The Explainable AI (XAI) in Manufacturing (XMANAI) project aims to provide a framework for the development and deployment of AI models in the manufacturing industry.
In the new era of Industry 4.0, AI systems are becoming increasingly prevalent and cost-effective. With the ability to analyze vast amounts of data, factories can reduce expenses, boost productivity, and minimize waste.
Name: Linda Napoletano Job title: Head of the Manufacturing Area Organization: Deep Blue srl Bio: Linda Napoletano holds a Ph.D. in Human-Computer Interaction. Since 2002, she has been working on EU-co-funded projects aiming at designing and validating humans integration and interactions into highly innovative processes.
Technical and Socio-Business assessments of AI Maturity in pilots of Explainable AI The EU-funded XMANAI project focuses on Explainable AI, as the
Name: Aitor San Vicente Job title: General Manager Organization: UNIMETRIK Bio: Expert in advanced industrial metrology services, Aitor has extensive experience both in the field of advanced manufacturing processes, mainly in the die-cutting and stamping sector, and in the development of quality control and digitalization solutions and strategies.
In XMANAI we have set out to develop robust and insightful AI pipelines that can assist manufacturers in their everyday operations and decision-making processes. To achieve our goal, we are creating a collaborative environment in which the explainability of the ML models’ decisions lies at the heart of our AI pipelines design, development and roll out. Needless to say, in order for these AI pipelines to be properly configured, trained, evaluated, deployed and applied, constantly monitored, assessed and refined as needed, numerous other processes need to be in place.
- Ethics considerations for manufacturing XAI: the XMANAI Ethical Evaluation Framework
- Partner Spotlight – Ford Motor Company
- XAI Model Guard: The XMANAI AI Models Security Framework
- Explainable AI: a key to trust and acceptance of AI-based decision support systems
- Partner Spotlight – CNH Industrial
- Industrial Asset Graph Modelling in XMANAI
- XMANAI Validation Environment for AI Models
- zExplAIn, Improving Manufacturing Processes with Explainable AI
- XMANAI partner spotlight – Deep Blue
- Technical and Socio-Business assessments of AI Maturity in pilots of Explainable AI
- XMANAI partner spotlight – UNIMETRIK
- AI Algorithms Lifecycle Management and Collaboration
- XMANAI partner spotlight – Knowledgebiz
- XMANAI partner spotlight – UBITECH
- A First Glimpse into the XMANAI platform
- XMANAI partner spotlight – Whirlpool
- Overview requirements
- XMANAI partner spotlight – Athena Research Center
- Industrial Assets Provenance in XMANAI
- XMANAI partner spotlight – Innovalia Association
- The Draft Catalogue of XMANAI XAI Models
- XMANAI´s Evaluation Framework
- XMANAI partner spotlight – Suite5
- XMANAI Reference Architecture: Perspectives
- Security Aspects of Industrial Data Management
- XMANAI partner spotlight – AiDEAS
- What is the XMANAI Minimum Viable Product (MVP)?
- XAI in Manufacturing
- Data Asset Management in XMANAI
- XMANAI partner spotlight – Tyris AI
- XMANAI partner spotlight – Politecnico di Milano
- Education role in AI technology implementation in industry
- XMANAI partner spotlight – Fraunhofer FOKUS
- AI Requirements for Manufacturing in the XMANAI Project
- Have you ever heard about Graphical Neural Networks?
- A brief overview of XAI Landscape
- XMANAI partner spotlight – TXT e-solutions
- Outlook on the XMANAI industrial demonstration cases
- Why we should make use of AI in the manufacturing industry?
- Human Aspects in Decision making and AI
- Moving from “black box” to “glass box” Artificial Intelligence in Manufacturing, with XMANAI