Fuzzy Logic to Predict Thermal Damages of Ground Parts

Miranda, Hugo Ivan Chang; Aguiar, Paulo; Euzébio, Carlos Danilo Gaioli; Bianchi, Eduardo Carlos

doi:10.2316/p.2010.674-116

Cited by 9 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using this method, a typical example was provided by Chen and Rowe [42] for En9 (medium carbon steel, AISI 1055), where the simulated maximum grinding temperature was 247°C and the predicted maximum tensile residual stress was 178 MPa. Additionally, Li and Jia [234] adopted a BP network model to establish the relationship between the grinding-induced residual stresses and the key grinding parameters; at the same time, this method was also utilized to predict the grinding forces and grinding burn of workpiece [234][235][236][237][238]. The accuracy of the BP network analysis results has been verified in grinding of titanium alloy Ti6Al-4V.…”

Section: Prediction Of Residual Stresses Based On Fuzzy Logic Methods mentioning

confidence: 99%

Review on grinding-induced residual stresses in metallic materials

Ding

Zhang

et al. 2016

Int J Adv Manuf Technol

105

View full text Add to dashboard Cite

This paper provides a state-of-the-art review on the investigations into the residual stresses in metallic structural materials generated by grinding. The materials covered include steels, titanium alloys, and nickel-based superalloys. The formation mechanisms of the residual stresses and their impacts are specifically discussed. Some major influential factors on the residual stresses formation in grinding, such as grinding wheel characteristics, dressing techniques, grinding parameters, cooling conditions, and properties of workpiece materials, are analyzed in detail. These include experimental measurement, modeling, simulation, knowledge-based monitoring, and fuzzy analysis. Finally, the paper highlights some important aspects of grinding-induced residual stresses for further investigation.

show abstract

Section: Prediction Of Residual Stresses Based On Fuzzy Logic Methods mentioning

confidence: 99%

Review on grinding-induced residual stresses in metallic materials

Ding

Zhang

et al. 2016

Int J Adv Manuf Technol

105

View full text Add to dashboard Cite

show abstract

“…The limits of the membership functions were determined by means of the analyzed dressing data of the passes and based on the experience of the research group in dressing operation. The centroid defuzzification method used in [20,31,34] produced satisfactory results. Thus, this method was employed in the present study due to the same nature of the machining processes previously cited.…”

Section: Fuzzy Modelsmentioning

confidence: 99%

“…The AE raw signal was obtained in real time by using an acoustic system, which consisted of a signal processing module, from Sensis, model DM-42, and the AE sensor from the same manufacturer, with frequency response up to 1 MHz, which is the same employed in [31]. Figure 1 shows the tests bench.…”

Section: Test Bench and Experimentsmentioning

confidence: 99%

Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model

Alexandre

Lopes

Dotto

et al. 2018

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The grinding process is situated at the end of the machining chain, where geometric and dimensional characteristics and highquality surface are required. The constant use of cutting tool (grinding wheel) causes loss of its sharpness and clogging of the pores among the abrasive grains. In this context, the dressing operation is necessary to correct these and other problems related to its use in the process. Dressing is a reconditioning operation of the grinding wheel surface aiming at restoring the original condition and its efficiency. The objective of this study is to evaluate the surface regularity and dressing condition of the grinding wheel in the surface grinding process by means of digital signal processing of acoustic emission and fuzzy models. Tests were conducted by using synthetic diamond dressers in a surface grinding machine equipped with an aluminum oxide grinding wheel. The acoustic emission sensor was attached to the dresser holder. A frequency domain analysis was performed to choose the bands that best characterized the process. A frequency band of 25-40 kHz was used to calculate the ratio of power (ROP) statistic, and the mean and standard deviation values of the ROP were inputted to the fuzzy system. The results indicate that the fuzzy model was highly effective in diagnosing the surface conditions of the grinding wheel.

show abstract

“…The geometrical quantities are dimension, shape and waviness, as essential macrogeometric quantities; whereas the roughness condition is the main microgeometric quantity. The surface integrity state can be described by residual stresses, hardness and structure of the material (Brinksmeier, Tönshoff, Czenkusch, & Heinzel, 1998;Miranda, Aguiar, Euzebio, & Bianchi, 2010). As more materials are removed, the form and properties of the tool are mainly changed due to the wear, which is an extremely complex process.…”

Section: Introductionmentioning

confidence: 99%