An Adaptive Support Vector Machine-Based Workpiece Surface Classification System Using High-Definition Metrology

Du, Shichang; Huang, Delin; Wang, Hui

doi:10.1109/tim.2015.2418684

Cited by 49 publications

(12 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different levels of noise have been added in the testing signals to check the robustness of the proposed algorithm. The performance of the SR‐ELM classifier has been compared with the multiclass SVM, adaptive SVM, and PNN.…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

Saini

Beniwal

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

The proposed work accomplishes detection and classification of simple and complex power quality disturbances occurring in the realm of wind-grid integrated system using fast time-time transform and small residual-extreme learning machine. Taking the advantage of time-time transform, this work has further tailored time-time transform by accommodating the dyadic scaling to make it a computationally less complex and faster technique. Here, threephase power quality signals are first segmented into single phases to be analyzed with fast time-time transform for getting the event information in timetime domain. The event information is further passed through two-stage feature extraction process to bring out twenty two characteristic curves; each of them is further used to compute five statistical features to produce 107 features after ignoring three redundant features. Thus, a high-dimensional feature set of size 321 is obtained corresponding to three phases of voltage signal. Because, high-dimensional feature set demands a robust classifier that can accurately classify the disturbances even in the presence of noise, small residual-extreme learning machine has been trained to classify the power quality signal into 12 classes (symmetrical sag, asymmetrical sag, swell, unbalance, harmonics, notch, momentary interruption, normal voltage signal, sag with harmonics, swell with harmonics, sag and notch with harmonics, and swell and notch with harmonics). This simulation-based study has been proved effective in dealing with noisy power quality signals also with reduced computational complexity and higher classification rate. KEYWORDS fast time-time transform, power quality, small residual-extreme learning machine classifier, wind-grid integrated system List of symbols and abbreviations: h(t), a time signal; w(t), Gaussian window; σ, standard deviation; f, frequency; β, output weight of SR-ELM network; G, output of hidden layer of SR-ELM network; X, input to SR-ELM; L, number of neurons in hidden layer of SR-ELM; M, number of neurons in output layer of SR-ELM; e, error; C. B.

show abstract

Section: Introductionmentioning

confidence: 99%

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

Saini

Beniwal

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Feature recognition is to extract and recognize form features of a three-dimensional (3D) computer-aided design (CAD) part model. The feature recognition results are helpful to product designing, 1–3 process planning, 4–10 and numerical control (NC) programming. 11–17 Thus, it is regarded as the premier technic for the integrated representation of product lifecycle data.…”

Section: Introductionmentioning

confidence: 99%

An oriented feature extraction and recognition approach for concave-convex mixed interacting features in cast-then-machined parts

Wang

Zhang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Feature recognition is an important technology of computer-aided design/computer-aided engineering/computer-aided process planning/computer-aided manufacturing integration in cast-then-machined part manufacturing. Graph-based approach is one of the most popular feature recognition methods; however, it cannot still solve concave-convex mixed interacting feature recognition problem, which is a common problem in feature recognition of cast-then-machined parts. In this study, an oriented feature extraction and recognition approach is proposed for concave-convex mixed interacting features. The method first extracts predefined features directionally according to the rules generated from attributed adjacency graphs–based feature library and peels off them from part model layer by layer. Sub-features in an interacting feature are associated via hints and organized as a feature tree. The time cost is reduced to less than [Formula: see text] by eliminating subgraph isomorphism and matching operations. Oriented feature extraction and recognition approach recognizes non-freeform-surface features directionally regardless of the part structure. Hence, its application scope can be extended to multiple kinds of non-freeform-surface parts by customizing. Based on our findings, implementations on prismatic, plate, fork, axlebox, linkage, and cast-then-machined parts prove that the proposed approach is applicable on non-freeform-surface parts and effectively recognize concave-convex mixed interacting feature in various mechanical parts.

show abstract

“…Several researches about online controlling flat surface variation based on HDM have been conducted. Du et al [2][3][4] proposed a shearletbased method and support vector machine-based methods to separate and extract different surface components using HDM. Du and Fei [5] also presented a co-Kriging method based on multivariate spatial statistics to estimate surface form error using HDM.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Approach for Online Minimizing Volume Difference of Multiple Chambers in Machining Processes Based on High-Definition Metrology

Huang

et al. 2017

Journal of Manufacturing Science and Engineering

Self Cite

View full text Add to dashboard Cite

The volume variation of multiple chambers of a workpiece is one of the most important factors that can directly influence the performance of the final product. This paper presents a novel systematic approach for online minimizing the volume difference of multiple chambers of a workpiece based on high-definition metrology (HDM). First, the datum of high-density points is transformed by a random sample consensus (RANSAC) algorithm due to its good robustness in fitting. Second, a procedure containing reconstruction of interior curved surfaces of chambers, boundary extraction, and projection is developed to calculate the accurate volumes of the multiple chambers. Third, a model for obtaining an optimized machining parameter for depth of chambers is explored to minimize the volume difference of any two ones of all the chambers. The model is formulated as a multi-objective optimization (MOO) problem, and a new procedure of multi-objective particle swarm optimization (MOPSO) algorithm is developed to solve this problem. Finally, a milling depth is output as the optimal milling parameter for controlling the volume variation of multiple chambers. The results of a case study show that the proposed approach can minimize the volume difference of four combustion chambers of a cylinder head and it can be well applied online in volume variation control of multiple chambers in machining processes.

show abstract

An Adaptive Support Vector Machine-Based Workpiece Surface Classification System Using High-Definition Metrology

Cited by 49 publications

References 51 publications

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

An oriented feature extraction and recognition approach for concave-convex mixed interacting features in cast-then-machined parts

A Systematic Approach for Online Minimizing Volume Difference of Multiple Chambers in Machining Processes Based on High-Definition Metrology

Contact Info

Product

Resources

About