AMMONOID: Advanced Methods for Manufacturing using Ontology and Numeric Objects for Iterative Design
Capture, validate, and transform manufacturing data into predictive models to build best practices faster.
The Challenge
In manufacturing, precise data handling is critical to refining processes, improving product quality, and driving innovation. Yet, traditional methods, such as manually recording data in notebooks, make data retrieval slow and error-prone. Experimental setups, material details, and results risk being siloed in outdated formats, leaving manufacturers without the agility to analyze or reuse data effectively for future innovations. This lack of organized, shareable data often limits the scalability of predictive modeling and experimentation, creating bottlenecks in the development of best practices.
How It Works
AMMONOID streamlines data management for experimental manufacturing by providing an advanced platform that stores, categorizes, and validates data, making it available for machine learning and predictive modeling. When researchers upload data to AMMONOID’s model library, it’s instantly accessible to a central modeling engine that powers iterative design across projects. Here’s how AMMONOID improves manufacturing data management:
- Data Storage and Security: Experimental setups, sample performances, and results are securely stored in a version-controlled database. This approach prevents data loss and provides stakeholders with easy data recovery, while enabling controlled sharing of data with customizable access levels.
- Organized Ontology-Based Data Structuring: Using the AMBER ontology, AMMONOID categorizes data under strict entity relationships for manufacturing and project management. This structured approach ensures that data is consistently organized and reusable across different manufacturing processes.
- Data Integrity and Consistency: AMMONOID enforces data quality standards by requiring users to define manufacturing methods and subprocesses, setting constraints that future users must follow. This framework reduces inconsistencies and makes experimental data reliable for modeling and simulation.
Key Advantages
- Reduced Data Management Costs: Eliminate manual record-keeping errors and version control complexities with automated data storage.
- Accelerated Predictive Modeling: Immediately utilize data in predictive models without additional formatting, supporting faster and more efficient experiment cycles.
- Enhanced Data Reusability: Consistent, ontology-driven data structuring means manufacturing data can be leveraged across diverse modeling and simulation applications, supporting cross-disciplinary innovation.
- User-Friendly Interface with MOOSE Integration: Allows easy access to MOOSE stochastic tools without requiring deep expertise in finite element modeling, expanding accessibility to robust modeling tools.
Market Applications
AMMONOID is invaluable for research technicians and scientists in experimental manufacturing sectors, including materials science, aerospace, and automotive industries. The tool is ideal for cataloging and structuring data for materials testing, design of experiments, and quality assurance. In labs developing novel materials, AMMONOID aids in systematic data collection and analysis, fueling the iterative improvement needed for breakthroughs in product development.
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