Jungyub Woo

Developing a virtual machining model to generate MTConnect machine-monitoring data from STEP-NC

The ability to predict performance of manufacturing equipment during early stages of process planning is vital for improving efficiency of manufacturing processes. In the metal cutting industry, measurement of machining performance is usually carried out by collecting machine-monitoring data that record the machine tool’s actions (e.g. coordinates of axis location and power consumption). Understanding the impacts of process planning decisions is central to the enhancement of the machining performance. However, current methodologies lack the necessary models and tools to predict impacts of process planning decisions on the machining performance. This paper presents the development of a virtual machining model (called STEP2M model) that generates machine-monitoring data from process planning data. The STEP2M model builds upon a physical model-based analysis for the sources of energy on a machine tool, and adopts STEP-NC and MTConnect standardised interfaces to represent process planning and machine-monitoring data. We have developed a prototype system for 2-axis turning operation and validated the system by conducting an experiment using a Computer Numerical Control lathe. The virtual machining model presented in this paper enables process planners to analyse machining performance through virtual measurement and to perform interoperable data communication through standardised interfaces.

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 that 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. It will require a large amount of effort and expertise to build such a virtual factory. This paper describes an effort to build a multiple resolution model of a manufacturing cell. The model provides the ability to study the performance at the cell level or at the machine level. The benefits and limitations of the presented approach and future research directions are also described.

Agile test framework for business-to-business interoperability

Business-to-business (B2B) applications are tested routinely for conformance and interoperability against a set of data exchange standards before deployment. However, the existence of many data exchange standards, planned utilizations, deployment environments, and testing scenarios makes it difficult to develop reusable testing tools. To address this challenge, we propose the Agile Test Framework (ATF), which consists of a test case design and test execution model. Test case is defined at two levels: abstract and executable. The abstract level addresses issues related to human understanding and the executable level addresses issues related to machine processing. The test execution model addresses issues related to both reusability and plug-compatibility. The ATF allows the test engineer to generate test cases for a variety of standards and scenarios. Thus, it increases reusability, extensibility, and efficiency compared to other test frameworks.

A Virtual Milling Machine Model to Generate Machine-Monitoring Data for Predictive Analytics

Real data from manufacturing processes are essential to create useful insights for decision-making. However, acquiring real manufacturing data can be expensive and time consuming. To address this issue, we implement a virtual milling machine model to generate machine monitoring data from process plans. MTConnect is used to report the monitoring data. This paper presents (1) the characteristics and specification of milling machine tools, (2) the architecture for implementing the virtual milling machine model, and (3) the integration with a simulation environment for extending to a virtual shop floor model. This paper also includes a case study to explain how to use the virtual milling machine model for predictive analytics modeling.

Development of a Data and Model-Interconnected Holonic Architecture for Intelligent Decision-Making on Cyber-Physical Production Systems

In manufacturing industries, the intelligence, autonomy and interconnectivity of manufacturing objects, and the adaptiveness in dynamic changes have been recognized as desirable objectives; however, they were hard to implement. Recently, the convergence of the advances in information and communication technology is making it possible to implement those objectives, called Smart Manufacturing (SM). Of these SM technologies, Cyber-Physical Production Systems (CPPS) will be considerably one of the most-advanced technologies because CPPS envisions dynamic and autonomous capabilities through mirroring physical and their cyber manufacturing objects in real-time. These capabilities match with the aim of Holonic Manufacturing Systems (HMS) in terms of autonomy and cooperation, thereby making HMS re-highlighted as a control architecture of CPPS. This paper presents the development of a data and model-interconnected holonic control architecture that especially considers the synchronization with a big data infrastructure and a data analytics environment. The present work aims at providing : 1) a predictive process-planning mechanism with the connection of manufacturing data and decision-making models, and 2) a systems integration environment for opening the information about intelligent manufacturing objects. The goals of the present work are to increase intelligence and interconnection, which are the major performances pursued by CPPS.

Considerations and recommendations for data availability for data analytics for manufacturing

Data analytics is increasingly becoming recognized as a valuable set of tools and techniques for improving performance in the manufacturing enterprise. However, data analytics requires data and a lack of useful and usable data has become an impediment to research in data analytics. In this paper, we describe issues that would help aid data availability including data quality, reliability, efficiency, and formats specific to data analytics in manufacturing. To encourage data availability, we present recommendations and requirements to guide future data contributions. We also describe the need for data for challenge problems in data analytics. A better understanding of these needs, recommendations, and requirements may improve the ability of researchers and other practitioners to improve research and more rapidly deploy data analytics in manufacturing.

Towards Test Framework for Efficient and Reusable Global e-Business Test Beds

We introduce a novel Agile Test Framework (ATF) for eBusiness systems interoperability and conformance testing. Largely, existing test frameworks have been designed to only consider a limited collection of specific test requirements. This makes it difficult to apply these frameworks to other similar standards specifications and to support cooperative work in building distributed global test systems. ATF addresses the core issues found in these traditional testing frameworks to alleviate the resulting inefficiencies and lack-of-reuse problems that arise in the development of eBusiness test beds.