End User
LMS
Description
With the help of this AI services bundle (X = a specific manufacturing process*), the behaviour of a manufacturing process can be modelled. As such, both the operation and the design of a manufacturing line can be facilitated, as optimization scenarios, as well what-if scenarios, can be considered. The input and the output comprises the profiles of the engaged variables in time. Sustainability can thus be optimized either by Energy efficiency directly, or through considering defects reduction workflows.
Core Capabilities
Optimization & Decision Support
Business Need
In modern manufacturing, especially in precision-dependent processes like welding, companies face increasing pressure to improve process efficiency, product quality, and sustainability. However, traditional approaches offer limited visibility into real-time process performance, making it difficult to prevent defects, reduce energy waste, or optimize overall system behavior. A Digital Twin of a manufacturing process (welding, Additive Manufacturing, and others) solves these challenges by providing a real-time, virtual representation of the physical process — enabling live monitoring, simulation, and predictive optimization.
This service addresses critical problems such as:
Energy inefficiencies due to non-optimized process parameters or poor equipment condition.
Defect rates that lead to material waste, rework, and additional energy/resource consumption.
Limited insights into how process adjustments impact both performance and sustainability.
By integrating real-time data and analytics, the Digital Twin empowers manufacturers to:
Optimize energy use directly through better control and process tuning.
Reduce defects and rework, indirectly saving energy and materials.
Sustainability is therefore enhanced not only by direct energy efficiency, but also through intelligent defect prevention and leaner workflows — both of which are made possible through the insights offered by the Digital Twin.
This service addresses critical problems such as:
Energy inefficiencies due to non-optimized process parameters or poor equipment condition.
Defect rates that lead to material waste, rework, and additional energy/resource consumption.
Limited insights into how process adjustments impact both performance and sustainability.
By integrating real-time data and analytics, the Digital Twin empowers manufacturers to:
Optimize energy use directly through better control and process tuning.
Reduce defects and rework, indirectly saving energy and materials.
Sustainability is therefore enhanced not only by direct energy efficiency, but also through intelligent defect prevention and leaner workflows — both of which are made possible through the insights offered by the Digital Twin.
Key Performance Indicators
Response time for real-time adjustments
Accuracy in prediction
Reduction in process-level KPIs through simple optimization scenarios
Applicability & Adaptation
Data Provided
Proprietary datasets from process characteristics (API, CSV, JSON, depending on the case) (unknown datasize) (existing documentation for structure) (data resolution per hour)
Historical for process variables (process parameters and performance indicators) (API, CSV, JSON, or streaming depending on the case) (unknown datasize, but rather large) (unknown structure) (data resolution potentially per millisecond)
Real-time data could be considered towards adaptation and active learning
TEF
TEF IND