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

Gajate, Agustín; Haber, Rodolfo E.; Toro, Raúl M. del; Vega, Pastora; Bustillo, Andrés

doi:10.1007/s10845-010-0443-y

Cited by 77 publications

(34 citation statements)

References 30 publications

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“…Welding time with 0.437 p-values among the other parameters has a high p-value, which indicates that this is not an important parameter. In previous studies, promising results were achieved to find the method of optimization and control modeling, and estimation of the experimental and theoretical results [29,30]. A comparison of the neural network and Taguchi approaches are shown in Figure 12.…”

Section: Discussionmentioning

confidence: 99%

A Comparative Study on the Effect of Welding Parameters of Austenitic Stainless Steels Using Artificial Neural Network and Taguchi Approaches with ANOVA Analysis

Kurt

Oduncuoğlu

Yilmaz

et al. 2018

Metals

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Abstract:In order to investigate the structure of welds, austenitic stainless steel (SS) studs with a diameter of 6 mm were welded to austenitic SS plates with a thickness of 5 mm using an arc stud welding (ASW) method. The effects of the welding current, welding time, and tip volume of the stud on the microstructure and ultimate tensile strength (UTS) of the welded samples were investigated in detail. The formation of δ-ferrites was detected in the weld zone because of the higher heat generated during the welding process. Higher welding current and time adversely affected the stud and significantly reduced the UTS of the samples. The UTS of the joints was also estimated using artificial neural network (ANN) and Taguchi approaches. The mathematical formulations for these two approaches were given in explicit form. Experimental results showed that the neural network results are more consistent with experimental results than those of the Taguchi method. Overall, it can be concluded that in order to achieve good welding joints and high strength values, ASW parameters should be investigated properly to determine the optimum conditions for each metal.

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Section: Discussionmentioning

confidence: 99%

A Comparative Study on the Effect of Welding Parameters of Austenitic Stainless Steels Using Artificial Neural Network and Taguchi Approaches with ANOVA Analysis

Kurt

Oduncuoğlu

Yilmaz

et al. 2018

Metals

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show abstract

“…Moreover, this figure of merit has been applied in previous case studies [17], [18] evaluating the performance of other modeling strategies.…”

Section: A Tool Wear Monitoringmentioning

confidence: 99%

“…A comparative study with an adaptive neural-fuzzy inference system (ANFIS) [18] and a transductive-weighted neurofuzzy inference system (TWNFIS) [17], [24] was conducted, in order to evaluate the performance of the proposed strategy for modeling tool wear. The reader can find further details on the topology and training parameters of the neurofuzzy models in [17].…”

Section: A Tool Wear Monitoringmentioning

confidence: 99%

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Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes

Penedo

Haber

Gajate

et al. 2012

IEEE Trans. Ind. Inf.

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Abstract-There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy fe-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

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“…Human decision making may me incorporated in con dition monitoring algorithms such as dealt with in Gajate et al (2012). For the NNalgorithm as used in this work, decisionmaking can be avoided with the availability of a much larger number of training data.…”

Section: Current Accuracymentioning

confidence: 99%

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

2012

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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.

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

Cited by 77 publications

References 30 publications

A Comparative Study on the Effect of Welding Parameters of Austenitic Stainless Steels Using Artificial Neural Network and Taguchi Approaches with ANOVA Analysis

A Comparative Study on the Effect of Welding Parameters of Austenitic Stainless Steels Using Artificial Neural Network and Taguchi Approaches with ANOVA Analysis

Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes

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

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