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

Baydar, Cem M.

doi:10.1109/robot.2001.932651

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…[1][2][3][4] However, systematic methods of error recovery have not yet been reported. We propose an approach to error recovery that uses the concepts of task stratification and error classification.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Categorization of Errors in Error Recovery Using Task Stratification and Error Classification

Nakamura

Nagata

Harada

et al. 2017

JRNAL

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We proposed an approach to error recovery that uses the concepts of task stratification and error classification. In our method, errors are classified according to the estimated cause into several categories, such as modeling and planning errors. When an error is classified correctly, this increases the probability that the most suitable recovery is performed. In this paper, we describe a procedure for error categorization.

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Section: Introductionmentioning

confidence: 99%

Estimation and Categorization of Errors in Error Recovery Using Task Stratification and Error Classification

Nakamura

Nagata

Harada

et al. 2017

JRNAL

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“…Several researchers in the area of manufacturing have focused on the sensor networks (Levi et al 2010) and fault diagnosis and prediction in case of assembly systems (Rickli et al 2011;Baydar and Saitou 2004). Levi, et al (2010) deals with the sensor networks in terms of its security performance in real world applications.…”

Section: Introductionmentioning

confidence: 99%

“…Fixture faults monitoring using auto regressive models in automotive assembly processes are discussed in Rickli et al (2011). Error prediction, diagnosis, and recovery for discrete part manufacturing using Monte Carlo simulations and genetic algorithm are discussed in Baydar and Saitou (2004).…”

Section: Introductionmentioning

confidence: 99%

Key characteristics-based sensor distribution in multi-station assembly processes

2013

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This paper presents a novel approach for optimal key characteristics-based sensor distribution in a multistation assembly process, for the purpose of diagnosing variation sources responsible for product quality defects in a timely manner. Current approaches for sensor distribution are based on the assumption that measurement points can be allocated at arbitrary locations on the part or subassembly. This not only presents challenges in the implementation of these approaches but additionally does not allow required product assurance and quality control standards to be integrated with them, due to lack of explicit relations between measured features and geometric dimensioning and tolerancing (GD&T). Furthermore, it causes difficulty in calibration of measurement system and increases the likelihood of measurement error due to the introduction of measurement points not defined in GD&T. In the proposed approach, we develop methodology for optimal sensor allocation for 6-sigma root cause analysis that maximizes the number of measurement points placed at critical design features called Key Characteristics (KCs) which are classified into: Key Product Characteristics and Key Control Characteristics and represent critical product and process design features, respectively. In particular, KCs have defined dimensional and geometric tolerances which provides necessary design reference model for process control and diagnosis of product 6-sigma variation faults. The proposed approach allows obtaining minimum required production system 6-sigma diagnosability. A feature-based procedure is proposed which includes Genetic Algorithm-based approach (allowing pre-defined KCs as the measurement points) and state-of-the-art approaches (unrestricted location of measurement points) to iteratively include arbitrary measurement points together with KCs in the final sensor layout. A case study of automotive assembly processes is used to illustrate the proposed feature-based approach AbstractThis paper presents a novel approach for optimal key characteristics-based sensor distribution in a multi-station assembly process, for the purpose of diagnosing variation sources responsible for product quality defects in a timely manner.. Current approaches for sensor distribution are based on the assumption that measurement points can be allocated at arbitrary locations on the part or subassembly. This not only presents challenges in the implementation of these approaches but additionally does not allow required product assurance and quality control standards to be integrated with them, due to lack of explicit relations between measured features and geometric dimensioning and tolerancing (GD&T). Furthermore, it causes difficulty in calibration of measurement system and increases the likelihood of measurement error due to the introduction of measurement points not defined in GD&T. In the proposed approach, we develop methodology for optimal sensor allocation for 6-sigma root cause analysis that maximizes the number of measurement points placed ...

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“…It is called off-line error prediction and recovery (Baydar and Saitou, 2001b). The method uses a commercial software package to model the assembly environment virtually.…”

Section: Introductionmentioning

confidence: 99%

“…Having the sensory symptoms and their associated failure type and 3-D-state, these conditions are stored and used for the diagnosis using Bayesian Reasoning. Next step is using an off-line error recovery system to generate robust recovery plans (Baydar and Saitou, 2001c) that can deal with multiple error conditions of similar nature using Genetic Programming (Baydar and Saitou, 2001b;Koza, 1992). Finally, this of¯ine recovery system can be downloaded to the controller of the robotic system to patch the process.…”

Section: Introductionmentioning

confidence: 99%

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

Baydar

2004

Journal of Intelligent Manufacturing

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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.

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

Cited by 10 publications

References 20 publications

Estimation and Categorization of Errors in Error Recovery Using Task Stratification and Error Classification

Estimation and Categorization of Errors in Error Recovery Using Task Stratification and Error Classification

Key characteristics-based sensor distribution in multi-station assembly processes

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

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