Engineering and Project Management
 
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).

Uncategorized

Visit to Glasgow

We are priviledged to be visiting Glasgow this week following up on a project to retrofit an exhaust gas cleaning system onboard a SuezMax class oil tanker.

Glenlee tall ship (Galatea)
Glenlee tall ship (Galatea)
Finnieston Crane
Finnieston Crane

We thank our client and friends for their continuous consideration and support during our stay.