The exhaustive quality control is becoming very important in the world́s globalised market. One example where quality control becomes critical is the percussion cap mass production, an element assembled in firearm ammunition. These elements must achieve a minimum tolerance deviation in their fabrication. This study outlines a machine vision system development using a three-dimensional camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high-speed movement for scanning the pieces, and mechanical errors and irregularities in percussion cap placement. Owing to these problems, it is impossible to solve the problem using traditional image processing methods, and hence, machine-learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.
Inspec keywords: automatic optical inspection; computer vision; quality control; weapons; mass production; military equipment; cameras; learning (artificial intelligence); control engineering computing
Other keywords:
Subjects: Optical, image and video signal processing; Manufacturing systems; Image sensors; Industrial applications of IT; Control engineering computing; Military circuits, components, and equipment; Defence industry; Inspection and quality control; Inspection and quality control; Production engineering computing; Computer vision and image processing techniques; Control technology and theory (production); Knowledge engineering techniques; Control applications in other manufacturing processes; Military control systems
References
-
-
1)
- M. Minsky . Steps toward artificial intelligence. Proc. IRE , 1 , 8 - 30
-
2)
- J. Pearl . (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference.
-
3)
- Boehnke, K.E.: `Hierarchical object localization for robotic bin picking', 2008, PhD, Faculty of Electronics and Telecommunications. Politehnica University of Timisoara, Romania.
-
4)
- T.H. Hou , M.D. Pern . A computer vision-based shape-classification system using image projection and a neural network. Int. J. Adv. Manuf. Technol. , 843 - 850
-
5)
- P. Domingos , M. Pazzani . On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. , 103 - 137
-
6)
- O. Pourret , P. Naim , B. Marcot . (2008) Bayesian networks: a practical guide to applications.
-
7)
- Tellaeche, A.: `Técnicas inteligentes basadas en percepción visual aplicadas a la agricultura de precisión', 2008, PhD, Universidad Nacional de Educación a Distancia, Madrid, España.
-
8)
- J. Leopold , H. Gunther , R. Leopold . New developments in fast 3D-surface quality control. Measurement , 2 , 179 - 187
-
9)
- E.N. Malamas , E. Petrakis , M. Zervakis , L. Petit , J.D. Legat . A survey on industrial vision systems, applications and tools. Image Vis. Comput. , 2 , 171 - 188
-
10)
- F. Bosche . Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Elsevier J. Adv. Eng. Inform. , 1 , 107 - 118
-
11)
- R.C. Gonzales , R.E. Woods . (1993) Digital image processing.
-
12)
- Pearl, J.: `Bayesian networks: a model of self-activated memory for evidential reasoning', Proc. Seventh Conf. Cognitive Science Society, University of California, 1985, Irvine, CA, USA, p. 329–334.
-
13)
- D.M. Tsai . A machine vision approach for detecting and inspecting circular parts. Int. J. Adv. Manuf. Technol.. , 217 - 221
-
14)
- E. Castillo , J.M. Gutiérrez , A.S. Hadi . (1997) Learning bayesian networks in expert systems and probabilistic network models.
-
15)
- H.I. Shafeek , E.S. Gadelmawla , A.A. Abdel-Shafy , I.M. Elewa . Automatic inspection of gas pipeline welding defects using an expert vision system. NDT&E Int. , 4 , 301 - 307
-
16)
- G. Pajares , J.M. De la Cruz . (2007) Visión por computador: imágenes digitales y aplicaciones.
-
17)
- J.R. Quinlan . (1993) C4.5: programs for machine learning.
-
18)
- G. Shakhnarovich , T. Darrel , P. Indyk . (2005) Nearest-neighbor methods in learning and vision.
-
19)
- J.R. Quinlan . Induction of decision trees. Mach. Learn. , 1 , 81 - 106
-
20)
- A. Tellaeche , X.P. Burgos-Artizzu , G. Pajares , A. Ribeiro . A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognit. , 2 , 521 - 530
-
21)
- Q. Li , X. Yao , B. Xu . A real-time 3D scanning system for pavement distortion inspection. Meas. Sci. Technol. , 1
-
22)
- Friedman, N., Goldszmidt, M.: `Building classifiers using Bayesian networks', AAAI/IAAI, 1996, Portland, Oregon, 2, p. 1277–1284.
-
23)
- C.M. Bishop . (2006) Pattern recognition and machine learning.
-
24)
- B.V. Dasarathy . (1991) Nearest neighbor (NN) norms: NN pattern classification techniques.
-
25)
- R.O. Duda , P.E. Hart , D.G. Stork . Pattern classification.
-
26)
- T.M. Cover , P.E. Hart . Nearest neighbor pattern classification. IEEE Trans. Inf. Theory , 1 , 21 - 27
-
27)
- Rish, I.: `An empirical study of the Naïve Bayes classifier', In IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001.
-
28)
- E. Alpaydın . (2004) Introduction to machine learning (adaptive computation and machine learning).
-
29)
- N.R. Draper . (1966) Applied regression analysis.
-
30)
- J. Kittler . On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. , 3 , 226 - 239
-
31)
- Ibarguren, A.: `Arquitectura de dos capas para el reconocimiento del lenguaje de signos: Gestos y Movimiento', 2008, PhD, Universidad del Pais Vasco, San Sebastián, España.
-
32)
- F.V. Jensen , T.D. Nielsen . (2007) Bayesian networks and decision graphs.
-
33)
- L.I. Kuncheva . (2004) Combining pattern classifiers: methods and algorithms.
-
34)
- A. Picon , M.A. Bereciartua , J.A. Gutierrez , J. Perez . Machine vision in quality control. Development of 3D robotizad laser-scanner. DYNA , 9 , 733 - 742
-
1)
Related content