Thermographic Study of Chip Temperature in High-Speed Dry Milling Magnesium Alloys

Kuczmaszewski, Józef; Zagórski, Ireneusz; Zgórniak, P.

doi:10.1515/mper-2016-0020

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…Furthermore, especially with respect to layers subtracted at a small radial depth of cut, the temperature in the area of cut is reportedly smaller [6]. PCD-cutter tools have been widely reported to produce better quality of surface finish, generate lower cutting force values and prevent tool overheating by decreasing friction at tool–workpiece interface [10]; consequently, the temperatures in the area of cut are significantly lower [11,12,13]. The cutting tool was the subject of analysis in the study by Zagórski et al [14,15], which investigated the AZ91HP magnesium alloy machining with “serrated”, i.e., “wavy-edged”, cutting tools.…”

Section: Introduction—state-of-the-artmentioning

confidence: 99%

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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This paper set out to investigate the effect of cutting speed vc and trochoidal step str modification on selected machinability parameters (the cutting force components and vibration). In addition, for a more detailed analysis, selected surface roughness parameters were investigated. The research was carried out for two grades of magnesium alloys—AZ91D and AZ31—and aimed to determine stable machining parameters and to investigate the dynamics of the milling process, i.e., the resulting change in the cutting force components and in vibration. The tests were performed for the specified range of cutting parameters: vc = 400–1200 m/min and str = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in vc resulted in the reduction of the cutting force components and the displacement and level of vibration recorded in tests. Selected cutting parameters were modelled by means of Statistica Artificial Neural Networks (Radial Basis Function and Multilayered Perceptron), which, furthermore, confirmed the suitability of neural networks as a tool for prediction of the cutting force and vibration in milling of magnesium alloys.

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Section: Introduction—state-of-the-artmentioning

confidence: 99%

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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“…In addition to the surface roughness analysis, phenomena such as acoustic emission and vibration were also analysed. The use of artificial neural networks also enables the prediction of other machinability indicators, such as the cutting force components, the cutting zone temperature or vibration components [26,27,28]. In addition, a set of values describing the phenomenon (factor) studied with the use of predictive models (e.g., econometric models) gives the possibility of inferring about future development of its capabilities [29].…”

Section: Introductionmentioning

confidence: 99%

Effect of the AWJM Method on the Machined Surface Layer of AZ91D Magnesium Alloy and Simulation of Roughness Parameters Using Neural Networks

et al. 2018

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This paper investigates the effect of change of the abrasive flow rate and the jet feed on the effectiveness of machining of AZ91D casting magnesium alloy. The evaluation of the state of the workpiece surface was based on surface and area roughness parameters (2D and 3D), which provided data on: irregularities formed on the workpiece edge surface (water jet exit), the surface quality after cutting, the workpiece surface chamfering, microhardness of the machined surface, and of specimen cross-sections (along the water jet impact). The process was tested for two parameter settings: abrasive flow rate 50 at cutting speed vf = 5–140 mm/min, and abrasive flow rate 100% (0.5 kg/min) at vf = 5–180 mm/min. The results demonstrate a significant effect of the abrasive flow rate and the jet feed velocity on the quality of machined surface (surface roughness and irregularities). In addition, selected 2D surface roughness parameters were modelled using artificial neural networks (radial basis function and multi-layered perceptron). It has been shown that neural networks are a suitable tool for prediction of surface roughness parameters in abrasive water jet machining (AWJM).

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“…The method for measuring wear indicators in the case of end mill cutters is more complicated than in the case of the turning process [38]. Various designations for these indicators can be found in the literature [12,17,39].…”

Section: Tool-life Testingmentioning

confidence: 99%

Influence of machining parameters on the polymer concrete milling process

Kępczak

Zgórniak

Lajmert

et al. 2020

Int J Adv Manuf Technol

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The paper examines the wear of end mill cutters, the parameters of a workpiece's surface layer and the values of cutting forces during the machining of polymer concrete, which is one of the most difficult-to-cut materials. Increased abrasive wear of the active cutting edges is a result of the heterogeneous structure of this material, and it has a negative effect on the cutting process, the durability of the cutting tools as well as the roughness of the workpiece. It is very important to determine the machining parameters for this material from a tool durability and quality of machining point of view. The research was divided into two stages. In the first stage, the appropriate machining parameters were determined based on the measurement of the components of the cutting force. In the second stage, durability tests of four end mill cutters were carried out. During the tests, the component cutting forces, tool wear and surface roughness were measured. The highest tool durability was recorded for the monolithic cemented carbide cutter with coating NC Mill G9F42120N 4F. Tools made of high-speed steel should not be used for machining polymer concrete, as they exhibited the highest intensity of tool wear. The rapid loss of machinability of these tools leads to a rapid increase in cutting force and the roughness of the machined surface. From an economic point of view, cemented carbide end mill cutters with coatings seem to be the appropriate choice for machining polymer concrete.

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Thermographic Study of Chip Temperature in High-Speed Dry Milling Magnesium Alloys

Cited by 13 publications

References 11 publications

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Effect of the AWJM Method on the Machined Surface Layer of AZ91D Magnesium Alloy and Simulation of Roughness Parameters Using Neural Networks

Influence of machining parameters on the polymer concrete milling process

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