Characterization of Cutting Force Induced Surface Shape Variation in Face Milling Using High-Definition Metrology1

Abstract

igh-definition metrology (HDM) systems withflne lateral resolution are capable of capturing the surface shape on a machined part that is beyond the capability of measurement systems employed in manufacturing plants today. Such surface shapes can precisely reflect the impact of cutting processes on surface quality. Understanding the cutting processes and the resultant surface shape is vital to high-precision machining process monitoring and control. This paper presents modeling and experiments of a face milling process to extract surface patterns from measured HDM data and correlate these patterns with cutting force variation. A relationship is established between the instantaneous cutting forces and the observed dominant surface patterns along the feed and circumferential directions for face milling. Potential applications of this relationship in process monitoring, diagnosis, and control are also discussed for face milling. Finally a systematic methodology for characterizing cutting force induced surface variations for a generic machining process is presented by integrating cutting force modeling and HDM measurements.

Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling

“…First, model f ðU l ðsÞ; b l Þ is employed to capture the relationship between the process inputs and the surface height in face milling processes. It is reported that the surface height variation in a face milling process is strongly influenced by a number of engineering factors [13,20,21], including (1) the product/surface design that characterizes the design patterns of a surface, e.g., size, shape, and spatial distribution of holes and slots, (2) physical attributes of part materials, such as the defects and heterogeneous physical attributes caused by manufacturing flaws from suppliers, (3) manufacturing process conditions, such as feed rate, spindle tilt, spindle speed, depth of cut, cutter path, and clamping force, and (4) multistage interdependence that characterizes the effects of downstream stages on the surface shapes created in the upstream stages. For details, please refer to Ref.…”

“…The functional form of f ðÁÞ should be determined based on a thorough understanding of the process physics. For instance, in a face milling process, the global trend can be approximated by a linear term [20,13], i.e., f ðUðsÞ; bÞ ¼ ½I; MRRðsÞb, where I is a column vector of ones, MRR(s) is a column vector and represents the material removal rate (MRR) at location s, and b ¼ ½b 0 ; b 1 T is a 2  1 vector. This linear representation reveals the fact that higher axial cutting force variations due to MMR variation may result in large displacement between the surface and the cutter, causing significant surface height variations.…”

“…It should be noted that the current multitask learning for Gaussian processes is data-driven and does not incorporate the engineering insight into the model prediction. For example, our prior research has uncovered a positive correlation between surface height and effective material removal rate [20,21] through cutting force dynamics and spatial data modeling. Incorporation of the cutting force variation induced surface modeling can help to improve surface shape prediction.…”

Possibilities of the Generation of Hardened Steel Parts With Defined Topographic Characteristics of the Machined Surfaces

“…The main advantages of hard machining over grinding are high flexibility, possible complete machining, less ecological burden, and higher productivity Material Removal Rate (MRR) [2], However, its industrial potential is still limited due to unsatis factory surface integrity and the attainable dimensional and shape accuracy [3,4]. Special interest is focused on precision and highprecision hard turning (PHT) operations which demand the Rz roughness parameter to be at 2.5^4 pm and below 1 pm, respec tively [5][6][7]. Moreover, it is important to investigate the capability profiles of both these operations to the functionality of the machined surfaces [8,9].…”

Co-Kriging Method for Form Error Estimation Incorporating Condition Variable Measurements

“…Surface form is predominantly considered as one of the most important features of practical product surfaces due to its crucial influence on the functional behavior of a machined part [1][2][3][4][5][6][7][8]. The form error estimation under various machining conditions is an essential step in the assessment of part surface quality generated in machining processes [9].…”