Manufacturing Process Support Using Artificial Intelligence

Jakub Pizoń; Jerzy Lipski

doi:10.4028/www.scientific.net/AMM.791.89

Paper Titles

Statistical Methods in the Automotive Industry
p.56

Optimal Production Planning for a Random Horizon
p.63

Mass Customized Projects Portfolio Scheduling - Imprecise Operations Time Approach
p.70

Management of Projects Realization Process in Enterprises of ICT Sector
p.81

Manufacturing Process Support Using Artificial Intelligence
p.89

GPS Technology Application as a Tool of Streamlining the Operations of a Transport Business and Economic Analysis
p.96

Economic Forecasting of the Transportation Services Using Analytic Approach
p.102

Synthesis of Decision Models and Algorithms in Manufacturing Systems
p.108

Interactive Generation of Spatial Solutions for Manufacturing Systems by Means of Virtual Reality
p.119

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 791Manufacturing Process Support Using Artificial...

Manufacturing Process Support Using Artificial Intelligence

Abstract:

The aim of this paper is to presents an interdisciplinary approach to the problem of manufacturing process support using Artificial Intelligence (AI) by introducing a cross physical-information control system concept. The scope of the article covers description of logical architecture concept, data exchange process and autonomous steering mechanism using artificial intelligent method. The article indicates that in order to successfully support manufacturing process using Artificial Intelligence proper logical support system architecture need to be applied. It is important to emphasize that the use of AI method is not enough to cover multidimensional production issues of gathering and processing data. Therefore whole system need to be organized in this way to support AI with data to be processed. Thanks to that, it is possible to meet many different goals and achieve significant results in the field of manufacturing process. Because of that in this paper the main focus is put on system logical architecture with descriptions of each element involved. Moreover, the article describes AI controller mechanism applied running on real time raw data collected from machines and products. Findings presented within paper could be use in real case scenarios as a design pattern to develop, deploy or optimize production management systems in small-medium enterprises based on low cost solutions of Internet of Things (IoT) providing data to be analyzed with use of cloud computing technology running AI algorithms.

You might also be interested in these eBooks

Theory and Practice of Industrial and Production Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 791)

Pages:

89-95

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.791.89

Citation:

Cite this paper

Online since:

September 2015

Authors:

Jakub Pizoń, Jerzy Lipski*

Keywords:

Artificial Intelligence, Genetic Algorithms, Production Engineering, Production Management

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] J. Davis, T. Edgar, J. Porter, J. Bernaden, M. Sarli, Smart manufacturing, manufacturing intelligence and demand-dynamic performance, Computers and Chemical Engineering, 47 (2012) 145-156.

DOI: 10.1016/j.compchemeng.2012.06.037

Google Scholar

[2] P. Wawrzyński, Fundamentals of artificial intelligence, first ed., Warsaw University of Technology Publishing House, Warsaw, (2014).

Google Scholar

[3] P. Mell, T. Grance, The NIST Definition of Cloud Computing, Recommendations of the National Institute of Standards and Technology, September (2011) 6.

DOI: 10.6028/nist.sp.800-145

Google Scholar

[4] T. Karpiński, Production Engineering, Publishing house WNT, Warsaw, (2007).

Google Scholar

[5] P. Arora, A. Haleem, M. Ksingh, H. Kumar, M. Kaushik, Design of a Production System Using Genetic Algorithm, Procedia Technology, 14 (2014) 390-396.

DOI: 10.1016/j.protcy.2014.08.050

Google Scholar

[6] L. Monostori, AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing, Engineering Applications of Artiﬁcial Intelligence, 16 (2003) 277–291.

DOI: 10.1016/s0952-1976(03)00078-2

Google Scholar

[7] P. Wawrzyński, Adaptive and learning systems, first ed., Warsaw University of Technology Publishing House, Warsaw, (2009).

Google Scholar

[8] S. Rajendran, Manufacturing Optimization through Intelligent Techniques (Manufacturing Engineering and Materials Processing), Taylor&Francis Group, New York (2006).

Google Scholar

[9] J. Koza, J. Yu, W. Mydlowec, F. Bennett Iii, M. Keane, Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming, Genetic Programming and Evolvable Machines, 1 (2000) 121-164.

DOI: 10.1023/a:1010076532029

Google Scholar