Enabling Flexible Manufacturing Systems by Using Level of Automation as Design Parameter

Handling flexibility in an ever changing manufacturing environment is one of the key challenges for a successful industry. By using tools for virtual manufacturing, industries can analyze and predict outcomes of changes before taking action to change the real manufacturing systems. This paper describes a simulation tool that can be used to study the effect of level of automation issues on the design of manufacturing systems, including their effect on the overall system performance, ergonomics, environment, and economic measures. Determining a suitable level of automation can provide a manufacturing system with the flexibility needed to respond to the unpredictable events that occur in factory systems such as machine failures, lack of quality, lack of materials, lack of resources, etc. In addition, this tool is designed to use emerging simulation standards, allowing it to provide a neutral interface for both upstream and downstream data sources

Core Manufacturing Simulation Data – a manufacturing simulation integration standard: overview and case studies

Standard representations for information entities common to manufacturing simulation could help reduce the costs associated with simulation model construction and data exchange between simulation and other manufacturing applications. This would make simulation technology more affordable and accessible to a wide range of potential industrial users. The Core Manufacturing Simulation Data (CMSD) specification was created to foster the more widespread use of manufacturing simulation technology through the reduction of data interoperability issues. CMSD is a standardized, computer-interpretable representation that allows for the efficient exchange of manufacturing shop-floor-related data in a manner that it can be used to create and execute manufacturing simulations. CMSD was standardized under the auspices of the international Simulation Interoperability Standards Organization (SISO). It defines an information model that describes the characteristics of and relationships between the core manufacturing entities that define shop floor operations. This enables greater integration and data exchange possibilities for manufacturing simulations and other manufacturing applications. This paper presents an overview of CMSD, its motivation, structure, and content. Case studies using CMSD to integrate real world manufacturing applications are also presented

Complexity and entropy representation for machine component diagnostics.

The Complexity-entropy causality plane (CECP) is a parsimonious representation space for time series. It has only two dimensions: normalized permutation entropy ([Formula: see text]) and Jensen-Shannon complexity ([Formula: see text]) of a time series. This two-dimensional representation allows for detection of slow or rapid drifts in the condition of mechanical components monitored through sensor measurements. The CECP representation can be used for both predictive analytics and visual monitoring of changes in component condition. This method requires minimal pre-processing of raw signals. Furthermore, it is insensitive to noise, stationarity, and trends. These desirable properties make CECP a good candidate for machine condition monitoring and fault diagnostics. In this work we study the effectiveness of CECP on three rotary component condition assessment applications. We use CECP representation of vibration signals to differentiate various machine component health conditions for rotary machine components, namely roller bearing and gears. The results confirm that the CECP representation is able to detect, with high accuracy, changes in underlying dynamics of machine component degradation states. From class separability perspective, the CECP representation is able to generate linearly separable classes for the classification of different fault states. This classification performance improves with increasing signal length. For signal length of 16,384 data points, the fault classification accuracy varies from 90% to 100% for bearing applications, and from 85% to 100% for gear applications. We observed that the optimum parameter for CECP representatino depends on the application. For bearing applications we found that embedding dimension D = 4, 5, 6, and embedding delay τ = 1, 2, 3 are suitable for good fault classification. For gear applications we find that embedding dimension D = 4, 5, and embedding delay τ = 1, 5 are suitable for fault classification

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

International audienceBayesian 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 article 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. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models. The BN PMML representation is available in PMML v4.3 released by the Data Mining Group. We demonstrate the conversion of analytical models into the BN PMML representation, and the PMML representation of such models into analytical models, through a Python parser. The BNs obtained after parsing PMML representation can then be used to perform Bayesian inference. Finally, we illustrate the developed BN PMML schema for a welding process