Engineering and Project Management
 

Category: Digital

Digital

Virtual Appliance for Engineering Simulations

The main software package in our FEA workflow is CalculiX, an open source finite element analysis application with an implicit and explicit solver, developed by Dr. Guido Dhondt of MTU Aero Engines GmbH, with support from other figures in the academic world, such as Prof. Martin Kraska, Brandenburg University of Applied Sciences.
 
We are making available a pre-built Oracle VM VirtualBox appliance, ready to run and packed full with open source applications for engineering applications. This virtual appliance has been configured to meet the diverse needs of mechanical engineering professionals, providing a suite of tools that enhance productivity, streamline workflows, and deliver reliable results. The suite includes advanced analytics and postprocessing capabilities, allowing engineers to perform complex calculations and simulations with ease. This not only saves time but also ensures accuracy, reducing the risk of errors and rework. 
The installed applications include, among others, CalculiX v2.20 (for FEA applications), OpenFOAM v9 (for CFD applications), ParaView v5.7.0, and FreeCAD v1.0, running on Ubuntu 20.04 LTS. Besides, a web-based interactive computing platform, Jupyter Notebook, is installed and configured for creating and sharing computational documents, allowing users to configure and deploy workflows in data science, scientific computing, and machine learning.
Continue reading"Virtual Appliance for Engineering Simulations"
Digital

Language Models

Humble beginnings, but encouraging enough to deploy on the network. EXO is an open-source framework which allows users to run AI models on a distributed cluster of heterogeneous devices running various operating systems. EXO splits up large AI models into smaller shards and distributes them across multiple networked devices, unifying existing infrastructure one powerful virtual GPU. While not fully featured yet, it is potentially suitable for deploying large models across multiple devices.

Digital

On Data Analysis

We are facing a new industrial revolution, where machines and sensors can connect to your IT infrastructure to provide more profound insight into your business and Key Performance Indicators (KPIs).

With the advent of this new paradigm, systems and monitoring applications are producing enormous amounts of actionable data allowing for cost optimization, prediction of future events, behavior classification, quality control, and a number of other functionalities.

 

The connection of sensors from remote locations to your local or remote IT infrastructure can be undertaken in a seamless manner through a low cost, energy efficient, and secure Internet of Things (IoT) network. Business intelligence overviews can be generated, alerts programmed and additional functionality plugged in and actioned based upon the received and analyzed data. Moreover, Machine learning (ML) models can be generated allowing for prediction on most valuable operational parameters. Find out how we can help by downloading our data analysis brochure.

Digital

Supply Chain Traceability

We are developing a supply chain tool to enable full traceability of certificates on a public blockchain, and focused on the maritime market. Currently running on Ethereum’s Rinkeby testnet, and hosted on a private network, it aims to provide a secure and reliable way to confirm the authenticity of a product certificate along its lifecycle.

Data is digitized at the time of certificate issuance, and stored on a public blockchain to ensure its inmutability and future traceability by third parties anywhere in the world, at any given time. Only authorized users (validators) are able to store the digitized data on the blockchain, whereas any party (authorized or not) can perform authentication and verification of the data at any later stage.

Material certificates can be incorporated or associated into broader product certificates so the whole supply chain can be traced at any given time during supply. As an optional feature, the authentication of a given product certificate can signal acceptance by an authorized party (ie, a purchaser in the organization) and automatically trigger a smart contract action (ie, authorizing payment or recognizing fulfillment of a contractual milestone).

 

While currently running on the Rinkeby testnet, the smart contracts will be redeployed at time of finalization so the final product will be running on the main Ethereum network, ensuring quick propagation of data as well as enhanced security.

Digital

Digital Twin

We have developed a digital twin model capable of pairing virtual and real equipment, including sensor readings.

The most significant features are as follows:

  • Fully integrated on a virtual reality (VR) environment.
  • Integration of as-built 3D models of equipment from different CAD packages, together with point cloud scan data on a seamless environment.
  • Integration of real time data from live SCADA systems or data sets enabling the presentation of real-time status and operating condition, including alarms.
  • Integration and visualization of SQL databases of equipment, including equipment and part datasheets, maintenance logs, etc.
  • Capable of connecting remotely with users, allowing for remote design reviews and/or remote collaboration thorough the life cycle of the equipment or installation.

 

Find out more through the following link, or downloading our informative brochure.

Digital

Automatic Defect Detection System

Undergoing development of an automatic defect detection system in manufactured parts, using convolutional neural networks to detect features in an image while simultaneously generating a high-quality segmentation mask for each instance.

Common defects include cavities, pores, lack of penetration, and other type of volumetric defects frequently found in castings and welded parts.

Detection of these defects can allow faulty products to be identified early in the manufacturing process, resulting in improvements in quality, as well as time and cost savings.

The defect detection system is trained and tested on publicly available X-ray dataset and is currently at an early development stage.

Welding Defects

The classification method being used is binary (defect or no-defect) so the results being obtained are only useful for a first pass assessment of the quality of the weld. It would be possible, on a further iteration, to use a multi-class defect classification allowing much greater granularity and variety in the results (gas cavity, lack of penetration, porosity, slag inclusion, undercut, lack of fusion, etc).