Simulating a Virtual Machining Model in an Agent-Based Model for Advanced Analytics.

Monitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data is expensive and time consuming.

Towards Standards-Based Generation of Reusable Life Cycle Inventory Data Models for Manufacturing Processes.

The production stage of a product's life cycle can significantly contribute to its overall environmental impact. Estimates of environmental impact for a product are typically produced using Life Cycle Assessment (LCA) methods. These methods rely on Life Cycle Inventory (LCI) data containing impact estimates of manufacturing processes and other operations that contribute to a product's creation.

Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing.

Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This paper presents an extension to the Predictive Model Markup Language (PMML) standard, for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination.

TOWARDS A VIRTUAL FACTORY PROTOTYPE.

A virtual factory should represent most of the features and operations of the corresponding real factory. Some of the key features of the virtual factory include the ability to assess performance at multiple resolutions and generate analytics data similar to what is possible in a real factory. One should be able to look at the overall factory performance and be able to drill down to a machine and analyze its performance.