Cutting tool wear estimation for turning

Sharma, Vishal S.; Sharma, Shubham; Sharma, Ajay Kumar

doi:10.1007/s10845-007-0048-2

Cited by 96 publications

(71 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The wear of tools is one of the most important characteristics defining the accuracy of the technological system of part machining [4,14]. The prediction and control of wear is one of the most essential problems emerging in the design of cutting operations [11,16].…”

Section: Introductionmentioning

confidence: 99%

Grindability of Selected Grades of Low-Alloy High-Speed Steel

Jaworski¹,

Kluz²,

Trzepieciński³

2016

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

In this paper, we presents the results of investigations studied the cutting ability and grindability of selected high-speed steels. We analysed the effect of the austenitization temperature on the grain size, the amount of retained austenite and percentage of retained austenite in HS3-1-1 steel. Furthermore, the investigations concerned on the efficiency of the keyway broaches during the whole period of operation were carried out. It was found that the value of average roughness parameter increases together with increases in the grinding depth. The investigations also show the influence of tempering conditions on the volume of carbide phases in HS3-1-1 steel.

show abstract

Section: Introductionmentioning

confidence: 99%

Grindability of Selected Grades of Low-Alloy High-Speed Steel

Jaworski¹,

Kluz²,

Trzepieciński³

2016

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

show abstract

“…In TCM the measurement of its orthogonal components often provides the necessary cutting force information as can be found in the work of Jemielniak et al (1998), Purushothaman (2010) and Sharma et al (2008). This work however compares TCM based on orthogonal forces to the one based on measurement of only the unidirectional strain signal.…”

Section: Introductionmentioning

confidence: 99%

Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring

2012

View full text Add to dashboard Cite

Signal processing using orthogonal cutting force components for tool condition monitoring has established itself in literature. In the application of single axis strain sen sors however a linear combination of cutting force compo nents has to be processed in order to monitor tool wear. This situation may arise when a single axis piezoelectric actuator is simultaneously used as an actuator and a sensor, e.g. its vibration control feedback signal exploited for monitoring purposes. The current paper therefore compares processing of a linear combination of cutting force components to the reference case of processing orthogonal components. Recon struction of the dynamic force acting at the tool tip from signals obtained during measurements using a strain gauge instrumented tool holder in a turning process is described. An application of this dynamic force signal was simulated on a ltermodel of that tool holder that would carry a selfsensing actuator. For comparison of the orthogonal and unidirectional force component tool wear monitoring strategies the same timedelay neural network structure has been applied. Wear sensitive features are determined by wavelet packet analysis to provide information for tool wear estimation. The proba bility of a difference less than 5 percentage points between the ank wear estimation errors of above mentioned two process ing strategies is at least 95 %. This suggests the viability of simultaneous monitoring and control by using a selfsensing actuator.

show abstract

“…However, due to the complexity of tool wear, its nonlinearity and the uncertainties of the process, there have been many approaches that have dealt with this problem through artificial intelligence techniques (Wang et al 2008;Warnecke and Kluge 1998;Pal et al 2009;Purushothaman 2009;Sharma et al 2008a). Several of these approaches use artificial neural networks for modeling or monitoring tool wear in turning process (Sick 2002).…”

Section: Introductionmentioning

confidence: 99%

“…However, the use of neuro-fuzzy systems for modeling and monitoring tool wear is scarce (Abellan-Nebot and Romero Subiron 2010). In the case of turning processes, there are only few approaches based on the Adaptive Network Fuzzy Inference System (ANFIS) or in some variation of itself (Dinakaran et al 2009;Li et al 2000Li et al ,2004Sharma et al 2007Sharma et al , 2008a. This paper presents two approaches for tool wear monitoring in turning processes based on neuro-fuzzy models.…”

Section: Introductionmentioning

confidence: 99%

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

et al. 2010

View full text Add to dashboard Cite

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

show abstract

Cutting tool wear estimation for turning

Cited by 96 publications

References 16 publications

Grindability of Selected Grades of Low-Alloy High-Speed Steel

Grindability of Selected Grades of Low-Alloy High-Speed Steel

Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Contact Info

Product

Resources

About