Modeling and optimization of surface roughness in single point incremental forming process

Suresh, Kurra; Rahman, Nasih Hifzur; Regalla, Srinivasa Prakash; Gupta, Amit Kumar

doi:10.1016/j.jmrt.2015.01.003

Cited by 91 publications

(51 citation statements)

References 16 publications

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“…The process parameters are considered significance to the experiment as there is a large angled slope of the straight line. We can see that all the points are relatively close to the straight line and thus that the data follow a normal distribution [8]. …”

Section: Optimization Resultsmentioning

confidence: 58%

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Optimization of the single point incremental forming process for titanium sheets by using response surface

2016

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Abstract. The single point incremental forming process is well-known to be perfectly suited for prototyping and small series. One of its fields of applicability is the medicine area for the forming of titanium prostheses or titanium medical implants. However this process is not yet very industrialized, mainly due its geometrical inaccuracy, its not homogeneous thickness distribution... Moreover considerable forces can occur. They must be controlled in order to preserve the tooling. In this paper, a numerical approach is proposed in order to minimize the maximum force achieved during the incremental forming of titanium sheets and to maximize the minimal thickness. A surface response methodology is used to find the optimal values of two input parameters of the process, the punch diameter and the vertical step size of the tool path.

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Section: Optimization Resultsmentioning

confidence: 58%

“…Several studies were developed to optimize the incremental forming of aluminum sheets [2,8]. A little less was done for titanium sheets.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the single point incremental forming process for titanium sheets by using response surface

2016

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“…Radial networks are composed of neurons whose activation functions can be realized using the representation given in the following equation (12): (12) The functions φ(||x -c||) are called the radial basis functions. Their values change radially around the center c. RBF networks can model any non-linear function with using a single hidden layer [22].…”

Section: Network Modelsmentioning

confidence: 99%

“…Gyurova and Friedrich [8] have also predicted sliding friction and wear properties of polyphenylene sulphide composites. ANN are also used to model and optimize the surface roughness in single point incremental forming [12]. Among the widespread usage of regression analysis in tribology, it is necessary to put special emphasis on the possibility of the determination of the friction co-efficiency [20] and the determination of the wear rate for different combinations of load, grit size, and sliding distance [5,13,24].…”

Section: Introductionmentioning

confidence: 99%

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Lemu¹,

Trzepieciński²,

Fejkiel³

2017

Adv. Sci. Technol. Res. J.

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This article reports a proposed approach to a frictional resistance description in sheet metal forming processes that enables the determination of the friction coefficient value under a wide range of friction conditions, without performing timeconsuming experiments. The motivation for this proposal is the fact that there exists a considerable amount of factors that affect the friction coefficient value and as a result building analytical friction model for specified process conditions is practically impossible. In this proposed approach, a mathematical model of friction behaviour is created using multiple regression analysis and artificial neural networks. The regression analysis was performed using a subroutine in MATLAB programming code and STATISTICA Neural Networks was utilized to build an artificial neural networks model. The effect of different training strategies on the quality of neural networks was studied. As input variables for regression model and training of radial basis function networks, generalized regression neural networks and multilayer networks, the results of strip drawing friction test were utilized. Four kinds of Al-Mg alloy sheets were used as a test material.

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“…The essential concept involves deforming a flat sheet in incremental steps, typically using a cylindrical tool with a hemispherical end, following a tool path on a computer numeric controlled (CNC) machine [9]. Surface roughness is a key area of concern in ISF, as the surface finish on parts is dependent on a number of factors such as the tool feed rate, the step down distance between successive contours, lubrication, tool diameter, and wall angle [7,10,11].…”

Section: Introductionmentioning

confidence: 99%

Effect of stress relieving heat treatment on surface topography and dimensional accuracy of incrementally formed grade 1 titanium sheet parts

Behera

2016

Int J Adv Manuf Technol

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The forming of parts with an optimized surface roughness and high dimensional accuracy is important in many applications of incremental sheet forming (ISF). To realize this, the effect of stress relieving heat treatment of grade-1 Ti parts performed before and after forming on the surface finish and dimensional accuracy was studied. It was found that heat treatment at a temperature of 540°C for 2 h improves the surface finish of formed parts resulting in a surface with little or no visible tool marks. Additionally, it improves the dimensional accuracy of parts after unclamping from the rig used for forming, in particular, that of parts with shallow wall angles (typically <25°). It was also noted that post-forming heat treatment improves the surface finish of parts. The surface topography of formed parts was studied using interferometry to yield areal surface roughness parameters and subsequently using secondary electron imaging. Back-scatter electron microscopy imaging results coupled with energy-dispersive Xray (EDX) analysis showed that heat treatment prior to forming leads to tool wear as indicated by the presence of Fe in samples. Furthermore, post-forming heat treatment prevents curling up of formed parts due to compressive stresses if the formed part is trimmed.

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Modeling and optimization of surface roughness in single point incremental forming process

Cited by 91 publications

References 16 publications

Optimization of the single point incremental forming process for titanium sheets by using response surface

Optimization of the single point incremental forming process for titanium sheets by using response surface

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Effect of stress relieving heat treatment on surface topography and dimensional accuracy of incrementally formed grade 1 titanium sheet parts

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