Q-DAS eMMA 3.4.0.
Automated, quick and accurate
This version of eMMA has released with some significant innovations in terms of automatic prediction (AI) functionality. Customers get the ability to learn more about processes in terms of stability and potential tolerance deviation. With an automatically generated forecast they are able to act before real problems will occur.
New AI integration with automated time-series analysis and forecasting (TSAF)
This prediction of feature and characteristics behaviour is possible thanks to the integration of TSAF (Time Series Analysis and Forecasting) artificial intelligence technology specially tailored for the analysis of manufacturing data. The system learns from the previous measurements and gives an accurate prediction of the future trend of measurement results: automated, quick and accurate.
The TSAF technology embedded within Q-DAS eMMA 3.4.0. identifies and analyses outliers, change points, and data gaps, etc. to compute an accurate forecast. Users can then plan and adjust their processes rather than react. As a result, process instabilities can be controlled and prevented in advance, resulting in a reduction in rework and scrap parts, which will lower costs.
Usability and Visualization Enhancements
Furthermore, Q-DAS eMMA core modules have been enhanced with additional functionality that enables more focused reporting and analysis for feature-based and mesh-based measurements. The latest update of the Q-DAS eMMA GUI offers a more intuitive operations and quick access to key functions, allowing users to remain focused on actual decision-making.
More control for the evaluation of meshes
- Easy and quick recalculation of surface deviations including the display of actual geometries. No more measurement reload.
- New parameters for fine configuration of mesh import. Plausibility control, validation of normal direction, and calculation accuracy allow users to tune import parameters according to their needs.
More customization options for meshes
- Custom definition of colour maps for the classification of mesh-based results (optical measurements/simulations) to create a visual representation tailored to specific data sets and accelerate decision-making.
More diagrams for detailed measurement analysis
The new “Trend chart detail” label in the Q-DAS eMMA analysis modules can display up to 30 measurement deviations. This shows the overall trend for each characteristic per dimension in a clear graphical way.
HIGHLIGHTS
- Identify problems before they occur
- Take correction actions in advance
- Ensure process capability (cp) goals are achieved
- Achieve better process KPIs
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Extensions
The following functionalities are a perfect extension to Q-DAS eMMA:
Q-DAS eMMA Analyst is a flexible and versatile module for the analysis of measurement results. Using statistical key performance indicators (KPIs), it provides insights that enables you to have better control over prototyping, launch, ramp-up and production processes.
Quality assurance at the production line requires the detection of tolerance deviations in real time and the identification of the cause of such deviations.
Q-DAS eMMA Planner is a module created to facilitate the management of inspection plans for both single parts and assembly structures.
Generating quality reports for thousands of parts can become a time consuming and cumbersome task. Although report templates have been adopted as a technique to reuse part of the design work, the creation of such templates still requires a great deal of time as well as experienced users in the use of template generation tools.
Q-DAS eMMA Inspector is a module specially designed to support the easy and fast analysis of large sets of optical measurement results. The rich 3D native environment enables users to smoothly explore and interact with the data as they identify and compare regions of interest. Likewise other eMMA modules, eMMA Inspector also supports both PDF documentation and interactive on-the-fly 3D analysis.
Q-DAS eMMA Assembler is a module for graphical analysis of virtual assemblies in based on eMMA Analyst. It combines the powerful analytic capabilities of the eMMA Analyst to be applied simultaneously to multiple component parts and virtual linked-features.