Monitoring of cylindrical grinding processes by use of a non-contact AE system

Sutowski, Paweł; Nadolny, Krzysztof; Kapłonek, Wojciech

doi:10.1007/s12541-012-0228-7

Cited by 27 publications

(15 citation statements)

References 25 publications

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“…Turning is widely used today in car manufacturing, machine, and machine-tool building as well as in other industries. One essential quality parameter in finish turning [1][2][3][4][5][6][7], milling [8][9][10][11][12], grinding [13][14][15] is surface roughness, Ra. There are many promising modern materials, such as Aluminum Alloy 6061 that is a frequent industrial component for aeronautics, yachting, and optical instruments.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

Abbas

Pimenov

Ердаков

et al. 2018

Metals

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Aluminum Alloy 6061 components are frequently manufactured for various industries-aeronautics, yachting, and optical instruments-due to their excellent physical and mechanical properties, including corrosion resistance. There is little research on the mechanical tooling of AA6061 and none on its structure and properties and their effects on surface roughness after finish turning. The objective of this comprehensive study is, therefore, to ascertain the effects of both the modern method of hardening AA6061 shafts and the finish turning conditions on surface roughness, Ra, and the minimum machining time for unit-volume removal, T m , while also establishing the cost price of processing one part, C. The hardening methods improved both the physical and the mechanical material properties processed with 2, 4, and 6 passes of equal channel angular pressing (ECAP) at room temperature, using an ECAP-matrix with a channel angle of 90 •. The reference workpiece sample was a hot extruded chip under an extrusion ratio (ER) of 5.2 at an extrusion temperature of 500 • C (ET = 500 • C). The following results were obtained: grain size in ECAP-6 decreased from 15.9 to 2.46 µm, increasing both microhardness from 41 Vickers hardness value (HV) to 110 HV and ultimate tensile strength from 132.4 to 403 MPa. The largest decrease in surface roughness, Ra-70%, was obtained turning a workpiece treated with ECAP-6. The multicriteria optimization was computed in a multilayer perceptron-based artificial neural network that yielded the following optimum values: the minimal length of the three-dimensional estimates vector with the coordinates Ra = 0.800 µm, T m = 0.341 min/cm 3 , and C = 6.955 $ corresponded to the optimal finish turning conditions: cutting speed v c = 200 m/min, depth of cut a p = 0.2 mm, and feed per revolution f r = 0.103 mm/rev (ET-500 extrusion without hardening).

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

Abbas

Pimenov

Ердаков

et al. 2018

Metals

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show abstract

“…Today, many industries such as mechanical engineering, automobile manufacturing, machine-tool building, and aerospace industries, among others, all employ turning. One of the main quality parameters in finish turning [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], milling [ 8 , 9 , 10 , 11 ], and grinding [ 12 , 13 ] is surface roughness. AZ 61 magnesium alloys are widely used in industry, due to their lightweight structure [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs

et al. 2018

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Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth–Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness (Ra) prediction of one component in computer numerical control (CNC) turning over minimal machining time (Tm) and at prime machining costs (C). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra, Tm, and C, in relation to cutting speed, vc, depth of cut, ap, and feed per revolution, fr. For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values vc, ap, and fr. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, Tm = 0.358 min/cm3, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed vc = 250 m/min, cutting depth ap = 1.0 mm, and feed per revolution fr = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

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“…The authors confirmed the clear influence of machining conditions on the geometric structure of the obtained cuts and the registered values of the emitted high-frequency stress waves. Another example is the cylindrical grinding processes [29], where non-contact, Schall-Emissions-Hydrophon (SEH) sensors and monitoring SEM units are used. The parameters describing the AE impulse indicate the intensity and nature of phenomena in the grinding area, and therefore allow for the observation of the active acoustic wave sources [30].…”

Section: Acoustic Emissionmentioning

confidence: 99%

The estimation of machining results and efficiency of the abrasive electro-discharge grinding process of Ti6Al4V titanium alloy using the high-frequency acoustic emission and force signals

Sutowski

Święcik

2017

Int J Adv Manuf Technol

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The paper presents the results obtained for abrasive electro-discharge grinding (AEDG) of Ti6Al4V titanium alloy with the use of super hard grinding wheels of cubic boron nitride with a metal bond. The experimental investigations were focused on process monitoring for sharp and worn grinding wheels. For the evaluation of the grinding process, the grinding force amplitude and a few descriptors of the high-frequency stress waves were investigated in both time and frequency domains. The results show that during the AEDG, specific elastic waves are generated. Since these waves have characteristic amplitudes, frequencies, and other different descriptors dependent on the current grinding wheel cutting ability and discharge parameters, they can be applied to estimate a statistically significant regression function. For each measurement signal, a few statistical features are calculated and used as input for data selection and classification procedures. Registration and classification of forces and acoustic emission signals can be used to analyse the abrasive electro-discharge grinding process and allow for the determination of the machining results and the lifespan of super hard grinding wheels.

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Monitoring of cylindrical grinding processes by use of a non-contact AE system

Cited by 27 publications

References 25 publications

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs

The estimation of machining results and efficiency of the abrasive electro-discharge grinding process of Ti6Al4V titanium alloy using the high-frequency acoustic emission and force signals

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