---
title: "Interspectral joins Vinnova-backed TRUSTAM consortium to bring federated AI to additive manufacturing quality control"
url: https://www.voxelmatters.com/interspectral-joins-vinnova-backed-trustam-consortium-to-bring-federated-ai-to-additive-manufacturing-quality-control/
date: 2026-05-11
modified: 2026-05-11
lang: en
author: "Joseph Caron-Dawe"
description: "Swedish provider of AI-driven process monitoring and quality assurance software for additive manufacturing technologies Interspectral has joined a publicly funded Swedish research consortium developing a federated artificial intelligence framework for..."
categories:
  - "AI"
  - "AM Research"
  - "AM Software"
  - "Money & Funding"
  - "Process Monitoring"
  - "Research & Education"
tags:
  - "insights"
image: https://www.voxelmatters.com/wp-content/uploads/2026/05/Interpectral-02-640x360.jpg
word_count: 350
---

# Interspectral joins Vinnova-backed TRUSTAM consortium to bring federated AI to additive manufacturing quality control

Swedish provider of [AI-driven process monitoring and quality assurance software](https://www.voxelmatters.com/interspectral-advances-smart-am-process-monitoring/) for additive manufacturing technologies [Interspectral](https://www.3dprintingbusiness.directory/company/interspectral/) has joined a publicly funded Swedish research consortium developing a federated artificial intelligence framework for quality assurance in additive manufacturing, with applications in aerospace and defense.

The project, called TRUSTAM — Trusted Federated Intelligence for Additive Manufacturing — has received multi-million Swedish krona funding from Vinnova, Sweden's national innovation agency. The consortium includes defense and aerospace group Saab, [AM services provider AMEXCI](https://www.voxelmatters.com/amexci-will-have-early-access-to-interspectrals-am-explorer-software/), and machine learning infrastructure company Scaleout Systems.

The technical core of TRUSTAM centers on federated learning, an approach in which AI models improve collectively across multiple production environments without raw process data leaving the facility where it was generated. Only model updates are exchanged between sites, allowing manufacturers to share operational intelligence while retaining full control over proprietary process data and intellectual property.

Interspectral holds the lead technical role in the project, responsible for developing the local AI model, which is the on-site component that learns from each machine's process data. It will also head up the workflow architecture connecting monitoring, analysis, and decision-making across facilities.

[![Interspectral joins Vinnova-backed TRUSTAM consortium to bring federated AI to additive manufacturing quality control](https://www.voxelmatters.com/wp-content/uploads/2026/05/Interpectral-01-640x360.jpg)](https://www.voxelmatters.com/wp-content/uploads/2026/05/Interpectral-01.jpg)

“We entered this collaboration because the challenge it addresses is one we encounter with our customers every day,” said Isabelle Hachette, Chief Executive Officer at Interspectral. “How do you scale AI-driven quality assurance across multiple production sites and different machine environments without ever compromising data security or IP ownership? That question demands a collaborative answer, and this consortium is uniquely positioned to deliver it.”

TRUSTAM's expected outputs include on-premise AI models calibrated to specific machines and production conditions, a validated framework for secure cross-site AI collaboration, and working demonstrators tested in live aerospace and defense environments. 

[Interspectral stated the project will also feed directly into planned AM Explorer platform capabilities](https://www.voxelmatters.com/interspectral-launches-integration-for-eosconnect-in-am-explorer/), including on-premise AI training and multimodal process analysis.

“Being trusted with the technical core of this project reflects the confidence our partners have in our platform and our competence,” Hachette added.

The project runs through early 2028, closing with a demonstrator phase and public dissemination of results to the broader AM community.