【摘要】：目前,焊接在壓力容器生產(chǎn)制造過程中的地位越來越重要,為了確保壓力容器的質(zhì)量,實(shí)行焊接質(zhì)量的控制和管理顯得尤為關(guān)鍵。當(dāng)前計(jì)算機(jī)技術(shù)以及數(shù)據(jù)庫技術(shù)的飛速發(fā)展,現(xiàn)代制造業(yè)在生產(chǎn)過程中已經(jīng)產(chǎn)生海量數(shù)據(jù),很顯然,傳統(tǒng)的統(tǒng)計(jì)方法在此陷入了困境,已經(jīng)不能滿足當(dāng)今時(shí)代的發(fā)展需要。本文針對現(xiàn)代制造業(yè)中的海量數(shù)據(jù)無法提取知識的問題,結(jié)合某公司的實(shí)際項(xiàng)目,將數(shù)據(jù)挖掘技術(shù)應(yīng)用于制造行業(yè)中的焊接質(zhì)量控制當(dāng)中。本文在前人研究的基礎(chǔ)上,重點(diǎn)研究了壓力容器焊接質(zhì)量控制的工作,研究內(nèi)容歸納如下:首先,針對壓力容器焊接質(zhì)量不合格的現(xiàn)象,從質(zhì)量管理5M1E(操作者、機(jī)器設(shè)備、原材料、工藝方法、環(huán)境和測量)等多個(gè)方面,全過程、多方位、多角度的來分析影響壓力容器焊接質(zhì)量的因素,縮小焊接質(zhì)量異常范圍。其次,本文提出了基于特征選擇與決策樹C5.0相結(jié)合的分類方法,首先采用特征選擇算法對大量的特征進(jìn)行降維處理,將降維后的焊接數(shù)據(jù),利用決策樹C5.0算法構(gòu)建焊接分類模型,從而找出影響焊接質(zhì)量不合格的影響因素。將這些重要因素看作"質(zhì)量控制點(diǎn)"并嚴(yán)格控制。最后,將決策樹算法與神經(jīng)網(wǎng)絡(luò)算法和Logistic回歸算法進(jìn)行比較,實(shí)驗(yàn)表明,針對本文中研究的問題,決策樹方法的準(zhǔn)確性要優(yōu)于神經(jīng)網(wǎng)絡(luò)算法和Logistic回歸算法,并將決策樹分析的結(jié)果應(yīng)用于公司的實(shí)際焊接工作中,焊接問題得到了明顯的改善,其理念和指導(dǎo)思想在壓力容器制造行業(yè)中具有—定的推廣應(yīng)用價(jià)值。
[Abstract]:At present, welding is becoming more and more important in the manufacturing process of pressure vessels. In order to ensure the quality of pressure vessels, the control and management of welding quality is particularly critical. With the rapid development of computer technology and database technology, the modern manufacturing industry has produced massive data in the process of production. Obviously, the traditional statistical method is in a dilemma and can not meet the needs of the development of the times. Aiming at the problem that mass data can not extract knowledge in modern manufacturing industry, this paper applies data mining technology to welding quality control in manufacturing industry, combining with the actual project of a company. On the basis of previous studies, this paper focuses on the work of welding quality control of pressure vessels. The research contents are summarized as follows: first, aiming at the phenomenon of nonconformity of welding quality of pressure vessels, the quality management 5M1E (operator, machine equipment), Raw materials, process methods, environment and measurement, etc., the whole process, multi-direction, multi-angle to analyze the factors that affect the welding quality of pressure vessels, and reduce the abnormal range of welding quality. Secondly, this paper proposes a classification method based on feature selection and decision tree C5.0. Firstly, the feature selection algorithm is used to reduce the dimension of a large number of features. The decision tree C5.0 algorithm is used to construct the welding classification model, so as to find out the influencing factors that affect the welding quality. Regard these important factors as "quality control points" and strictly control them. Finally, the decision tree algorithm is compared with the neural network algorithm and the Logistic regression algorithm. The experimental results show that the accuracy of the decision tree algorithm is better than that of the neural network algorithm and the Logistic regression algorithm. The result of decision tree analysis is applied to the actual welding work of the company, and the welding problem is obviously improved. Its concept and guiding ideology have a definite value of popularization and application in the pressure vessel manufacturing industry.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG457.5

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