Model for surface topography prediction in peripheral milling considering tool vibration

Arizmendi, M.; Campa, Francisco J.; Fernández, J.; Lacalle, Luís Norberto López de; Gil, Alain Manuel Chaple; Bilbao, Eneritz; Veiga, Fernando; Lamíkiz, Aitzol

doi:10.1016/j.cirp.2009.03.084

Cited by 108 publications

(47 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…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%

“…Adamczak et al (2009) and Miko and Nowakowski (2012) developed a generalized mathematical model of surface roughness formation for surfaces shaped with round-nose tools. Arizmendi et al (2009) presented a model for the prediction of surface topography in peripheral milling operations processing signals that capture tool vibration during the cutting process. However, tool wear is not a parameter of that model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2017

View full text Add to dashboard Cite

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

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2017

View full text Add to dashboard Cite

show abstract

“…Hence the amplitude of tool vibration will be less during the initial stage which increases slowly as the tool wear progresses and becomes very high when the tool is nearing the end of its life. In metal cutting, there exists a relation between vibration and surface roughness [36,37]. When the bouncing of the tool in and out of the work piece (tool vibration) increases, there will be high irregularities on the surface which will result in poor surface finish.…”

Section: Comparison Of Performance With That During Conventional Wet mentioning

confidence: 99%

Study on the influence of fluid application parameters on tool vibration and cutting performance during turning of hardened steel

Paul

Varadarajan²,

Gnanadurai

2016

Engineering Science and Technology, an International Journal

View full text Add to dashboard Cite

A B S T R A C TRecently the concept of hard turning has gained much attention in the metal cutting industry. In hard turning, multiple operations can be performed in single step, thereby it replaces the traditional process cycle. But it involves very large quantities of cutting fluid. Procurement, storage and disposal of cutting fluid involve expenses and environmental problem. Pure dry turning is a solution to this problem as it does not require any cutting fluid at all. But pure dry turning requires ultra hard cutting tools and extremely rigid machine tools, and also it is difficult to implement in the existing shop floor as the machine tool may not be rigid enough to support hard turning. In this context, turning with minimal fluid application is a viable alternative wherein, extremely small quantities of cutting fluid are introduced at critical contact zones as high velocity pulsing slugs, so that for all practical purposes it resembles pure dry turning and at the same time free from all the problems related to large scale use of cutting fluid as in conventional wet turning. In this study, fluid application parameters that characterize the minimal fluid application scheme were optimized and its effect on cutting performance and tool vibration was studied. From the results, it was observed that minimal fluid application in the optimized mode brought forth low vibration levels and better cutting performance.

show abstract

“…Sylvain et al [2] developed a simulation model for the prediction of machined surface patterns based on the well-known N-buffer method, and the proposed model can be coupled to a feed-rate prediction model in sculptured surface machining. Arizmend et al established a model for the topography prediction of ballend milled surfaces, considering the tool parallel axis offset, which is solved by transforming them to polynomial equations through Chebyshev expansions [3] and then present a model for the prediction of surface topography in peripheral milling operations taking into account the tool vibrates during the cutting process, which are transformed into equivalent polynomial equations and solved for discrete positions along the feed direction by applying a standard root finder [4]. Quinsat et al [5,6] studied the influence of 3D surface roughness parameter on 3D surface topography, and an analysis of the patterns obtained for various sets of machining parameters serves to highlight those that influence 3D surface topography and proposed a simulation model of material removal in five axes based on the N-buffer method and integrating the inverse kinematics transformation.…”

Section: Introductionmentioning

confidence: 99%

Modeling and on-line simulation of surface topography considering tool wear in multi-axis milling process

Zhang

et al. 2014

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The understanding and prediction of the generated surface topography are very important in milling process. This paper presents a surface topography model and an on-line simulation method of surface topography considering tool wear in a multi-axis milling process. First, the cutting edge equation is established by dividing the cutting edge into a series of cutting edge elements considering tool wear influence and tilt angle of tool axis on each discrete cutting edge element, and an algorithm is proposed to determine the range of divided position angle. Then, the sculptured surface workpiece is divided into a series of elements along the feed direction with the same or less than discrete precision of cutting edge. The intersection points between the discrete cutting edge elements and the planes of both sides of each divided element on workpiece can be obtained based on the established cutting edge equation in the cutting process. The left cutting trajectory points on workpiece are got by reserving the points with the minimum coordinate value in the Z-axis direction under the same coordinate value in the X-axis and Yaxis direction among the intersection points and points on workpiece. Thus, surface topography model can be constructed based on the established cutting edge equation, the determined range of position angle, discrete cutting edge elements, and divided element on workpiece surface. Finally, an on-line simulation algorithm is proposed to simulate the generation process of surface topography in multi-axis milling operation. Experimental work and validation of the established model are performed on a five-axis milling center by using stainless steel 0Cr17Ni12Mo2 and cemented carbides milling cutter. The results about the topography generation of the constructed B-spline surface show that the predictions of the established surface topography model correlate well with the experimental results.

show abstract

Model for surface topography prediction in peripheral milling considering tool vibration

Cited by 108 publications

References 11 publications

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

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

Study on the influence of fluid application parameters on tool vibration and cutting performance during turning of hardened steel

Modeling and on-line simulation of surface topography considering tool wear in multi-axis milling process

Contact Info

Product

Resources

About