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KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI

Yıl 2005, Cilt: 21 Sayı: 1, 92 - 107, 01.02.2005

Öz

İşletmeler arasındaki artan rekabet, yüksek kalite standartlarını önemli bir hale getirmiştir. Bunun yanı sıra müşteri memnuniyeti de rekabetçi bir iş ortamında önemlidir. Bundan dolayı işletmeler esnek olmalıdır. Esneklik için üretim ve kalite kontrol sistemleri otomatik ve değişikliklere uyumlu olmalıdır. Otomatik bir kalite kontrol sistemi için yapay zekâ teknikleri kullanılmaktadır. Bu çalışmada yapay sinir ağlarının kalite kontrol problemlerindeki uygulamaları araştırılmıştır. Desen tanıma, tahmin, sınıflandırma gibi pek çok kalite kontrol problemi için yapay sinir ağları kullanılmaktadır. YSA yaklaşımı ile birlikte kalite kontrol faaliyetleri daha kolay olmakta, maliyetler ve muayene süreleri minimize edilebilmektedir. Çalışmada, yapay sinir ağlarının uygulamaları, avantaj ve dezavantajları incelenmiştir.

Kaynakça

  • Canpolat, F., Yılmaz, K., Ata, R., Köse, M., Prediction of Ratio of Mineral Substitution In The Production of Low-Clinker Factored Cement By Artificial Neural Network, Celal Bayar University, Mathematical and Computational Applications, 8(2), 209-217,2003.
  • Smith, A. E., X-Bar and R Control Chart Interpretation Using Neural Computing, International Journal of Production Research, 32 (2), 309-320, 1994.
  • Patro, S., Neural Networks and Evolutionary Computation for Real-time Quality Control, A Dissertation in Industrial Engineering, Texas Tech University, 1997.
  • Sağıroğlu, Ş., Beşdok, E., Erler, M., Mühendislikte Yapay Zeka Uygulamaları-I: Yapay Sinir Ağları, Ufuk Yayıncılık, Kayseri, 2003.
  • Allahverdi, N.,Yapay Sinir Ağları, Yayınlanmamış Ders Notları, Selçuk Üniversitesi, Konya, 2003
  • Karlık, B., Çok Fonksiyonlu Protezler İçin Yapay Sinir Ağları Kullanılarak Miyoelektrik Kontrol, İstanbul, 1994.
  • Haykin, S., Neural Networks: A Comprehensive Foundation, New York: MacMillan College Publishing Company, 1994.
  • Auger, M., Detection of Laser-Welding Defects Using Neural Networks, A Thesis for The Degree Of Master of Science (Engineering), Queen’s University, 2001.
  • Werbos, P. J., Generalization of Backpropagation with Application to a Recurrent Gas Market Models, Neural Networks, 1, 339-356, 1988.
  • Kohonen, T., Self-Organizing Map, Proceedings of the IEEE, 78 (9), 1464-1480, 1990.
  • Caudill, M., Neural Network Training Tips and Techniques, AI Expert, 6, (1), 56-61, 1991.
  • Rojas, R., Neural Networks- A Systematic Introduction, Springer-Verlag, 1996.
  • Guo, Y., Dooley, K. J., Identification of Change Structure in Statistical Process Control, International Journal of Production Research, 30, 1655-1669, 1992.
  • Su, C., Tong, L., A Neural Network-Based Procedure for the Process Monitoring Of Clustered Defects in Integrated Circuit Fabrication, Computers in Industry, 34, 285-294, 1997.
  • Hwarng, B. H., Detecting Mean Shift in AR (1) Processes, Decision Sciences Institute, Annual Meeting Proceedings, 2002.
  • Velasco, T., Rowe, M. R., Back Propagation Artificial Neural Networks for the Analysis of Quality Control Charts, Computers and Industrial Engineering, 25(1-4), 397- 400, 1993.
  • Pham, D. T., Öztemel, E., Control Chart Pattern Recognition Using Learning Vector Quantization Networks, International Journal of Production Research, 32(3), 721-729, 1994.
  • Pham, D. T., Öztemel, F., An Integrated Neural Network And Expert System Tool For Statistical Process Control, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering, 209 (B2), 91-97, 1995.
  • Leger, R. P., Garland, Wm. J., Poehlman, W.F.S., Fault Detection And Diagnosis Using Statistical Control Charts And Artificial Neural Networks, Artificial Intelligence in Engineering, 12(1), 35-47, 1998.
  • Reddy, D.C., Ghosh. K., Identification and Interpretation of Manufacturing Process Patterns through Neural Networks, Mathematical and Computer Modelling,27 (5), 15-36, 1998.
  • Karlık, B., A Neural Network Image Recognition for Control of Manufacturing Plant, Mathematical & Computational Applications, 8(2), 181-189, 2003.
  • Vıharos, Zs. J., Monostorı, L., Optimization of Process Chains by Artificial Neuronal Networks and Genetic Algorithms Using Quality Control Charts, 8th Daaam International Symposium, 1997.
  • Jackson, N. F., Neural Network Model Using A Genetic Algorithm To Perform The Functions of X-Bar Charts, A Dissertation for the doctor of Philosophy Degree, The University of Mississippi, 1999.
  • Cook, D. F., Shannon, R. E., A Predictive Neural Network Modeling System for Manufacturing Process Parameters, International Journal of Production Research, 30 (7), 1537-1550, 1992.
  • Hwarng, H. B., Hubele, N. F., X-bar Chart Pattern Recognition Using Neuralnets, ASQC Quality Congress Transactions, 884-889, 1991.
  • Pugh, G. A., Synthetic Neural Networks for Process Control, Computers and Industrial Engineering, 17 (1- 4), 24-26, 1989.
  • Pugh, G. A., A Comparison of Neural Networks to SPC Charts, Computers and Industrial Engineering, 21 (1-4), 253-255, 1991.
  • Smith, A. E., Yazıcı, H., an Intelligent Composite System for Statistical Process Control, Engineering. Applications Artificial Intelligence, 5 (6), 519-526, 1992.
  • Regattıerı, M., Zuben, F.J.V., Rocha, A. F., Neurofuzzy Interpolation: II-Reducing Complexity of Description, IEEE International Conference on Neural Networks, 3, 1835-1840, 1993.
  • Chınnam, R. B., Role of Neural Networks and Genetic Algorithms in Developing Intelligent Quality Controllers for On-line Parameter Design, International Journal of Smart Engineering System Design, 2000.
  • Rosales, D. J. V., Soft Computing Technologies In Quality Control With Applications To Injection Molding, A Dissertation for the Degree of Philosophy, New Mexico State University, 2001.
  • Parvathınathan, G., An Evaluation Of Uncertainty In Water Quality Modeling For The Lower Rio Grande River Using Qual2E-Uncas and Neural Networks, A Thesis for the Master of Science, Texas A&M University, 2002.
  • Huang, P. T. B., A Neural Networks-Based-In-Process Adaptive Surface Roughness Control (NN-IASRC) System In End-Milling Operations, A Dissertation For The Degree of Doctor of Philosopy, Iowa State University, 2002.
  • Kang, B. S., Choe, D. H., Park, S. C., Intelligent Process Control In Manufacturing Industry With Sequential Processes, International Journal of Production Economics, 60-61, 583-590, 1999.
  • Sette, S., Boulhart, L., Langenhove, L. V., Using Genetic Algorithms To Design A Control Strategy Of An Industrial Process, Control Engineering Practice, 6, 523-527, 1998.
  • Guo, W., Brodowsky, H., Determination of the Trace 1, 4-Dioxane, Microchemical Journal, 64, 173-179, 2000.
  • Çıftcı, A. S., A Neural Internal Model Control Scheme for an Industrial Rotary Calciner, a Dissertation for the Degree of Doctor of Philosophy, Michigan Technological University, 2000.
  • Chang, D.S., Jıang, S. T., Assessing Quality Performance Based On The On-Line Sensor Measurement Using Neural Networks, Computers & Industrial Engineering, 42, 417-424, 2002.
  • Polıgne, I., Broyart, B., Trystram, G., Collıgran, A., Prediction Of Mass-Transfer Kinetics And Product Quality Changes During A Dehydration-Impregnation-Soaking Process Using Artificial Neural Networks Application To Pork Curing, Lebensm – Wiss. U. Technology, 35, 748-756, 2002.
  • Andersen, K., Cook, G. E., Karsaı, G., Ramaswamy, K., Artificial Neural Networks Applied To Arc Welding Process Modeling And Control, IEEE Transactions on Industry Applications, 26, 824-830, 1990.
  • Zaderej, V. V., The Use Of Neural Networks To Reduce Process Variability, A Thesis For The Degree of Master of Business Administration, Quinnipiac College, 1995.
  • Shea, G., The Economic Control Of Quality, Nonlinear. Analysis. Theory Methods. &. Applications, (30), 4033-4040, 1997.
  • Stıtch, T. J., Spoerre, J. K., Velasco, T., The Application Of Artificial Neural Networks To Monitoring And Control Of An Induction Hardening Process, Journal of Industrial Technology, 1, 1999.
  • Jıahe, A., Huıju, J. G., Yaohe, H., Xıshan, X., Artificial Neural Network Prediction Of The Microstructure Of 60Si2MnA Rod Based On Its Controlled Rolling And Cooling Process Parameters, Materials Science and Engineering, A344, 318-322, 2002.
  • Yazıcı, H., Smith, A. E., A Composite System Approach For Intelligent Quality Control, Proceedings of the IIE Research Conference, 325-328, 1992.
  • Bahlmann, C., Heıdemann, G., Rıtter, H., Artificial Neural Networks For Automated Quality Control Of Textile Seams, Pattern Recognition, 32,1049-1060, 1999.
  • Kım, T. H., Cho, T. H., Moon, Y. S., Park, S. H., Visual Inspection System For The Classification Of Solder Joints, Pattern Recognition, 32, 565-575, 1998.
  • Zhou, J., Using Genetic Algorithms And Artificial Neural Networks For Multisource Geospatial Data Modeling And Classification, The University of Connecticut, 1998.
  • Chıang, T. L., Su, C. T., Optimization Of TQFP Molding Process Using Neuro-Fuzzy-GA Approach, European Journal of Operation Research, 147, 156-164, 2003.
  • Tanı, T., Murakoshı, S., Sato, T., Umano, M., Tanaka, K., Application Of Neuro-Fuzzy Hybrid Control System To Tank Level Control, IEEE Int. Conf. On Fuzzy Systems, 1, 618-623, 1993.
  • Lıu, J. N. K., Quality Prediction For Concrete Manufacturing, Automation in Construction, 5, 491-499, 1997
  • Paıva, R. V., Dourado, A., Duarte, B., Quality Prediction In Pulp Bleaching: Application Of A Neuro- Fuzzy System, Control Engineering Practice, 12(5), 587-594, 2004.
  • Park, G. H., Lee, Y. J., Leclaır, S. R., Intelligent Rate Control For MPEG-4 Coders, Engineering Applications of Artificial Intelligence, 13, 565-575, 2000.
  • Cheng, R. W., Tozawa, T., Gen, M., Kato, H., Takayama, Y., AE Behaviors Evaluation With BP Neural Network, Computers and Industrial Engineering, 31(3-4), 867-871, 1996
  • Feng, T. J., Li, X., Ji, G. R., Zheng, B., Zhang, H. Y., Wang, G. Y., Zheng, G. X., A New Laser-Scanning Sensing Technique For Underwater Engineering Inspection, Artificial Intelligence in Engineering, 10 (4), 363-368, 1996.
  • Bukkapatnam, S. T. S., Monitoring And Control Issues In Chaotic Processes: An Application To Turning Process, A Thesis in Industrial and Manufacturing Engineering, The Pennsylvania State University, 1997.
  • Thomsen, J.J., Lund, K., Quality Control Of Composite Materials By Neural Network Analysis Of Ultrasonic Power Spectra, Materials Evaluation, 49(5), 594-600, 1991.
  • Barschdorff, D., Case Studies In Adaptive Fault Diagnosis Using Neural Networks, Proc.of the IMACS Annals on Computing and Applied Mathematics MIM-S2, Brussels, pp. III.A.1/1-1/6, 1990.
  • Kang, B.-S., Park, S.-C., Integrated Machine Learning Approaches For Complementing Statistical Process Control Procedures, Decision Support Systems, 29, 59-72, 2000.
  • Beavorstock, M. C., It Takes Knowledge To Apply Neural Networks For Control, ISA Transactions, 32, 235-240, 1993.
  • Cordes, G. A., Smatt, H. B., Johnson, J. A., Design And Testing Of A Fuzzy Logic/ Neural Network Hybrid Controller For Three-Pump Liquid Level/Temperature Control, IEEE Int. Conf. On Fuzzy Systems, 1, 167-171, 1993.
  • Shoureshı, R., Intelligent Control Systems: Are They For Real? Trans. ASME, 115, 392-401, 1993.
  • Cheng, C. S., A Multi-Layer Neural Network Model For Detecting Changes In The Process Mean, Computers and Industrial Engineering, 28 (1), 51-61, 1995.
  • Sım, A., Parvın, B., Keagy, P., Invariant Representation And Hierarchical Network For Inspection Of Nuts From X-Ray Images, International Journal of Imaging Systems and Technology, 7(3), 231-237, 1996.
  • Puerto, F. D., Ghalıa, M. B., White Color Tracking Adjustment In Television Receivers Using Neural Networks, Engineering Applications of Artificial Intelligence, 15, 601-606, 2002.
  • Mezgar, I., Egresıts Cs., Monostorı, L., Design And Real-Time Reconfiguration Of Robust Manufacturing Systems By Using Design Of Experiments And Artificial Neural Networks, Computers in Industry, 33, 61- 70, 1997.
  • Grauel, A., Ludwıg, L. A., Klene, G., Comparison of Different Intelligent Methods for Process and Quality Monitoring, International Journal of Approximate Reasoning, 16, 89-117, 1996
  • Haussler, J., Wortberg, J., Neural Network-Based System Boosts Quality, Modern Plastics International, 26(12), 103-107, 1996.
  • Zavarehı, M. K., On-Line Condition Monitoring and Fault Diagnosis In Hydralic System Components Using Parameter Estimation and Pattern Classification, Department Of Mechanical Engineering, The University Of British Columbia, 1997.
  • Bridges, L. W., Mort, N., New Approaches To On-Line Quality Control For Enameled Wire Manufacture, Control Engineering Practice, 6, 1397-1403, 1998.
  • Zhang, Y.F., Nee, A. Y. C., Fuh, J. Y. H., Neo, K. S., Loy, H.K., A Neural Network Approach To Determining Optimal Inspection Sampling Size For CMM, Computer Integrated Manufacturing Systems, 9(3), 161-169, 1996.
  • Sanchez, M.S., Bertran, E., Sarabıa L. A., Ortız, M.C, Quality Control Decision With Near Infrared Data, Chenometrics and Intelligent Labarotory Systems, 53, 69-80, 2000.
  • Burke, L. I., Automated Identification of Tool Wear States in Machining Processes: An Application of Self-Organizing Neural Network. Ph.D. Thesis, University of California-Berkeley, 1989.
  • Guıllot, M., El Ouafı, A., On-line Identification Of Tool Breakage In Metal Cutting Processes By Use Of Neural Networks, In Intelligent Engineering Systems Through Artificial Neural Networks, Amer Society of Mechanical, 701-709, 1991.
  • Wu, H.-J., Cheng-Shin Liou and Hsu-Heng Pi, Fault Diagnosis Of Processing Damage In Injection Molding Via Neural Network Approach, In Intelligent Engineering Systems Through Artificial Neural Networks, Amer Society of Mechanical, 645-650, 1991.
  • Dornfeld, D. A., Unconventional Sensors and Signal Conditioning For Automatic Supervision, III. International Conf. On Automatic Supervision, Monitoring and Adaptive Control in Manufacturing, Rydzyna, Poland, 197-233, 1990.
  • Domınguez, S., Campoy, P., Aracıl, R., A Neural Network Based Quality Control System For Steel Strip Manufacturing, Annual Review in Automatic Programing, 19, 185-190, 1994.
  • Chang, C. C., Song, K. T., Ultrasonic Sensor Data Integration And Its Application To Environment Perception, Journal of Robotic Systems, 13(10), 663-677, 1996.
  • Du, T. C., Wolfe, P. M., Implementation of Fuzzy Logic Systems and Neural Networks in Industry, Computers in Industry, 32, 261-272, 1997.

USING ARTIFICIAL NEURAL NETWORKS TO SOLVE QUALITY CONTROL PROBLEMS

Yıl 2005, Cilt: 21 Sayı: 1, 92 - 107, 01.02.2005

Öz

Increasing competition between companies made high quality standards very important. Also customer satisfaction is important in a competitive business environment. Hence, companies must be flexible. Manufacturing and quality control systems must be automatically and be able to adapt to change for flexibility. Artificial intelligence techniques are used to perform an automatically control system. In this paper, artificial neural network applications for quality control problems are reviewed. Neural networks are used a number of quality control problems, such as pattern recognition, forecasting, classification. Quality control functions are became easier, and the cost of it and time for inspection is minimized by using neural network approach. In this paper, artificial neural network (ANN) applications, its advantages and disadvantages are investigated.

Kaynakça

  • Canpolat, F., Yılmaz, K., Ata, R., Köse, M., Prediction of Ratio of Mineral Substitution In The Production of Low-Clinker Factored Cement By Artificial Neural Network, Celal Bayar University, Mathematical and Computational Applications, 8(2), 209-217,2003.
  • Smith, A. E., X-Bar and R Control Chart Interpretation Using Neural Computing, International Journal of Production Research, 32 (2), 309-320, 1994.
  • Patro, S., Neural Networks and Evolutionary Computation for Real-time Quality Control, A Dissertation in Industrial Engineering, Texas Tech University, 1997.
  • Sağıroğlu, Ş., Beşdok, E., Erler, M., Mühendislikte Yapay Zeka Uygulamaları-I: Yapay Sinir Ağları, Ufuk Yayıncılık, Kayseri, 2003.
  • Allahverdi, N.,Yapay Sinir Ağları, Yayınlanmamış Ders Notları, Selçuk Üniversitesi, Konya, 2003
  • Karlık, B., Çok Fonksiyonlu Protezler İçin Yapay Sinir Ağları Kullanılarak Miyoelektrik Kontrol, İstanbul, 1994.
  • Haykin, S., Neural Networks: A Comprehensive Foundation, New York: MacMillan College Publishing Company, 1994.
  • Auger, M., Detection of Laser-Welding Defects Using Neural Networks, A Thesis for The Degree Of Master of Science (Engineering), Queen’s University, 2001.
  • Werbos, P. J., Generalization of Backpropagation with Application to a Recurrent Gas Market Models, Neural Networks, 1, 339-356, 1988.
  • Kohonen, T., Self-Organizing Map, Proceedings of the IEEE, 78 (9), 1464-1480, 1990.
  • Caudill, M., Neural Network Training Tips and Techniques, AI Expert, 6, (1), 56-61, 1991.
  • Rojas, R., Neural Networks- A Systematic Introduction, Springer-Verlag, 1996.
  • Guo, Y., Dooley, K. J., Identification of Change Structure in Statistical Process Control, International Journal of Production Research, 30, 1655-1669, 1992.
  • Su, C., Tong, L., A Neural Network-Based Procedure for the Process Monitoring Of Clustered Defects in Integrated Circuit Fabrication, Computers in Industry, 34, 285-294, 1997.
  • Hwarng, B. H., Detecting Mean Shift in AR (1) Processes, Decision Sciences Institute, Annual Meeting Proceedings, 2002.
  • Velasco, T., Rowe, M. R., Back Propagation Artificial Neural Networks for the Analysis of Quality Control Charts, Computers and Industrial Engineering, 25(1-4), 397- 400, 1993.
  • Pham, D. T., Öztemel, E., Control Chart Pattern Recognition Using Learning Vector Quantization Networks, International Journal of Production Research, 32(3), 721-729, 1994.
  • Pham, D. T., Öztemel, F., An Integrated Neural Network And Expert System Tool For Statistical Process Control, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering, 209 (B2), 91-97, 1995.
  • Leger, R. P., Garland, Wm. J., Poehlman, W.F.S., Fault Detection And Diagnosis Using Statistical Control Charts And Artificial Neural Networks, Artificial Intelligence in Engineering, 12(1), 35-47, 1998.
  • Reddy, D.C., Ghosh. K., Identification and Interpretation of Manufacturing Process Patterns through Neural Networks, Mathematical and Computer Modelling,27 (5), 15-36, 1998.
  • Karlık, B., A Neural Network Image Recognition for Control of Manufacturing Plant, Mathematical & Computational Applications, 8(2), 181-189, 2003.
  • Vıharos, Zs. J., Monostorı, L., Optimization of Process Chains by Artificial Neuronal Networks and Genetic Algorithms Using Quality Control Charts, 8th Daaam International Symposium, 1997.
  • Jackson, N. F., Neural Network Model Using A Genetic Algorithm To Perform The Functions of X-Bar Charts, A Dissertation for the doctor of Philosophy Degree, The University of Mississippi, 1999.
  • Cook, D. F., Shannon, R. E., A Predictive Neural Network Modeling System for Manufacturing Process Parameters, International Journal of Production Research, 30 (7), 1537-1550, 1992.
  • Hwarng, H. B., Hubele, N. F., X-bar Chart Pattern Recognition Using Neuralnets, ASQC Quality Congress Transactions, 884-889, 1991.
  • Pugh, G. A., Synthetic Neural Networks for Process Control, Computers and Industrial Engineering, 17 (1- 4), 24-26, 1989.
  • Pugh, G. A., A Comparison of Neural Networks to SPC Charts, Computers and Industrial Engineering, 21 (1-4), 253-255, 1991.
  • Smith, A. E., Yazıcı, H., an Intelligent Composite System for Statistical Process Control, Engineering. Applications Artificial Intelligence, 5 (6), 519-526, 1992.
  • Regattıerı, M., Zuben, F.J.V., Rocha, A. F., Neurofuzzy Interpolation: II-Reducing Complexity of Description, IEEE International Conference on Neural Networks, 3, 1835-1840, 1993.
  • Chınnam, R. B., Role of Neural Networks and Genetic Algorithms in Developing Intelligent Quality Controllers for On-line Parameter Design, International Journal of Smart Engineering System Design, 2000.
  • Rosales, D. J. V., Soft Computing Technologies In Quality Control With Applications To Injection Molding, A Dissertation for the Degree of Philosophy, New Mexico State University, 2001.
  • Parvathınathan, G., An Evaluation Of Uncertainty In Water Quality Modeling For The Lower Rio Grande River Using Qual2E-Uncas and Neural Networks, A Thesis for the Master of Science, Texas A&M University, 2002.
  • Huang, P. T. B., A Neural Networks-Based-In-Process Adaptive Surface Roughness Control (NN-IASRC) System In End-Milling Operations, A Dissertation For The Degree of Doctor of Philosopy, Iowa State University, 2002.
  • Kang, B. S., Choe, D. H., Park, S. C., Intelligent Process Control In Manufacturing Industry With Sequential Processes, International Journal of Production Economics, 60-61, 583-590, 1999.
  • Sette, S., Boulhart, L., Langenhove, L. V., Using Genetic Algorithms To Design A Control Strategy Of An Industrial Process, Control Engineering Practice, 6, 523-527, 1998.
  • Guo, W., Brodowsky, H., Determination of the Trace 1, 4-Dioxane, Microchemical Journal, 64, 173-179, 2000.
  • Çıftcı, A. S., A Neural Internal Model Control Scheme for an Industrial Rotary Calciner, a Dissertation for the Degree of Doctor of Philosophy, Michigan Technological University, 2000.
  • Chang, D.S., Jıang, S. T., Assessing Quality Performance Based On The On-Line Sensor Measurement Using Neural Networks, Computers & Industrial Engineering, 42, 417-424, 2002.
  • Polıgne, I., Broyart, B., Trystram, G., Collıgran, A., Prediction Of Mass-Transfer Kinetics And Product Quality Changes During A Dehydration-Impregnation-Soaking Process Using Artificial Neural Networks Application To Pork Curing, Lebensm – Wiss. U. Technology, 35, 748-756, 2002.
  • Andersen, K., Cook, G. E., Karsaı, G., Ramaswamy, K., Artificial Neural Networks Applied To Arc Welding Process Modeling And Control, IEEE Transactions on Industry Applications, 26, 824-830, 1990.
  • Zaderej, V. V., The Use Of Neural Networks To Reduce Process Variability, A Thesis For The Degree of Master of Business Administration, Quinnipiac College, 1995.
  • Shea, G., The Economic Control Of Quality, Nonlinear. Analysis. Theory Methods. &. Applications, (30), 4033-4040, 1997.
  • Stıtch, T. J., Spoerre, J. K., Velasco, T., The Application Of Artificial Neural Networks To Monitoring And Control Of An Induction Hardening Process, Journal of Industrial Technology, 1, 1999.
  • Jıahe, A., Huıju, J. G., Yaohe, H., Xıshan, X., Artificial Neural Network Prediction Of The Microstructure Of 60Si2MnA Rod Based On Its Controlled Rolling And Cooling Process Parameters, Materials Science and Engineering, A344, 318-322, 2002.
  • Yazıcı, H., Smith, A. E., A Composite System Approach For Intelligent Quality Control, Proceedings of the IIE Research Conference, 325-328, 1992.
  • Bahlmann, C., Heıdemann, G., Rıtter, H., Artificial Neural Networks For Automated Quality Control Of Textile Seams, Pattern Recognition, 32,1049-1060, 1999.
  • Kım, T. H., Cho, T. H., Moon, Y. S., Park, S. H., Visual Inspection System For The Classification Of Solder Joints, Pattern Recognition, 32, 565-575, 1998.
  • Zhou, J., Using Genetic Algorithms And Artificial Neural Networks For Multisource Geospatial Data Modeling And Classification, The University of Connecticut, 1998.
  • Chıang, T. L., Su, C. T., Optimization Of TQFP Molding Process Using Neuro-Fuzzy-GA Approach, European Journal of Operation Research, 147, 156-164, 2003.
  • Tanı, T., Murakoshı, S., Sato, T., Umano, M., Tanaka, K., Application Of Neuro-Fuzzy Hybrid Control System To Tank Level Control, IEEE Int. Conf. On Fuzzy Systems, 1, 618-623, 1993.
  • Lıu, J. N. K., Quality Prediction For Concrete Manufacturing, Automation in Construction, 5, 491-499, 1997
  • Paıva, R. V., Dourado, A., Duarte, B., Quality Prediction In Pulp Bleaching: Application Of A Neuro- Fuzzy System, Control Engineering Practice, 12(5), 587-594, 2004.
  • Park, G. H., Lee, Y. J., Leclaır, S. R., Intelligent Rate Control For MPEG-4 Coders, Engineering Applications of Artificial Intelligence, 13, 565-575, 2000.
  • Cheng, R. W., Tozawa, T., Gen, M., Kato, H., Takayama, Y., AE Behaviors Evaluation With BP Neural Network, Computers and Industrial Engineering, 31(3-4), 867-871, 1996
  • Feng, T. J., Li, X., Ji, G. R., Zheng, B., Zhang, H. Y., Wang, G. Y., Zheng, G. X., A New Laser-Scanning Sensing Technique For Underwater Engineering Inspection, Artificial Intelligence in Engineering, 10 (4), 363-368, 1996.
  • Bukkapatnam, S. T. S., Monitoring And Control Issues In Chaotic Processes: An Application To Turning Process, A Thesis in Industrial and Manufacturing Engineering, The Pennsylvania State University, 1997.
  • Thomsen, J.J., Lund, K., Quality Control Of Composite Materials By Neural Network Analysis Of Ultrasonic Power Spectra, Materials Evaluation, 49(5), 594-600, 1991.
  • Barschdorff, D., Case Studies In Adaptive Fault Diagnosis Using Neural Networks, Proc.of the IMACS Annals on Computing and Applied Mathematics MIM-S2, Brussels, pp. III.A.1/1-1/6, 1990.
  • Kang, B.-S., Park, S.-C., Integrated Machine Learning Approaches For Complementing Statistical Process Control Procedures, Decision Support Systems, 29, 59-72, 2000.
  • Beavorstock, M. C., It Takes Knowledge To Apply Neural Networks For Control, ISA Transactions, 32, 235-240, 1993.
  • Cordes, G. A., Smatt, H. B., Johnson, J. A., Design And Testing Of A Fuzzy Logic/ Neural Network Hybrid Controller For Three-Pump Liquid Level/Temperature Control, IEEE Int. Conf. On Fuzzy Systems, 1, 167-171, 1993.
  • Shoureshı, R., Intelligent Control Systems: Are They For Real? Trans. ASME, 115, 392-401, 1993.
  • Cheng, C. S., A Multi-Layer Neural Network Model For Detecting Changes In The Process Mean, Computers and Industrial Engineering, 28 (1), 51-61, 1995.
  • Sım, A., Parvın, B., Keagy, P., Invariant Representation And Hierarchical Network For Inspection Of Nuts From X-Ray Images, International Journal of Imaging Systems and Technology, 7(3), 231-237, 1996.
  • Puerto, F. D., Ghalıa, M. B., White Color Tracking Adjustment In Television Receivers Using Neural Networks, Engineering Applications of Artificial Intelligence, 15, 601-606, 2002.
  • Mezgar, I., Egresıts Cs., Monostorı, L., Design And Real-Time Reconfiguration Of Robust Manufacturing Systems By Using Design Of Experiments And Artificial Neural Networks, Computers in Industry, 33, 61- 70, 1997.
  • Grauel, A., Ludwıg, L. A., Klene, G., Comparison of Different Intelligent Methods for Process and Quality Monitoring, International Journal of Approximate Reasoning, 16, 89-117, 1996
  • Haussler, J., Wortberg, J., Neural Network-Based System Boosts Quality, Modern Plastics International, 26(12), 103-107, 1996.
  • Zavarehı, M. K., On-Line Condition Monitoring and Fault Diagnosis In Hydralic System Components Using Parameter Estimation and Pattern Classification, Department Of Mechanical Engineering, The University Of British Columbia, 1997.
  • Bridges, L. W., Mort, N., New Approaches To On-Line Quality Control For Enameled Wire Manufacture, Control Engineering Practice, 6, 1397-1403, 1998.
  • Zhang, Y.F., Nee, A. Y. C., Fuh, J. Y. H., Neo, K. S., Loy, H.K., A Neural Network Approach To Determining Optimal Inspection Sampling Size For CMM, Computer Integrated Manufacturing Systems, 9(3), 161-169, 1996.
  • Sanchez, M.S., Bertran, E., Sarabıa L. A., Ortız, M.C, Quality Control Decision With Near Infrared Data, Chenometrics and Intelligent Labarotory Systems, 53, 69-80, 2000.
  • Burke, L. I., Automated Identification of Tool Wear States in Machining Processes: An Application of Self-Organizing Neural Network. Ph.D. Thesis, University of California-Berkeley, 1989.
  • Guıllot, M., El Ouafı, A., On-line Identification Of Tool Breakage In Metal Cutting Processes By Use Of Neural Networks, In Intelligent Engineering Systems Through Artificial Neural Networks, Amer Society of Mechanical, 701-709, 1991.
  • Wu, H.-J., Cheng-Shin Liou and Hsu-Heng Pi, Fault Diagnosis Of Processing Damage In Injection Molding Via Neural Network Approach, In Intelligent Engineering Systems Through Artificial Neural Networks, Amer Society of Mechanical, 645-650, 1991.
  • Dornfeld, D. A., Unconventional Sensors and Signal Conditioning For Automatic Supervision, III. International Conf. On Automatic Supervision, Monitoring and Adaptive Control in Manufacturing, Rydzyna, Poland, 197-233, 1990.
  • Domınguez, S., Campoy, P., Aracıl, R., A Neural Network Based Quality Control System For Steel Strip Manufacturing, Annual Review in Automatic Programing, 19, 185-190, 1994.
  • Chang, C. C., Song, K. T., Ultrasonic Sensor Data Integration And Its Application To Environment Perception, Journal of Robotic Systems, 13(10), 663-677, 1996.
  • Du, T. C., Wolfe, P. M., Implementation of Fuzzy Logic Systems and Neural Networks in Industry, Computers in Industry, 32, 261-272, 1997.
Toplam 79 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA83FE94NC
Bölüm Makale
Yazarlar

İhsan Kaya Bu kişi benim

Selda Oktay Bu kişi benim

Orhan Engin

Yayımlanma Tarihi 1 Şubat 2005
Yayımlandığı Sayı Yıl 2005 Cilt: 21 Sayı: 1

Kaynak Göster

APA Kaya, İ., Oktay, S., & Engin, O. (2005). KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 21(1), 92-107.
AMA Kaya İ, Oktay S, Engin O. KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. Şubat 2005;21(1):92-107.
Chicago Kaya, İhsan, Selda Oktay, ve Orhan Engin. “KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 21, sy. 1 (Şubat 2005): 92-107.
EndNote Kaya İ, Oktay S, Engin O (01 Şubat 2005) KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 21 1 92–107.
IEEE İ. Kaya, S. Oktay, ve O. Engin, “KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 21, sy. 1, ss. 92–107, 2005.
ISNAD Kaya, İhsan vd. “KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 21/1 (Şubat 2005), 92-107.
JAMA Kaya İ, Oktay S, Engin O. KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2005;21:92–107.
MLA Kaya, İhsan vd. “KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 21, sy. 1, 2005, ss. 92-107.
Vancouver Kaya İ, Oktay S, Engin O. KALİTE KONTROL PROBLEMLERİNİN ÇÖZÜMÜNDE YAPAY SİNİR AĞLARININ KULLANIMI. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2005;21(1):92-107.

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