AI Platform Enables Predictive Metrology Asset Management

Hexagon Manufacturing Intelligence has launched APOLLO, an AI-driven platform designed to monitor and manage metrology assets such as coordinate measuring machines (CMMs) and machine tools. The system applies predictive analytics to detect anomalies and support proactive maintenance, helping manufacturers maintain measurement accuracy and production continuity.

Predictive Monitoring for Metrology Systems
APOLLO shifts asset management from reactive maintenance to predictive strategies by continuously analyzing machine behavior, environmental conditions, and operational data. The platform can identify patterns associated with potential defects or failures up to 90 days in advance.

This early detection enables maintenance teams to schedule interventions during planned downtime, reducing the risk of unexpected equipment failures and minimizing disruption to production processes.

Improving Measurement Reliability and OEE
Metrology systems play a critical role in ensuring product quality, particularly in high-precision industries. Equipment downtime or measurement drift can lead to production delays and quality deviations.

By monitoring asset performance in real time, APOLLO helps maintain consistent measurement accuracy while improving overall equipment effectiveness (OEE). The platform also identifies underperforming machines, allowing targeted optimization of maintenance schedules and resource allocation.

Integration Across Manufacturing Environments
The platform is designed for compatibility with both Hexagon and third-party equipment, enabling deployment across diverse manufacturing environments. A centralized dashboard provides fleet-wide visibility into asset health, supporting coordinated management of multiple machines.

APOLLO can be deployed in cloud-based environments for scalability or on-premises to meet data sovereignty and cybersecurity requirements. This flexibility allows manufacturers to integrate the platform into existing digital supply chain infrastructures.

Addressing Workforce and Complexity Challenges
Manufacturers are increasingly facing skilled labor shortages and growing production complexity. Traditional asset management approaches often rely on manual logs and operator experience, which can lead to inconsistent maintenance practices.

APOLLO replaces these methods with data-driven insights, standardizing monitoring and decision-making processes. This reduces dependence on undocumented knowledge and supports more consistent operational performance.

Data-Driven Maintenance Optimization
The platform uses operational and environmental data to optimize maintenance intervals based on actual usage conditions rather than fixed schedules. This approach helps reduce unnecessary servicing while ensuring timely intervention when needed.

By stabilizing machine performance and reducing measurement variability, APOLLO supports higher throughput and improved quality outcomes without increasing operational risk.

Relevance for Digital Manufacturing
The introduction of AI-driven asset monitoring reflects broader trends in digital manufacturing, where connected systems and analytics enable more efficient and resilient operations. Integrating metrology data into a digital supply chain allows manufacturers to align quality control with production planning and maintenance strategies.

As production environments become more complex, platforms such as APOLLO provide the visibility and predictive capabilities required to maintain performance, reduce downtime, and ensure consistent product quality.

Edited by Romila DSilva, Induportals Editor, with AI assistance.