Side-Milling-Force Model Considering Tool Runout and Workpiece Deformation

Xie, Miao; Yu, Xinli; Bao, Wei; Liu, Changfu; Xia, Min

doi:10.3390/electronics12040968

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…In the cutting force prediction for micro-machining processes, the tool run-out phenomenon plays a primary role. Tool run-out determines the difference between the tool edge’s actual and theoretical trajectories [ 17 ]. It greatly affects the accuracy of micro-milling, in contrast to conventional milling, where the same phenomenon has neglectable effects.…”

Section: Introductionmentioning

confidence: 99%

Tool Run-Out in Micro-Milling: Development of an Analytical Model Based on Cutting Force Signal Analysis

Abeni,

Cappellini,

Seneci

et al. 2024

Micromachines

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Micro-machining is a widespread finishing process for fabricating accurate parts as biomedical devices. The continuous effort in reducing the gap between the micro- and macro-domains is connected to the transition from conventional to micro-scale machining. This process generates several undesired issues, which complicate the process’s optimization, and tool run-out is one of the most difficult phenomena to experimentally investigate. This work focuses on its analytical description; in particular, a new method to calibrate the model parameters based on cutting force signal elaboration is described. Today, run-out prevision requires time-consuming geometrical measurements, and the main aim of our innovative model is to make the analysis completely free from dimensional measurements. The procedure was tested on data extrapolated from the micro-machining of additively manufactured AlSi10Mg specimens. The strategy appears promising because it is built on a strong mathematical basis, and it may be developed in further studies.

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Section: Introductionmentioning

confidence: 99%

Tool Run-Out in Micro-Milling: Development of an Analytical Model Based on Cutting Force Signal Analysis

Abeni,

Cappellini,

Seneci

et al. 2024

Micromachines

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The Influence of Insert Mounting Errors on the Surface Roughness of 1.0503 Steel in Face Milling

Nowakowski,

Rolek,

Blasiak

et al. 2024

Materials

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This article looked at how insert mounting errors affect the cutting tool performance in the face milling of 1.0503 steel. This study was conducted for 490-050Q22-08M inserts mounted in a Sandvik Coromant 490-050Q22-08M CoroMill cutter attached to an AVIA VMC 800 vertical milling center. A 3D geometrical model of the cutter was developed to determine the engagement of the particular inserts in the material removal process at different feeds per tooth. The test results showed that, at feeds ranging from 0.02 mm/tooth to 0.06 mm/tooth, only three out of five inserts took part in the face milling process, while at feeds higher than 0.12 mm/tooth, all the inserts mounted in the cutter body were engaged. The relative displacements in the tool-workpiece system were measured along the axis of rotation of the tool using a Renishaw XL-80 laser interferometer. The vibration signals recorded during cutting confirmed that there was a clear relationship between the number of inserts engaged in the process and the root mean square, the arithmetic mean, and the DC component. Multiple 2D scans of the face milled surface to measure parameters Ra and Rt helped determine the feed range where the cutting process was stable. The conducted studies allowed for the identification of optimal operating ranges for a tool with parameterized errors in the mounting of inserts within the tool body. The influence of these mounting errors, in correlation with the feed per tooth, on the surface roughness of 1.0503 steel was presented and compared with five other materials.

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A Review of Proposed Models for Cutting Force Prediction in Milling Parts with Low Rigidity

Radu,

Schnakovszky

2024

Machines

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Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems.

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Side-Milling-Force Model Considering Tool Runout and Workpiece Deformation

Cited by 3 publications

References 32 publications

Tool Run-Out in Micro-Milling: Development of an Analytical Model Based on Cutting Force Signal Analysis

Tool Run-Out in Micro-Milling: Development of an Analytical Model Based on Cutting Force Signal Analysis

The Influence of Insert Mounting Errors on the Surface Roughness of 1.0503 Steel in Face Milling

A Review of Proposed Models for Cutting Force Prediction in Milling Parts with Low Rigidity

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