Smart machining systems: issues and research trends

Deshayes, Laurent; Welsch, Lawrence A.; Dönmez, Alkan; Ivester, Robert W.; Gilsinn, David E.; Rhorer, Richard L.; Whitenton, Eric P.; Potra, Florian A.

doi:10.1007/1-4020-4617-0_25

Cited by 15 publications

(15 citation statements)

References 17 publications

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“…The smart machining system program is an operational project at the National Institute of Standards and Technology (NIST), USA, with an emphasis on machining information models. Two main categories of machining models were under consideration for solving interoperability issues: models describing the cutting process, and models describing the machine tool resources [32]. Thus, the role of the resource modelling methodology is considered as a major aspect in the next-generation manufacturing paradigms.…”

Section: Representation Of Cnc Machine Tools Within a Manufacturing Ementioning

confidence: 99%

A unified manufacturing resource model for representation of computerized numerically controlled machine tools

Vichare

Nassehi

Newman

2009

Proceedings of the Institution of Mechanical Engineers, Part B:

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The capability of any manufacturing system primarily depends on its available machine tools. Thus machine tool representation is a vital part of modelling any manufacturing system. With the rapid advances in computerized numerically controlled (CNC) machines, machine tool representation has become a more challenging task than ever before. Today's CNC machine tools are more than just automated manufacturing machines, as they can be considered multi-purpose, multi-tasking, and hybrid machining centres. This paper presents a versatile methodology for representing such state-of-the-art CNC machining system resources.A machine tool model is a conceptual representation of the real machine tool and provides a logical framework for representing its functionality in the manufacturing system. There are several commercial modelling tools available in the market for modelling machine tools. However, there is no common methodology among them to represent the wide diversity of machine tool configurations. These modelling tools are either machine vendor specific or limited in their scope to represent machine tool capability. In addition, the current information models of STEP-NC, namely ISO 14649, can only describe machining operations, technologies, cutting tools, and product geometries. However, they do not support the representation of machine tools. The proposed unified manufacturing resource model (UMRM) has a data model which can fill this gap by providing machine specific data in the form of an EXPRESS schema and act as a complementary part to the STEP-NC standard to represent various machine tools in a standardized form. UMRM is flexible enough to represent any type of CNC machining centre. This machine tool representation can be utilized to represent machine tool functionality and consequential process capabilities for allocating resources for process planning and machining.

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Section: Representation Of Cnc Machine Tools Within a Manufacturing Ementioning

confidence: 99%

A unified manufacturing resource model for representation of computerized numerically controlled machine tools

Vichare

Nassehi

Newman

2009

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…Machining is a complex activity based on a system of many diverse physical, electronic, and human elements with interacting and interdependent activities that are difficult to predict and control. Mathematical and physical models of machining systems aim to capture the interdependence of elements within the system to enable effective system analysis and optimization, as well as a predictive capability to compensate for effects of changes in the system (Deshayes and others 2005). The ability of the manufacturing system to accommodate unpredicted changes is an important factor determining robustness and longevity.…”

Section: Machining Production Systemsmentioning

confidence: 99%

“…Adaptive control (Masory and Koren 1985) and recursive constraint bounding (Ivester and Danai 1996, 1998; Ivester and others 1997) techniques provide some means for coping with variability and repeatability issues (Liang and others 2004), but are not adequate for planning purposes. Effective planning, which is critical for agile manufacturing and modern machining production systems (Deshayes and others 2005), necessitates a methodical approach for addressing predictability of machining operations.…”

Section: Machining Production Systemsmentioning

confidence: 99%

“…Machining process engineers must be able to predict and compensate for the many sources of error that change over time and are often task specific (Jurrens and others 2003). The following sections provide an overview of research activities aimed at developing the necessary infrastructure to realize this capability (Deshayes and others 2005; Ivester and others 2005). The research presented in this article focuses on work conducted at the Natl.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Productivity Improvement through Modeling: An Overview of Manufacturing Experience for the Food Industry

Ivester

2008

Comp Rev Food Sci Food Safe

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This article is part of a collection entitled "Models forABSTRACT: Food production and manufacturing of durable goods share common needs for understanding, modeling, and controlling processing conditions in order to obtain desirable product characteristics. This article presents a review of research on manufacturing process modeling, with a focus on historical developments in the metrology and standards infrastructure to improve productivity and competitiveness of machining systems. To remain competitive, manufacturing requires accurate and reliable machines and processes whose characteristics are known and guaranteed for a wide variety of tasks and conditions. Productive, high-quality manufacturing will increasingly rely on a science-based understanding and monitoring of the available machining processes and equipment to produce the first part and every subsequent part on time and to specification with no significant time spent on process development or setup.

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“…Informative and timely asset management can be used to accurately assess manufacturing operation and to make adjustments to meet shifting manufacturing conditions. Example manufacturing roles for asset management include: accumulating resource knowledge for calculating accounting functions such as the actual machining cost of a part for bidding, and determining profits [1–4],identifying production bottlenecks [5–8],building up machine histories in order to perform predictive maintenance [9–12],incorporating equipment and process knowledge dynamically on a machine [13–15],recognizing excessively high asset utilization as a prerequisite to determining procurement needs [16–18]. …”

Section: Introductionmentioning

confidence: 99%

Automating Asset Knowledge With MTConnect

Venkatesh

Manning

et al. 2016

Volume 3: Joint MSEC-NAMRC Symposia

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In order to maximize assets, manufacturers should use real-time knowledge garnered from ongoing and continuous collection and evaluation of factory-floor machine status data. In discrete parts manufacturing, factory machine monitoring has been difficult, due primarily to closed, proprietary automation equipment that make integration difficult. Recently, there has been a push in applying the data acquisition concepts of MTConnect to the real-time acquisition of machine status data. MTConnect is an open, free specification aimed at overcoming the “Islands of Automation” dilemma on the shop floor. With automated asset analysis, manufacturers can improve production to become lean, efficient, and effective. The focus of this paper will be on the deployment of MTConnect to collect real-time machine status to automate asset management. In addition, we will leverage the ISO 22400 standard, which defines an asset and quantifies asset performance metrics. In conjunction with these goals, the deployment of MTConnect in a large aerospace manufacturing facility will be studied with emphasis on asset management and understanding the impact of machine Overall Equipment Effectiveness (OEE) on manufacturing.

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Smart machining systems: issues and research trends

Cited by 15 publications

References 17 publications

A unified manufacturing resource model for representation of computerized numerically controlled machine tools

A unified manufacturing resource model for representation of computerized numerically controlled machine tools

Productivity Improvement through Modeling: An Overview of Manufacturing Experience for the Food Industry

Automating Asset Knowledge With MTConnect

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