Modeling and Simulation of Milling Force in Virtual Numerical Control Milling Process

Sui, Xiu Lin; Zhang, Jia Tai; Ge, Jiang Hua; Wang, Ya Ping; Yuan, H.

doi:10.4028/www.scientific.net/kem.392-394.697

Cited by 1 publication

(1 citation statement)

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“…Many cutting force models have been developed for ball-end milling processes, especially the mechanistic models, including the work of Sui [3], Zhou [4] and Milfelner [5]. Mechanistic models try to relate the cutting forces to the chip geometry by experimentally determined cutting force coefficients.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials

Župerl

Čuš

Irgolič

2016

SV-JME

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340 INTRODUCTIONEnd milling of multi-layered metal materials is an important manufacturing function in the automotive tool making industry. The most well-known process to fabricate the multi-layered materials was developed by Sandia National Laboratories and is known as laser engineered net shaping (LENS). The company Optomec makes and sells equipment based on Sandia's LENS process. LENS uses a laser power of up to 4 kW to fuse metal powders into threedimensional structures layer by layer, guided by a CAD model. The process is enclosed in an airtight, argon environment which prevents oxidation. The closed-loop process controls ensure the geometric and mechanical integrity of the completed part [1]. Due to the inhomogeneous structure of multi-layered metal materials manufactured with the LENS process, the machining of these materials leads to undesirable effects such as tool breakage, rapid cutting tool wear, surface deterioration and shelling of the cladded layers (delayerization). All of these undesirable effects are directly connected to the cutting tool forces acting on the workpiece. Delayerization of material is tightly related to the cutting force normal to the layer deposition plane [2]. Cutting forces can be seen as a control parameter for many other phenomena involved in the milling of these materials. Therefore, there is a considerable practical interest to analyse and predict precisely the cutting forces during milling of multilayered metal materials. Knowing the cutting forces is fundamental for understanding the cutting processes, optimizing the milling operations and evaluating the presence of instabilities that could affect the effectiveness of milling processes.Many cutting force models have been developed for ball-end milling processes, especially the mechanistic models, including the work of Sui [3], Zhou [4] and Milfelner [5]. Mechanistic models try to relate the cutting forces to the chip geometry by experimentally determined cutting force coefficients. The major problem is the lack of cutting force coefficients for oblique cutting and for different tool/workpiece combinations, such as multi-layered laser based metal deposition (LBMD) materials. The coefficients are obtained by labour intensive and timeconsuming cutting experiments and adjusting of model parameters. The problem is even more complicated due to the highly nonlinear and inhomogeneous nature of multi-layer LBMD materials as compared with metals. For these reasons, the generation of specific cutting energy data for LBMD materials is more challenging. No evidence of research efforts that attempts to model the cutting forces in milling LBMD materials has been found. Additionally, the obtained models are also difficult to extend to different tooling systems, conditions, and parameters.An artificial neural network (ANN) can be used as an alternative to analytical approaches. The method has become widespread in the predictive modelling of milling processes [6] to [8]. ANNs determine an implicit relationship between the input(s) and out...

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Section: Introductionmentioning

confidence: 99%