Ai and Machine Learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing

Monostori, László

doi:10.3182/20020721-6-es-1901.01644

Cited by 42 publications

(33 citation statements)

References 33 publications

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“…Despite these seemingly wide differences, the scientific principles and even the techniques underlying monitoring, diagnosis and prognosis are similar in both the physical and business settings. The application of pattern recognition (PR) techniques, expert systems (ESs), artificial neural networks (ANNs), fuzzy systems (FSs) and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as some consecutive elements of a process started more than two decades ago [123]. Extensive background on monitoring can be found, e.g., in [124] [125] [126] [127] and [128].…”

Section: Process Control Monitoring and Diagnosismentioning

confidence: 99%

Agent-Based Systems for Manufacturing

2006

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The emerging paradigm of agent-based computation has revolutionized the building of intelligent and decentralized systems. The new technologies met well the requirements in all domains of manufacturing where problems of uncertainty and temporal dynamics, information sharing and distributed operation, or coordination and cooperation of autonomous entities had to be tackled. In the paper software agents and multi-agent systems are introduced and through a comprehensive survey, their potential manufacturing applications are outlined. Special emphasis is laid on methodological issues and deployed industrial systems. After discussing open issues and strategic research directions, we conclude that the evolution of agent technologies and manufacturing will probably proceed hand in hand. The former can receive real challenges from the latter, which, in turn, will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.

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Section: Process Control Monitoring and Diagnosismentioning

confidence: 99%

Agent-Based Systems for Manufacturing

2006

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“…Artificial intelligence approaches and machine learning techniques deal with uncertainty in manufacturing management through modelling quantitative information [15]. For example, the Petri net technique is used to relocate operations in order to use shop floor resources in the presence of machine breakdown uncertainty [16].…”

Section: Current Methods To Tackle Uncertaintymentioning

confidence: 99%

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

Azizi

Ali

Ping

2015

The South African Journal of Industrial Engineering

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Production throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS) to modelling the throughput of production under five significant production uncertainties: scrap, setup time, break time, demand, and lead time of manufacturing. The effects of these uncertainties on the production of floor tiles were studied by performing 104 observations on the production uncertainties over 104 weeks, based on a weekly production plan in a tile manufacturing industry. The results of the ANFIS model were compared with the multiple linear regression (MLR) model. The results showed that the ANFIS model was capable of forecasting production throughput under uncertainty with higher accuracy than was the MLR model, indicated by an R-squared of 98 per cent. OPSOMMINGProduksie deurset meet die vertoning en gedrag van 'n produksiesisteem. Produksie deurset modellering is ingewikkeld as gevolg van die onsekerhede in die produksielyn. Hierdie studie ondersoek die toepassing van die aanpasbare neuro-wasige afleidingsisteem om die deurset van produksie onderhewe aan vyf noemenswaardige produksie onsekerhede, naamlik skroot, opstel tyd, breek tyd, aanvraag en die vervaardiging leityd. Die effek van hierdie onsekerhede op die vervaardiging van vloerteëls is ondersoek deur 104 weeklikse observasies op die produksie onsekerhede oor 'n tydperk van 104 weke te neem. Die resultate van die model is vergelyk met 'n meervoudige lineêre regressie model. Die resultate toon dat die aanpasbare neuro-wasige afleidingsisteem in staat was om produksie deurset onderhewe aan onsekerheid te voorspel met 'n hoër akkuraatheid as die meervoudige lineêre regressie model. Dit word aangedui met 'n bepaalheidskoëffisient van 98 persent.

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“…In recent years, artificial intelligence (AI) has been widely applied to various fields, and many great achievements have been made in this field. For instance, the complex changes and uncertainties in manufacturing can be managed by AI and machine learning techniques [9]. Another case in point is the routing problems for ground vehicles, and minimal total cost can be achieved using AI without violating the capacity and time window constraints [10].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Path Selection Strategy for Aircraft Landing on a Carrier

Song

et al. 2018

Applied Sciences

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Abstract:Landing is one of the most dangerous tasks in all the operations on an aircraft carrier, and the landing safety is very important to the pilot and the flight deck operation. Nowadays, the landing safety of carrier aircraft is improved by designing an automatic landing controller and by training the pilot to increase his/her control ability. However, the importance of choosing the landing path has not been investigated thus far. In this paper, the problem of landing path selection for an aircraft carrier is studied as there are several candidates corresponding to different situations. A fuzzy path selection strategy is proposed to solve the problem considering the fuzziness of environmental information and human judgment, and the goal is to provide the pilot with a more reasonable decision. The strategy is in view of the idea of Fuzzy Multi-attribute Group Decision Making (FMAGDM), which has been widely used in industry. Firstly, the background of the landing path selection is given. Then, the factors influencing the decision making are abstracted to build the conceptual model. A TOPSIS-based group decision-making method is developed to denote the preference of each decision maker for each alternative route, and the optimal landing path under the current environment is determined taking into account the knowledge and the weight of both the pilot and the landing console operator (LCO). Experimental studies under different setups, i.e., different environments, are carried out. The results demonstrate that the proposed path selection strategy is validated in different environments, and the optimal landing paths corresponding to different environments can be determined.

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Ai and Machine Learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing

Cited by 42 publications

References 33 publications

Agent-Based Systems for Manufacturing

Agent-Based Systems for Manufacturing

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

A Fuzzy Path Selection Strategy for Aircraft Landing on a Carrier

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