Novel Spatial Cutting Tool-wear Measurement System Development and its Evaluation

Čerče, Luka; Pušavec, Franci; Kopač, Janez

doi:10.1016/j.procir.2015.08.058

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…However, in our experiment, the wear area of the flank wear is distributed in a circular arc, and there is no obvious uniform wear area. Moreover, the maximum tool flank wear is often used as the criteria to define the end of effective tool life [40]. Therefore, the maximum wear width near the tool tip was measured, as shown in Figure 6b.…”

Section: Methodsmentioning

confidence: 99%

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

Cheng

Jiao

Yan

et al. 2022

Sensors

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For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-task learning (MTL) model for simultaneous estimation of surface roughness and tool wear. To address the problem, a new MTL model with shared layers and two task-specific layers was proposed. A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features from cutting force signals. To enhance the performance of the MTL model, the scaled exponential linear unit (SELU) was introduced as the activation function of SDAE. Moreover, a dynamic weight averaging (DWA) strategy was implemented to dynamically adjust the learning rate of different tasks. Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients. Frequency-domain features were extracted from the power spectrum obtained by the Fourier transform. After that, all features were combined as the input vectors of the proposed MTL model. Finally, surface roughness and tool wear were simultaneously predicted by the trained MTL model. To verify the superiority and effectiveness of the proposed MTL model, nickel-based superalloy Haynes 230 was machined under different cutting parameter combinations and tool wear levels. Some other intelligent algorithms were also implemented to predict surface roughness and tool wear. The results showed that compared with the support vector regression (SVR), kernel extreme learning machine (KELM), MTL with SDAE (MTL_SDAE), MTL with SCAE (MTL_SCAE), and single-task learning with PSAE (STL_PSAE), the estimation accuracy of surface roughness was improved by 30.82%, 16.67%, 14.06%, 26.17%, and 16.67%, respectively. Meanwhile, the prediction accuracy of tool wear was improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For practical engineering application, the dimensional deviation and surface quality of the machined parts can be controlled through the established MTL model.

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Section: Methodsmentioning

confidence: 99%

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

Cheng

Jiao

Yan

et al. 2022

Sensors

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show abstract

“…Various approaches measuring the wear of cutting tools based on additional sensors are available [7,8,9,13]. They can work on-line and are able to directly output a trustworthy and accurate measurement of wear.…”

Section: Cutting Tool Wear Measurementmentioning

confidence: 99%

An Industrial Vision System to Analyze the Wear of Cutting Tools

Gillmann

Post

Kirsch

et al. 2017

AMM

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The wear behavior of cutting tools directly affects the quality of the machined part. The measurement and evaluation of wear is a time consuming process and is subjective. Therefore, an image-based wear measurement that can be computed automatically based on given image series of cutting tools and an objective way to review the resulting wear is presented in this paper. The presented method follows the industrial vision system pipeline where images of cutting tools are used as input which are then transformed through suitable image processing methods to prepare them for the computation of a novel image based wear measurement. For multiple cutting tool settings a comparative visualization of the wear measurement outputs is presented. The effectiveness of the presented approach is shown by applying the method to measure the wear of four different cutting tool shapes.

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“…An average value of three measurements was taken. In addition, area surface roughness (3D) was performed in second analysis with 3D scanning device, developed for characterizing the surface topography [8]. Measuring system is a combination of a high -accuracy 2D laser displacement sensor Keyence LJ G015 with proper controller [9] and linear motorized translation stage Standa 8MT173 -DCE2 shown in Fig.…”

Section: Surface Characteristics Measurementmentioning

confidence: 99%

Influence of milling process on machined surface of porous polyurethane (PU) foam

et al. 2016

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Original scientific paper Nowadays polyurethane modelling foams are successfully replacing original materials in many applications due to certain comparative advantages also in copying artefacts. Milling is a fundamental machining operation frequently used in transforming products of PU foam. This experimental study focuses on surface roughness of milled PU foam. The influences of machining parameters on surface roughness in PU foam milling were assessed. Linear and area surface roughness evaluations after CNC milling are compared and estimated for the use in future research. Additionally the surface roughness values increase with a feed rate per tooth and decrease with an increase of spindle speed. The milling tests showed significant influence of modelling board density on the final surface roughness. Additional area surface roughness values have been evaluated. Keywords: milling; polyurethane modelling foam; surface roughness Utjecaj procesa glodanja na obrađenu površinu porozne poliuretanske (PU) pijeneIzvorni znanstveni članak U ovo doba poliuretanske pjene za modeliranje uspješno zamjenjuju izvorne materijale u mnogim aplikacijama zbog određenih komparativnih prednosti, tako i kod kopiranja artefakta. Glodanje je temeljna strojna operacija koja se često koristi u obradi proizvoda od PU pjene. Ova eksperimentalna studija usredotočuje se na površinsku hrapavost glodane PU pjene. Ocijenjeni su utjecaji parametara obrade na hrapavost površine u procesu glodanja PU pjene. Linearne i površinske procjene hrapavosti nakon CNC glodanja su uspoređene i procijenjene za uporabu u budućim istraživanjima. Isto tako vrijednosti hrapavosti površina povećavaju se s brzinom punjenja po zubu i smanjuju se s povećanjem brzine vretena. Testovi glodanja pokazali su značajan utjecaj gustoće PU pjena za modeliranje na konačnu hrapavost površine. Dodatno su ocijenjene vrijednosti hrapavosti površina.

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Novel Spatial Cutting Tool-wear Measurement System Development and its Evaluation

Cited by 11 publications

References 14 publications

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

An Industrial Vision System to Analyze the Wear of Cutting Tools

Influence of milling process on machined surface of porous polyurethane (PU) foam

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