Quality and Inspection of Machining Operations: Review of Condition Monitoring and CMM Inspection Techniques — 2000 to Present

The objective of this paper is to assess the correlation of volumetric tool wear (VTW) and wear rate o f carbide tools on the material removal rate (MRR) of titanium alloys. A pre viously developed methodology for assessing the worn tool material volume is utilized for quantifying the VTW of carbide tools when machining Ti-6Al-4V. To capture the tool response, controlled milling experiments are conducted at suitable corner points of the recommended feed-speed design space, for constant stock material removal volumes. For each case, the tool material volume worn away, as well as the corresponding volumetric wear profile evolution in terms of a set of geometric coefficients, is quantified-these are then related to the MRR. Further, the volumetric wear rate and the M-ratio (volume of stock removed to VTW) which is a measure of the cutting tool efficiency, are related to the MRR-these provide a tool-life based optimal MRR for profitability. This work not only elevates tool wear from a ID to 3D concept, but helps in assessing machining economics from a stock material-removal-ejficiency perspective as well.inadequacies were all the more pronounced in the case of tools catered to machining titanium alloys due to their typical complex geometric profiles involving high positive rake and relief angles. Consequently, a VTW quantification methodology was developed, standardized, and experimentally validated [1], This has potential to offer insights into the mechanics of wear itself from a 3D stand point, which is critical to a holistic understanding of the wear pro cess. Currently, tool wear is best tracked by a set of successive photomicrographs of the flank/rake face, at periodic instances dur ing the cut, which provide no information on how one wear profile led to another. An understanding of such progression/interactions will provide insights into the behavior and response of tool condi tion monitoring (TCM) parameters used for indirectly measuring tool wear, such as forces, vibrations, frequencies, etc., which was noted as a need in the recent ASME state-of-the-art paper on TCM [2], 2 Background 2.1 Qualitative Assessment of Tool Wear Quantifiers. CutCutting tool failure is most commonly quantified as a limiting measure of flank or crater wear or limits of surface finish, cutting force, vibration amplitude, etc. The standard measure of limiting tool life for carbide inserts is usually a uniform average flank depth (VBb) of about 0.3 mm or a localized wear depth (VBBmax) of about 0.6 mm [3], which can vary based on the application. Likewise, wear on the rake face is most commonly characterized by limiting values of crater wear depth (KT) as given by [4] KT = 0.06 + 0.3/where, / is the feed/rev. Note that this criterion was initially coined for single point turning tools with simple geometries. The ISO standards for tool life testing in milling [3] define 16 distinct tool deterioration phenomena for face milling, and 13 APRIL 2015, Vol. 137 / 021021-1

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“…where VPi and VPi (dot) are the VTW and rates at the end of each pass i (i = [1][2][3], and a, -d, ■ are curve-fit coefficients.…”

Section: F Ig 6 T H E C O R R E La Tio N O F S P E C Ific V T W Ra mentioning

confidence: 99%

Correlation of the Volumetric Tool Wear Rate of Carbide Milling Inserts With the Material Removal Rate of Ti–6Al–4V

Kuttolamadom

Mehta

Mears

2015

Journal of Manufacturing Science and Engineering

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“…An understanding of such progression/ interactions will provide insights into the behavior and response of tool condition monitoring (TCM) parameters used for indirectly measuring tool wear, such as forces, vibrations (accelerations), frequency/acoustic parameters, etc. This was noted as a need in the recent ASME state-of-the-art paper on TCM [12].…”

Section: Alternatives To Traditional Wear and Failure Assessment Of Cmentioning

confidence: 99%

On the Volumetric Assessment of Tool Wear in Machining Inserts With Complex Geometries—Part 1: Need, Methodology, and Standardization

Kuttolamadom¹,

Mears²

2012

Journal of Manufacturing Science and Engineering

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The objectives of this paper are to qualitatively assess the inadequacies of the current manner of tool wear quantification and consequently to suggest/develop a more comprehensive approach for machining tool wear characterization. Traditional parameters used for tool wear representation such as flank and crater wear are no longer self-sufficient to satisfactorily represent the advanced wear status of more recent cutting tools with complex geometric profiles. These complexities in tool geometries are all the more pronounced when catered to difficult-to-machine materials such as titanium and its alloys. Hence, alternatives to traditional tool wear assessment parameters are briefly explored and a suitable one is selected, that will help understand the very nature of the evolving wear profile itself fi'om a three dimensional standpoint. The assessment methodology is further developed and standardized and suggestions for future use and deployment are provided. The measurement system is evaluated using an analysis of variance (ANOVA) gauge repeatability and reproducibility (R&R) study as well. In Part 2 of this paper, this method is deployed for assessing tool wear in machining TÍ-6AI-4 V and concepts such as the M-ratio and its derivatives are developed to quantify the efficiency of the cutting tool during each pass.

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“…significantly influence the form error of finished surface on machined parts. Roth et al [37] thought a machining condition has a direct effect on part quality and proposed online sensor-based approaches for tool condition monitoring that provide a means to assess the underlying tool condition during the cutting process. Benardos and Vosniakos [38] reviewed the approaches that predict the surface form error and roughness under various machining conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Lan

2015

Journal of Manufacturing Science and Engineering

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The form error estimation under various machining conditions is an essential step in the assessment of product surface quality generated in machining processes. Coordinate measuring machines (CMMs) are widely used to measure complicated surface form error. However, considering measurement cost, only a few measurement points are collected offline by a CMM for a part surface. Therefore, spatial statistics is adopted to interpolate more points for more accurate form error estimation. It is of great significance to decrease the deviation between the interpolated height value and the real one. Compared to univariate spatial statistics, only concerning spatial correlation of height value, this paper presents a method based on multivariate spatial statistics, co-Kriging (CK), to estimate surface form error not only concerning spatial correlation but also concerning the influence of machining conditions. This method can reconstruct a more accurate part surface and make the estimation deviation smaller. It characterizes the spatial correlation of machining errors by variogram and cross-variogram, and it is implemented on one of the common features: flatness error. Simulated datasets as well as actual CMM data are applied to demonstrate the improvement achieved by the proposed multivariate spatial statistics method over the univariate method and other interpolation methods.

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Quality and Inspection of Machining Operations: Review of Condition Monitoring and CMM Inspection Techniques — 2000 to Present

Cited by 8 publications

References 61 publications

Correlation of the Volumetric Tool Wear Rate of Carbide Milling Inserts With the Material Removal Rate of Ti–6Al–4V

Correlation of the Volumetric Tool Wear Rate of Carbide Milling Inserts With the Material Removal Rate of Ti–6Al–4V

On the Volumetric Assessment of Tool Wear in Machining Inserts With Complex Geometries—Part 1: Need, Methodology, and Standardization

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

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