Sharpness Evaluation of Grinding Wheel Face by the Grinding of Steel Ball

Nakayama, Keisuke; Takagi, Jun-ichiro; Irie, Etsuo; Okuno, Koji

doi:10.1016/s0007-8506(07)61327-4

Cited by 20 publications

(12 citation statements)

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“…• Friction processes: [10] Characterized by transformation of mechanical energy into other types of energy, such as thermal, acoustic, electrical, and chemical energy. Friction processes bring about physico-chemical changes to the materials at the contact surface, as well as the spreading of these changes inside the volume of the workpiece.…”

Section: Interrelationships Between System Elementsmentioning

confidence: 99%

Tribosystems of Abrasive Machining Processes

Marinescu¹,

Rowe²,

Dimitrov³

et al. 2004

Tribology of Abrasive Machining Processes

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Section: Interrelationships Between System Elementsmentioning

confidence: 99%

Tribosystems of Abrasive Machining Processes

Marinescu¹,

Rowe²,

Dimitrov³

et al. 2004

Tribology of Abrasive Machining Processes

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“…As this paper includes information about the sharpness of the wheel, which was measured in the experiments, a description is given here of the method used for measuring wheel sharpness. According to Nakayama et al [9], the wheel sharpness is extremely important because it is a determining factor of the cutting forces, heat generation, surface integrity, and other important characteristics of the grinding process in the production of a workpiece. The authors proposed a simple method whereby a steel ball is ground by the wheel to be tested under a constant load, and the material removal rate is measured as a function of various normal pressures.…”

Section: Introductionmentioning

confidence: 99%

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

Moia

Thomazella

Aguiar

et al. 2014

J Braz. Soc. Mech. Sci. Eng.

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The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as ''sharp'' (with cutting capacity) or ''dull'' (with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

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“…The sharpness of the wheel face is also directly related to heat generation and grinding forces developed during grinding. Nakayama et al [2] proposed a procedure for evaluating the abrasive wheel sharpness. Alexandre et al [3] proposed a test bench for measuring the sharpness of the abrasive wheel to study the dresser condition.…”

Section: Sharpness Evaluation Of the Grinding Wheelmentioning

confidence: 99%

Acoustic Emission-Based Grinding Wheel Sharpness Monitoring Using Machine Learning Classifier

Revant

Kumar

Rameshkumar

et al. 2020

Lecture Notes in Mechanical Engineering

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Grinding is one of the important secondary manufacturing processes used to improve the dimensional accuracy, surface finish and geometric form of the component. Grinding wheel consists of abrasive particles, which perform the metal removal function. The sharpness of the grinding wheel is one of the important factors for achieving the required surface geometry in the component. In this study, a simple device used to measure the sharpness of the abrasive particles of the grinding wheel is designed and fabricated. Aluminium oxide grinding wheel conditions are established using the sharpness of the abrasive grinding wheel. Grinding process is monitored using acoustic emission (AE) Sensor. AE features are extracted in time domain and dominated features which contain useful information about the grinding wheel that are identified. A correlation between grinding wheel condition and AE feature is established using ANN-based machine learning classifier.

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Sharpness Evaluation of Grinding Wheel Face by the Grinding of Steel Ball

Cited by 20 publications

References 0 publications

Tribosystems of Abrasive Machining Processes

Tribosystems of Abrasive Machining Processes

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

Acoustic Emission-Based Grinding Wheel Sharpness Monitoring Using Machine Learning Classifier

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