Monitoring roundness profiles based on an unsupervised neural network algorithm

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The data-rich environments of industrial applications lead to large amounts of correlated quality characteristics that are monitored using Multivariate Statistical Process Control (MSPC) tools. These variables usually represent heterogeneous quantities that originate from one or multiple sensors and are acquired with different sampling parameters. In this framework, any assumptions relative to the underlying statistical distribution may not be appropriate, and conventional MSPC methods may deliver unacceptable performances. In addition, in many practical applications, the process switches from one operating mode to a different one, leading to a stream of multimode data.Various nonparametric approaches have been proposed for the design of multivariate control charts, but the monitoring of multimode processes remains a challenge for most of them. In this study, we investigate the use of distribution-free MSPC methods based on statistical learning tools. In this work, we compared the kernel distance-based control chart (K-chart) based on a one-class-classification variant of support vector machines and a fuzzy neural network method based on the adaptive resonance theory. The performances of the two methods were evaluated using both Monte Carlo simulations and real industrial data. The simulated scenarios include different types of out-of-control conditions to highlight the advantages and disadvantages of the two methods. Real data acquired during a roll grinding process provide a framework for the assessment of the practical applicability of these methods in multimode industrial applications.

Section: A Rationale For the Use Of One‐class‐classification Methodsmentioning

confidence: 99%

Section: A Quality Control Scheme Based On Fuzzy Art Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparison Study of Distribution‐Free Multivariate SPC Methods for Multimode Data

Grasso

Colosimo

Semeraro

et al. 2014

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“…Wang and Guo presented the process yield for nonlinear profiles. Recently, roundness profile monitoring such as circle and cylindrical profiles is considered by some researchers . In order to identify a signature model, linear regression is often used.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Process Yield for Circular Profiles

Wang

2013

Two indices C p (circular) and C pk (circular) based on the functional method have been proposed to measure the process capability of circular profiles. However, these two indices only provide potential capability and a lower bound on the process yield, respectively. In this paper, we develop a new yield index S pk (circular) for circular profiles. This index provides an exact measure of process yield. The asymptotic normal distribution of the estimated index is derived. The statistical inferences such as hypothesis testing, confidence interval, and lower confidence interval can be easily constructed. A simulation study is conducted to assess the performance of the proposed method. The simulation results confirm that the estimates are close to the true value and the coverage rates of the confidence intervals are greater than the 95% lower limit of the stated nominal in most cases. One real data set is used to illustrate the applicability of the proposed method.

“…Colosimo et al . used a spatial autoregressive regression to model circular profile, and Pacella and Semeraro proposed an unsupervised neural network in monitoring circular profile.…”

Section: Introductionmentioning

confidence: 99%

Functional Process Capability Indices for Circular Profile

Keshteli

Kazemzadeh

Amiri

et al. 2013

Profile is a relation between one response variable and one or more explanatory variables that represent quality of a product or performance of a process. On the other hand, process capability indices are measures to help practitioners in improving the processes to satisfy the customer's expectations. Few researches are done to account for the process capability index in the areas of profile monitoring. All of these researches are focused on process capability index in simple linear profile. In all of these methods, response variables in different levels of explanatory variable are considered, and the relationship in all range of explanatory variable is neglected. In this paper, a functional method is proposed to measure process capability index of circular profiles in all range of explanatory variable. The proposed method follows the traditional definition of process capability indices. The functional method uses reference profile, functional specification limits and functional natural tolerance limits to present a functional form of process capability indices. This functional form results in measuring the process capability in each level of explanatory variable in circular profile as well as a unique value of process capability index for circular profile. The application of the proposed method is illustrated through a real case in automotive industry.