Parametric tool wear estimation solution of HSC appropriate machining

Luo, Yiyong

doi:10.1007/s00170-003-1691-1

Cited by 12 publications

(9 citation statements)

References 7 publications

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“…Advanced manufacturing technologies are adopted in industry because of worldwide competition (Luo, 2002(Luo, , 2003(Luo, , 2004. Simulation technology can reduce product cost, shorten product development time, and improve product quality.…”

Section: Discussionmentioning

confidence: 99%

Data exchange strategy for manufacturing simulation of shop floor information systems

Luo

Lee

2009

IJRFITA

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Simulation is defined as the imitation of the operation of a system or real-world process over time, and in many cases, manufacturing provides one of the most important applications of simulation (Zolfaghari and Roa, 2006). Standard interfaces could make information sharing effective, and hence promote the utilization of simulators. An information model (McLean et al., 2005), which represents machine shop data and facilitates data sharing among machine shop's manufacturing execution system, scheduling system, and simulation system, has been developed at the National Institute of Standards and Technology (NIST). This paper briefs the machine shop information model. This paper discusses information exchange, using NIST's information model, between different representations and presents an algorithm to exchange data between a database system and an eXtensible Markup Language (XML) [1] document. The algorithm has been built based on Document Object Model (DOM), XML Path Language (XPath), and Open Database Connectivity Database Engine (ODBC). The paper also describes interfaces for XML schema's validation, structured query, and data transfer.

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Section: Discussionmentioning

confidence: 99%

Data exchange strategy for manufacturing simulation of shop floor information systems

Luo

Lee

2009

IJRFITA

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“…Advanced manufacturing technologies are adopted in industry because of world wide competitive (Luo, 2002;Luo, 2003;Luo 2004). Simulation technology can reduce product cost, shorten product development time, and improve product quality.…”

Section: Discussionmentioning

confidence: 99%

Machine shop information model application, next step

Lee¹,

Luo²

2007

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Simulation is defined as the imitation of the operation of a system or real-world process over time, and in many cases, manufacturing provides one of the most important applications of simulation (Zolfaghari and Roa, 2006). Standard interfaces could make information effective sharing, and hence promote the utilization of simulators. An information model (McLean et al., 2005), which represents machine shop data and facilitates data sharing among machine shop's manufacturing execution system, scheduling system, and simulation system, has been developed at the National Institute of Standards and Technology (NIST). Recently NIST researchers in collaboration with industrial partners have been working on a standards development effort titled Core Manufacturing Simulation Data (CMSD) Product Development Group (PDG) under the guidelines, policies and procedures of the Simulation Interoperability Standards Organization (SISO). A key activity of the CMSD PDG is to develop a CMSD information model using the machine shop information model as the strawman. This paper briefs the machine shop information model and the CMSD information model. This paper discusses information exchange, using NIST's information model, between different representations and presents an algorithm to exchange data between a database system and an eXtensible Markup Language (XML) [1] document. The algorithm has been built based on Document Object Model (DOM), XML Path Language (XPath), and Open Database Connectivity Database Engine (ODBC). The paper also describes interfaces for XML schema's validation, structured query, and data transfer.

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“…They proposed optimal control strategy that demonstrates how production cost can be minimised by adjusting machining parameters and extending the tool usage within the constraints for specific machining conditions. Luo (2004) presented an improved algorithm and a knowledge-based decision model developed to calculate effective tool contact is also discussed to help reduce calculation time and improve calculation efficiency. Danko et al (2004) considered the application of Radial Basis Function Neural Network (RBFNN) for tool wear determination in the milling process.…”

Section: Introductionmentioning

confidence: 99%

Modelling of tool wear and surface roughness in hard turning using regression and artificial neural network

Palanisamy¹,

Shanmugasundaram²

2008

IJMMM

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The objective of this paper is to study the effect of tool wear on surface roughness in hard turning. Tool wear prediction plays an important role in industry for higher productivity, and product quality. This paper focuses on two different models, namely Regression and Artificial Neural Network (ANN) models for predicting tool wear and surface roughness taking cutting speed, feed and depth of cut as input parameters. Experiments have been conducted for measuring tool wear and surface roughness based on Design of Experiments (DOE) technique in a PSG 110 CNC Lathe on machining of AISI H13 steel bars using Cubic Boron Nitride (CBN) content inserted tool. The experimental values are used in Six Sigma software for finding the coefficients to develop the Regression model. The experimentally measured values are also used to train the feed forward back propagation ANN for prediction of tool wear and surface roughness. The performance of the trained ANN model has been tested with experimental data, and found to be better predictions for tool flank wear and surface roughness within the range that they had been trained.Reference to this paper should be made as follows: Palanisamy, P. and Shanmugasundaram, S. (2008) 'Modelling of tool wear and surface roughness in hard turning using regression and artificial neural network', Int. J.

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Parametric tool wear estimation solution of HSC appropriate machining

Cited by 12 publications

References 7 publications

Data exchange strategy for manufacturing simulation of shop floor information systems

Data exchange strategy for manufacturing simulation of shop floor information systems

Machine shop information model application, next step

Modelling of tool wear and surface roughness in hard turning using regression and artificial neural network

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