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A framework for the self reconfiguration of automated visual inspection systems.

機(jī)譯：自動(dòng)外觀檢查系統(tǒng)的自我重新配置的框架。

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Current automated visual inspection systems lack the flexibility demanded by the modern dynamic manufacturing environments in which the introduction and retirement of products is the norm. In these environments, it is difficult to reuse or reconfigure the inspection algorithms constructed using traditional approaches because of the considerable amount of time and effort required to adapt the inspection algorithms to perform the inspection of new products. This dissertation addresses this problem in two different ways: by proposing a structured framework for the design of efficient reconfigurable automated inspection systems and by developing methodologies for the solution of specific problems derived from this framework.; The first methodology aims to speed up the feature selection process during the algorithmic reconfiguration of the automated inspection systems. The methodology is based on the traditional stepwise variable selection procedure, but instead of using the conventional discriminant metrics such as Wilks-Lambda, it uses an estimation of the marginal classification error as the figure of merit to introduce new features into a quadratic classifier. This marginal error rate is estimated by using the densities of the conditional stochastic representations of the quadratic discriminant function. The application of the proposed methodology results in significant savings of computational time in the estimation of classification error over the traditional simulation and crossvalidation methods. Thus, the proposed methodology renders significant savings of time when reusing the preexisting inspection features to inspect the new products introduced into the assembly line.; The second methodology seeks to provide proactive design recommendations about the statistical characteristics of complementary features that minimize the total classification error when using with the preexisting features. The methodology is based on the conditional distributions of the quadratic classifier. The proposed methodology determines the values of the parameters of these distributions in the solution space determined by the canonical transformation of the original populations, and also provides a method to translate these values into the original populations' parameters. From the perspective of the development of auto reconfiguration systems, the proposed feature construction method has the potential of saving tremendous amount of time by directing the search for new features to particular domains.
機(jī)譯：當(dāng)前的自動(dòng)視覺(jué)檢查系統(tǒng)缺乏現(xiàn)代動(dòng)態(tài)制造環(huán)境所要求的靈活性，在現(xiàn)代動(dòng)態(tài)制造環(huán)境中，產(chǎn)品的引入和報(bào)廢是常態(tài)。在這些環(huán)境中，難以重用或重新配置使用傳統(tǒng)方法構(gòu)造的檢查算法，因?yàn)橐箼z查算法適應(yīng)新產(chǎn)品的檢查需要大量的時(shí)間和精力。本文以兩種不同的方式解決了這個(gè)問(wèn)題：通過(guò)提出一種結(jié)構(gòu)化的框架來(lái)設(shè)計(jì)有效的可重構(gòu)自動(dòng)檢查系統(tǒng)，以及通過(guò)開(kāi)發(fā)解決從該框架衍生出的特定問(wèn)題的方法。第一種方法旨在在自動(dòng)檢查系統(tǒng)的算法重新配置過(guò)程中加快特征選擇過(guò)程。該方法基于傳統(tǒng)的逐步變量選擇程序，但是不使用傳統(tǒng)的判別指標(biāo)（例如Wilks-Lambda），而是使用邊際分類誤差的估計(jì)作為優(yōu)值，將新功能引入二次分類器。通過(guò)使用二次判別函數(shù)的條件隨機(jī)表示的密度來(lái)估算此邊際錯(cuò)誤率。與傳統(tǒng)的仿真和交叉驗(yàn)證方法相比，該方法的應(yīng)用大大節(jié)省了估計(jì)分類誤差的計(jì)算時(shí)間。因此，當(dāng)重新使用預(yù)先存在的檢查功能來(lái)檢查引入裝配線的新產(chǎn)品時(shí)，所提出的方法可節(jié)省大量時(shí)間。第二種方法旨在提供有關(guān)補(bǔ)充特征的統(tǒng)計(jì)特性的主動(dòng)設(shè)計(jì)建議，以使與現(xiàn)有特征一起使用時(shí)的總分類錯(cuò)誤最小化。該方法基于二次分類器的條件分布。所提出的方法確定了通過(guò)原始種群的規(guī)范變換確定的解空間中這些分布的參數(shù)值，并且還提供了一種將這些值轉(zhuǎn)換為原始種群參數(shù)的方法。從自動(dòng)重配置系統(tǒng)的發(fā)展的角度來(lái)看，所提出的特征構(gòu)造方法具有通過(guò)將對(duì)新特征的搜索引導(dǎo)到特定領(lǐng)域而節(jié)省大量時(shí)間的潛力。

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