Concerning automated assembly: knowledge-based issues and a fuzzy system for assembly under uncertainty

Tzafestas, Spyros G.; Stamou, Giorgos

doi:10.1016/s0951-5240(97)00007-4

Cited by 8 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhou and DiCesare (1989), proposed four argumentation methods of process control logic code with error recovery codes: input conditioning, alternate path, feedback error recovery and feed-forward error recovery. Fuzzy reasoning was also used in conjunction with the Fuzzy Petri-Nets (Cao and Sanderson, 1992;Jing et al, 1996) or with expert systems (Kang and Wenhan, 1993;Tzafestas and Stamou, 1997) to provide probabilistic reasoning on the error diagnostics.…”

Section: Previous Workmentioning

confidence: 99%

Off-line error prediction, diagnosis and recovery using virtual assembly systems

Baydar

2004

Journal of Intelligent Manufacturing

View full text Add to dashboard Cite

Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is dif®cult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focussing on on-line diagnosing and recovery of the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are de®cient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3-D model of the assembly line to predict the possible errors in an off-line manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms. Several case studies are performed on single-station and multi-station assembly systems and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly downtimes of robotic assembly systems will be reduced.

show abstract

Section: Previous Workmentioning

confidence: 99%

Off-line error prediction, diagnosis and recovery using virtual assembly systems

Baydar

2004

Journal of Intelligent Manufacturing

View full text Add to dashboard Cite

show abstract

“…On the other hand, data driven approaches with little modelling requirements have also been investigated. These include artificial neural networks [3], fuzzy logic [4] and Bayesian networks [5], [6] among others. The models here do not necessarily capture precise nominal system behaviour as in model-based approaches, but rather a general understanding of how the system works.…”

Section: Introductionmentioning

confidence: 99%

A methodology for developing local smart diagnostic models using expert knowledge

Madsen

Søndberg-Jeppesen

Lohse

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

View full text Add to dashboard Cite

Abstract-This paper describes an innovative modular component-based modelling approach for diagnostics and condition-monitoring of manufacturing equipment. The approach is based on the use of object-oriented Bayesian networks, which supports a natural decomposition of a large and complex system into a set of less complex components. The methodology consists of six steps supporting the development process: Begin, Design, Implement, Test, Analyse, and Deploy. The process is iterative and the steps should be repeated until a satisfactory model has been achieved. The paper describes the details of the methodology as well as illustrates the use of the componentbased modelling approach on a linear axis used in manufacturing. This application demonstrates the power and flexibility of the approach for diagnostics and condition-monitoring and shows a significant potential of the approach for modular componentbased modelling in manufacturing and other domains.

show abstract

Intelligent Robotic Assembly and Disassembly: General Architecture and Implementation Case Studies

Tzafestas¹,

Tzafestas²

1999

Advances in Manufacturing

View full text Add to dashboard Cite

Concerning automated assembly: knowledge-based issues and a fuzzy system for assembly under uncertainty

Cited by 8 publications

References 12 publications

Off-line error prediction, diagnosis and recovery using virtual assembly systems

Off-line error prediction, diagnosis and recovery using virtual assembly systems

A methodology for developing local smart diagnostic models using expert knowledge

Intelligent Robotic Assembly and Disassembly: General Architecture and Implementation Case Studies

Contact Info

Product

Resources

About