this knowledge, pellet properties can be optimized for different customer applications.