Identification of common sensory features for the control of CNC milling operations under varying cutting conditions

Ertekin, Yalcin; Kwon, Yongjin; Tseng, Tzu-Liang

doi:10.1016/s0890-6955(03)00087-7

Cited by 68 publications

(27 citation statements)

References 11 publications

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“…Cutting parameters, i.e. feed rate, depth of cut, cutting speed, cutting edge and the number of cutting tool, also have effects on cutting under varying cutting conditions [5,6]. Several modeling techniques of input-output and in-process parameter relationship using ANN sets offer a distribution-free alternative and have attracted the attention of manufacturing practitioners and researchers alike when they run into difficulties in building empirical models in metal cutting process control.…”

Section: Literature Surveymentioning

confidence: 99%

Optimization Technique For Surface Roughness Prediction in Turning Operation

Ojha¹,

Yadav²,

Yadav

2015

SIJSMS

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Section: Literature Surveymentioning

confidence: 99%

Optimization Technique For Surface Roughness Prediction in Turning Operation

Ojha¹,

Yadav²,

Yadav

2015

SIJSMS

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“…Therefore, the procedure of the cutting force monitoring has received attention of many researchers to detect the tool condition, work piece accuracy, etc. [14][15][16][17][18][19][20][21]. Many studies have been conducted to implement an e ective tool to prevent the tool breakage by monitoring the cutting force [22].…”

Section: Introductionmentioning

confidence: 99%

Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

2017

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Abstract. Cutting tool wear in machining processes reduces the product surface quality, a ects the dimensional and geometrical tolerances, and causes tool breakage during the metal cutting. Therefore, online tool wear monitoring is needed to prevent reduction in machining quality. An Arti cial Neural Network (ANN) model was developed in this study to predict and simulate the tool ank wear. To achieve this aim, an experiment array was provided using full factorial method, and the tests were conducted on a CNC lathe machine tool. Vibration amplitude of the cutting tool and cutting forces were considered as criterion variables in monitoring the tool ank wear. For designing the model, the cutting parameters, cutting forces, and vibration amplitude were de ned as model inputs, and tool ank wear was selected as an output. The model was also introduced as a simulation block diagram to be used as a useful model in online and automated manufacturing systems. The estimated and measured results were then compared with each other. Based on the comparison results, maximum squared error values are under 6 10 14 mm, and R 2 is 1, meaning that the designed model can predict the results with high and reliable accuracy.

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“…In general, cutting conditions, the cutting tool geometry, the cutting tool type, the use of cooling liquid, the rigidity of the machine tool used, the cutting method, and the type of material used are effective on average surface roughness in the machining processes. Cutting parameters such as feed rate, cutting depth, cutting speed, and the number of cutting edges have some effects on cutting processes [11,12].…”

Section: Introductionmentioning

confidence: 99%

The Effects of Humidity on Cast PA6G during Turning and Milling Machining

Bozdemir

2017

Advances in Materials Science and Engineering

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We compared the foundry PA6G samples in several dry and humid but different storage environments by processing them under the same cutting conditions such as progress rate (100, 120, 140, and 160 mm/min), cutting rate (90, 110, and 130 m/min), and cutting depth (1, 1.5, 2, 2.5, and 3 mm), in terms of formation of average surface roughness values. An improvement of 10.4% in average surface roughness was observed in the measurements performed after the milling process on the humid material and then the process was carried out under a dry condition. Degradation of about 14% in the average surface roughness was observed. The measurement was carried out after the samples were used in milling measurement which was performed on the dry PA6G material that was kept in a humid environment. An improvement of 6.4% in average surface roughness was observed. The measurements were performed after CNC machines process was applied on humid and dried PA6G material. This difference between milling and turning procedures is caused by the workpiece losing its humidity in the turning machine due to the turning effect. It was noted that the processes performed on the CNC turning stand were less affected by the humidity factor.

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Identification of common sensory features for the control of CNC milling operations under varying cutting conditions

Cited by 68 publications

References 11 publications

Optimization Technique For Surface Roughness Prediction in Turning Operation

Optimization Technique For Surface Roughness Prediction in Turning Operation

Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

The Effects of Humidity on Cast PA6G during Turning and Milling Machining

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