A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Muñoz‐Escalona, Patricia; Maropoulos, Paul

doi:10.1016/j.jmsy.2014.06.011

Cited by 77 publications

(34 citation statements)

References 21 publications

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“…The second phase, contact probe and beak of the cutter radially. 11,12 We measure the radial runouts of the first tooth, it is the same for the 6 teeth. The following table 1 follows: Taking the values of radial and axial runouts of the teeth of the 12 tooth milling cutter with the same way of 12 teeth.…”

Section: Tables Of Experimental Data and Simulationsmentioning

confidence: 99%

Geometric analysis of the influence of perpendicularity of a spindle axis of the milling machine on the surface quality

Bloul

Bourdim

Hamou

et al. 2017

Advances in Mechanical Engineering

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This paper deals with analyzes of the influence of the perpendicularity of the spindle of the milling machine on the machined surface. This is part of the geometric errors of machine tools and in a direct manner constitutes a defect on the quality of the workpiece. Therefore, the surface roughness is particularly sensitive to the cutting speed, the feed rate, round of teeth default, the tool tip radius and the cutter teeth number. This article examines the characteristics of the surface topography of steel parts, in finishing machining using milling cutters. The study is conducted by computer simulation tests and experimental part using surface condition monitoring instruments, taking into consideration the round teeth default. The variation of the inclination of the spindle of the milling machine in three positions (90°+ 30#, 90°, and 90°-30#) shows a good agreement between the simulation and the experimental results for sharp and moderately worn tools. Similarly, this study showed that the presented model could thus be integrated into systems computer-aided design and computer-aided manufacturing. Finally, the physical and statistical parameters of roughness during milling at position 90°confirmed that, when the defect of the perpendicularity is eliminated to the maximum, the best surface conditions are obtained.

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Section: Tables Of Experimental Data and Simulationsmentioning

confidence: 99%

Geometric analysis of the influence of perpendicularity of a spindle axis of the milling machine on the surface quality

Bloul

Bourdim

Hamou

et al. 2017

Advances in Mechanical Engineering

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“…Many researchers have studied surface roughness in face milling (Baek et al 1997;Adamczak et al 2009;Miko and Nowakowski 2012;Arizmendi et al 2009;Rosales et al 2010;Muñoz-Escalona and Maropoulos 2015;Felho et al 2015;Zhenyu et al 2015;Moghaddam and Kolahan 2016;Popov and Schindelarz 2017;Jersák and Simon 2017). Baek et al (1997) presented a mathematical model for surface roughness prediction taking into account the dynamic characteristics of the face-milling operation.…”

Section: Introductionmentioning

confidence: 99%

“…Rosales et al (2010) investigated the dependence of rotation speed, feed rate, cutting depth, tool geometry, and runout errors in face milling. Muñoz-Escalona and Maropoulos (2015) designed a geometrical model for the prediction of surface roughness in face milling with square insert tools. Felho et al (2015) presented the estimated relations between the calculated theoretical and measured real roughness values, allowing researchers to predict the machined surface roughness.…”

Section: Introductionmentioning

confidence: 99%

“…Jersák and Simon (2017) studied the influence of cooling lubricants on surface roughness and the energy efficiency of cutting machine tools for the three following techniques: lubricantfree cutting, cutting with the use of a lubricant with the MQL technique, and only utilizing finish-turning and finish-face milling. However, no account of the impact of face mill wear on surface roughness as it forms has been found in these references (Baek et al 1997;Adamczak et al 2009;Miko and Nowakowski 2012;Arizmendi et al 2009;Rosales et al 2010;Muñoz-Escalona and Maropoulos 2015;Felho et al 2015;Zhenyu et al 2015;Moghaddam and Kolahan 2016;Popov and Schindelarz 2017;Jersák and Simon 2017).…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N , in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power-that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements. Keywords

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“…However, competitiveness is a key factor in such industries, requiring accurate and extremely efficient equipment. Indeed, the available equipment on the market has continued to grow, becoming increasingly sophisticated and allowing an improved accuracy [1], answering to the need of the market for complex organic shapes and high surface quality.…”

Section: Introductionmentioning

confidence: 99%

Machining Duplex Stainless Steel: Comparative Study Regarding End Mill Coated Tools

et al. 2016

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Abstract:The difficulties in the machining of duplex stainless steel are well known. However, research on this matter is rather limited. Suppliers offer quite different cutting tools for the same raw material, with end mills of two, three or even four knives and a huge number of distinct coatings, some of them under commercial brands, making it difficult to assess the advantages they offer. Furthermore, there is a remarkable difference among the several types of duplex stainless steel available nowadays on the market. The present work intends to assess the machining performance of different tools, analyzing the behavior and wear mechanisms with two different cutting lengths, keeping constant the machining trajectory. Some other parameters were also kept constant, such as cutting speed, depth of cut and cutting width, as well as feed per tooth. The machining process was carried out under lubricated conditions, using an emulsion of 5% oil in water. Tools provided with a different number of teeth and surface coatings were tested, analyzing the wear behavior of each cutting length using scanning electron microscopy, trying to identify wear performance and how each coating contributes to increased tool life. The surfaces produced were also analyzed by means of profilometry measurements, correlating tool wear and part surface roughness. This comparative study allows determining the advantages of different tools relative to others, based on coatings and tool geometry.

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A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Cited by 77 publications

References 21 publications

Geometric analysis of the influence of perpendicularity of a spindle axis of the milling machine on the surface quality

Geometric analysis of the influence of perpendicularity of a spindle axis of the milling machine on the surface quality

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Machining Duplex Stainless Steel: Comparative Study Regarding End Mill Coated Tools

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