Hae Sung Lee

Bio: Hae Sung Lee is an academic researcher from Seoul National University. The author has contributed to research in topics: Deflection (engineering) & End mill. The author has an hindex of 2, co-authored 2 publications receiving 74 citations.

The form error prediction in side wall machining considering tool deflection

Abstract: In this research, an effective method for the form error prediction in side wall machining with a flat end mill is suggested. The form error is predicted directly from the tool deflection without surface generation by cutting edge locus with time simulation. The developed model can predict the surface form error accurately about 300 times faster than the previous method. Cutting forces and tool deflection are calculated considering tool geometry, tool setting error and machine tool stiffness. The characteristics and the difference of generated surface shape in up milling and down milling are discussed. The usefulness of the presented method is verified from a set of experiments under various cutting conditions generally used in die and mold manufacturing. This study contributes to real time surface shape estimation and cutting process planning for the improvement of form accuracy.

Systematic finishing of dies and moulds

Abstract: Finishing is the final process in mold manufacturing. Although it produces the desired surface finish, it is a time-consuming process. But, there have been a few systematic methods to control the procedure. In this work, the machining characteristics of the finishing tools are studied by basic experiments. Concepts of critical surface roughness and removal volume are introduced to establish a systematic finishing process model that can find the finishing process requiring the least time. The process planned by an expert worker was compared with the process from the established model. The comparison showed that the new process from the established model took less time and generated less form error on the machined surface than the process planned by expert worker.

Milling error prediction and compensation in machining of low-rigidity parts

Abstract: The paper reports on a new integrated methodology for modelling and prediction of surface errors caused by deflection during machining of low-rigidity components. The proposed approach is based on identifying and modelling key processing characteristics that influence part deflection, predicting the workpiece deflection through an adaptive flexible theoretical force-FEA deflection model and providing an input for downstream decision making on error compensation. A new analytical flexible force model suitable for static machining error prediction of low-rigidity components is proposed. The model is based on an extended perfect plastic layer model integrated with a FE model for prediction of part deflection. At each computational step, the flexible force is calculated by taking into account the changes of the immersion angles of the engaged teeth. The material removal process at any infinitesimal segment of the milling cutter teeth is considered as oblique cutting, for which the cutting force is calculated using an orthogonal–oblique transformation. This study aims to increase the understanding of the causes of poor geometric accuracy by considering the impact of the machining forces on the deflection of thin-wall structures. The reported work is a part of an ongoing research for developing an adaptive machining planning environment for surface error modelling and prediction and selection of process and tool path parameters for rapid machining of complex low-rigidity high-accuracy parts.

An advanced FEA based force induced error compensation strategy in milling

Abstract: The study introduces a multi-level machining error compensation approach focused on force-induced errors in machining of thin-wall structures. The prediction algorithm takes into account the deflection of the part in different points of the tool path. The machining conditions are modified at each step when the cutting force and deflection achieve a local equilibrium. The machining errors are predicted using a theoretical flexible force-deflection model. The error compensation is based on optimising the tool path taking into account the predicted milling error. The error compensation scheme is simulated using NC simulation package and is experimentally verified.

Strategies for error prediction and error control in peripheral milling of thin-walled workpiece

Abstract: This paper aims at developing efficient strategies for controlling the force-induced surface dimensional errors in peripheral milling of thin-walled structures. Emphasis is put on how to select the feed per tooth and depth of cut simultaneously for tolerance specification and maximization of the feed per tooth simultaneously. Three methods are presented. The first one proceeds by optimally selecting the maximum feed per tooth without tolerance violation. The second one is to find the appropriate cutting parameters by solving a linear programming problem. To show the efficiency of the first two methods, the third one, i.e. the so-called mirror error compensation method taken from references, is also addressed for comparison. Mechanistic model for cutting force estimation, cantilever beam model for cutter deflection estimation and finite element method for workpiece deflection estimation are used in all three methods. Besides, improvements on the calculation scheme of the surface dimensional error have been made and both numerical and experimental results are adopted for verification.

Roughness and texture generation on end milled surfaces

Abstract: Plane surface generation mechanism in flat end milling is studied in this research. The bottom of a flat end mill has an end cutting edge angle that plays an important role in surface texture. Surface texture is produced by superposition of conical surfaces generated by the end cutting edge rotation. The machined surface is cut once again by the trailing cutting edge. This back cutting phenomenon is frequently observed on surfaces after finishing. Tool run-out and tool setting error including tool tilting and eccentricity between tool center and spindle rotation center are considered together with tool deflection caused by cutting forces. Tool deflection affects magnitude of back cutting and the surface form accuracy. As a result, the finished surface possesses peaks and valleys with form waviness. Surface topography parameters such as RMS deviation, skewness and kurtosis are used for evaluating the generated surface texture characteristics. Through a set of cutting tests, it is confirmed that the presented model predicts the surface texture and roughness parameters precisely including back cutting effect.