Online non-contact surface finish measurement in machining using graph theory-based image analysis

Tootooni, Mohammad Samie; Liu, Chenang; Roberson, D.G.; Donovan, Ryan; Rao, Prahalada; Kong, Zhenyu; Bukkapatnam, Satish T. S.

doi:10.1016/j.jmsy.2016.09.007

Cited by 57 publications

(23 citation statements)

References 32 publications

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“…Hiremath et al [38] studied the influence of various cutting conditions on cutting forces and surface roughness in the turning of 6061Al on a conventional lathe machine using a PCD tool. Tootooni et al [39] used non-contact, vision-based online measurement for investigating surface finish in the turning of external steel and aluminum-alloy shaft diameters (4340 and 6061 grades). A number of studies [28][29][30][31][32][33][34][35][36][37][38][39] have investigated the turning of 6062 aluminum alloys.…”

Section: Introductionmentioning

confidence: 99%

“…The Pareto method is the most efficient method for solving these sorts of tasks. However, no multicriterion optimization of Alloy 6061 machining was proposed in the above studies [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. Moreover, these studies took no account of the physical and the mechanical properties of the various conditions of the AA6061 workpiece on surface roughness in turning.…”

Section: Introductionmentioning

confidence: 99%

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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%

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

“…In anticipation of the next sixth technology revolution, it is becoming an increasingly important technique for processing large data sets using artificial intelligence and the integration of artificial intelligence algorithms in automated production. Many previous investigations have been devoted towards developing prediction models for rough turning [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Risbood et al [1] researched and produced models for forecasting roughness and dimensional deviation for dry and wet turning of mild steel rods.…”

Section: Introductionmentioning

confidence: 99%

“…Mia and Dhar [16] developed an artificial neural network (ANN) model to predict the average surface roughness in turning hardened steel EN 24 T. Jurkovic et al [17] compared three machine learning methods for predicting the high-speed turning observed parameters (surface roughness (Ra), cutting force (Fc), and the tool life (T)). Tootooni et al [18] reported surface roughness using a noncontact measurement method during the turning process. Abbas [19] analyzed the effect of the feed rate, depth of cut and cutting speed on the surface roughness in turning high-strength steel.…”

Section: Introductionmentioning

confidence: 99%

“…Abbas [19] analyzed the effect of the feed rate, depth of cut and cutting speed on the surface roughness in turning high-strength steel. Though all the previously mentioned research [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] provided prediction models of surface roughness, they failed to solve the problem of determining the optimal cutting parameters for the minimum surface roughness and maximum production rate.…”

Section: Introductionmentioning

confidence: 99%

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Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

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High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of highstrength steel featuring three factors-cutting speed V, feed rate f, and depth of cut t-on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.

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Image Processing for Digital Twin

Pal

Mishra

Pal

et al. 2021

Springer Series in Advanced Manufacturing

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Online non-contact surface finish measurement in machining using graph theory-based image analysis

Cited by 57 publications

References 32 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

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Image Processing for Digital Twin

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