Canvas Category Software : Information Technology : Asset Performance Management

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Primary Location Mississauga, Ontario, Canada

Financial Status VC-Seed; Glasswing Ventures, Argon Ventures

Basetwo is designed to solve the toughest problems in manufacturing engineering. Our mission is to empower engineers to operationalize AI that they can understand and interpret in order to improve quality, transparency, and performance. Weโ€™ve spent the last decade deploying AI in the heavy industry and manufacturing sector. Weโ€™ve been across the globe and have listened to and learned first hand from engineers, operators, managers, and scientists that influence the manufacturing process. The Basetwo platform represents everything weโ€™ve learnt about what OT and IT professionals need to operationalize AI for process control and manufacturing applications.

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Overview of the Digital Twin Lifecycle

๐Ÿ“… Date:

โœ๏ธ Author: Haider Kamal

๐Ÿ”– Topics: Digital Twin

๐Ÿข Organizations: Basetwo

Productionizing digital twins in an industrial, regulated environment is challenging. From connecting to a variety of data lakes and cleaning data to make it human or machine useable, all the way to visualization, modeling, and exporting of key model outputs to various stakeholders, there are a dozen different steps organizations need to get right to effectively benefit from digital twin technologies. In todayโ€™s age of aspirational Industry 4.0, many organizations are at various stages of their digitalization journeys. On one end, some may be working at sorting and centralizing their data onto cloud-based data lakes, while others may be further along and already have numerous sophisticated models built to represent their assets and related processes.

The core of productionizing digital twins is subject matter expertise across multiple teams to work synchronously to meet stringent engineering, regulatory, and cybersecurity requirements. From an engineering perspective, digital twins need to be explainable and grounded in the physical systemโ€™s physics, biology, and/or chemistry. From a regulatory perspective, diligent record-keeping is required for auditability (i.e., tracing when models were built, what data was used for training, how model outputs were consumed, etc). Lastly, from a cybersecurity perspective, IT departments often require significant controls on how digital twins may interface directly or indirectly with control systems and/or other mission-critical databases.

This article provides an overview of the digital twin lifecycle through a TwinOps workflow shown in the figure below. TwinOps is focused on the lifecycle of taking digital twins from design to production, and then providing the infrastructure to maintain and monitor them once operationalized.

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Introduction to Hybrid Modelling for Digital Twins

๐Ÿ“… Date:

โœ๏ธ Author: Thouheed Abdul Gaffoor

๐Ÿ”– Topics: Digital Twin, Physics-informed neural networks

๐Ÿข Organizations: Basetwo

Physics-informed Machine Learning (PIML) involves embedding established domain knowledge (i.e. physics, chemistry, biology) with machine learning (ML) to effectively model dynamic industrial systems. While these dynamic systems face challenges such as high sensor noise and sparse measurements, they often are characterized by some fundamental scientific/engineering knowledge. There are 3 general ways to embed domain knowledge with ML, including:

  • Introducing observational bias to the data
  • Introducing inductive bias into the model structure
  • Introducing learning bias to how models are trained

Physics-informed neural networks (PINNs) are a novel approach that integrate the information from both process data and engineering knowledge by embedding the ODEs into the loss function of a neural network. PIML integrates data and mathematical models seamlessly even in noisy and high- dimensional contexts.Thanks to its natural capability of blending physical models and data as well as the use of automatic differentiation, PIML is well placed to become an enabling catalyst in the emerging era of digital twins.

Read more at Basetwo Blog

Basetwo Launches AI Platform for Manufacturing, Raises USD $3.8M

๐Ÿ“… Date:

๐Ÿ”– Topics: funding event

๐Ÿข Organizations: Basetwo

Basetwo, a SaaS AI platform for manufacturers, today announced it secured a USD $3.8M seed round led by Glasswing Ventures and Argon Ventures with additional funding from Caffeinated Capital, Graphite Ventures, MaRs IAF, CEAS Investments, Pareto Holdings, Plug and Play, and Quiet Capital.

The funding supports the launch of the category-making Basetwo AI platform, a first-of-its-kind solution that enables process engineers to rapidly build digital twins of their manufacturing plants leveraging a familiar no-code interface. Basetwo promotes the digitalization of manufacturing systems to foster intelligent manufacturing across the global supply chain. Basetwo intends to be the default platform for continuous improvement in the current Good Manufacturing Process (cGMP), beginning with the pharmaceutical market and expanding to the broader process manufacturing markets.

Read more at Basetwo Blog