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Artificial Neural Networks vs. Fuzzy Logic: Simple Tools to Predict and Control Complex Processes—Application to Plasma Spray Processes

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The plasma-sprayed coating architecture and in-service properties are derived from an amalgamation of intrinsic and extrinsic spray parameters. These parameters are interrelated; following mostly non-linear relationships. For example, adjusting power parameters (to modify particle temperature and velocity upon impact) also implies an adjustment of the feedstock injection parameters in order to optimize geometric and kinematic parameters. Optimization of the operating parameters is a first step. Controlling these is a second step and consists of defining unique combinations of parameter sets and maintaining them as constant during the entire spray process. These unique combinations must be defined with regard to the in-service coating properties. Several groups of operating parameters control the plasma spray process; namely (i) extrinsic parameters that can be adjusted directly (e.g., the arc current intensity) and (ii) intrinsic parameters, such as the particle velocity or its temperature upon impact, that are indirectly adjusted. Artificial intelligence (AI) is a suitable approach to predict operating parameters to attain required coating characteristics. Artificial Neural Networks (ANN) and Fuzzy Logic (FL) were implemented to predict in-flight particles characteristics as a function of power process parameters. The so-predicted operating parameters resulting from both methods were compared. The spray parameters are also predicted as a function of achieving a specified hardness or a required porosity level. The predicted operating parameters were compared with the predicted in-flight particle characteristics. The specific case of the deposition of alumina-titania (Al2O3-TiO2, 13% by weight) by APS is considered.

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Abbreviations

AI:

Artificial Intelligence: study and design of systems capable of perceiving their environment and taking actions maximizing their chance of success

ANN:

Artificial Neural Network: interconnected group of artificial neurons which processes information using a connectionist approach to computation

Artificial neuron:

(related to Artificial Neural Network) basic unit in an artificial neural network based on an abstraction of a biological neuron and which receives one or more inputs (representing the one or more dendrites) and sums them to produce an output (synapse). The sums of each node are weighted and the sum is passed through a non-linear function known as transfer function

Back-propagation:

(related to Artificial Neural Network) most used technique used for training feed-forward artificial neural networks (networks that have no feedback or no connections that loop)

Defuzzification:

(related to Fuzzy Logic) procedure during which a real value from the result of the inference is produced and can be used as a fuzzy control input

FL:

Fuzzy Logic: derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic

Fuzzification:

(related to Fuzzy Logic) procedure during which the real input variables (power parameters in the present study) are translated in terms of fuzzy sets

MF:

Membership Function (related to Fuzzy Logic): associates a weighting with each of the inputs that are processed, defines functional overlap between inputs, and ultimately determines an output response. In the present study, MF correspond to the power parameters

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Acknowledgment

The French National Agency for Innovation (OSEO-ANVAR, grant number J 06.09.002 l) and the CNRS MRCT “Plasma” network are gratefully acknowledge for their financial support.

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Authors and Affiliations

  1. LERMPS, Université de Technologie de Belfort-Montbéliard, site de Sévenans, 90010, Belfort cedex, France

    Abdoul-Fatah Kanta, Marie-Pierre Planche & Christian Coddet

  2. SPCTS—UMR CNRS 6638, Faculty of Sciences, University of Limoges, 123 avenue Albert Thomas, 87060, Limoges cedex, France

    Ghislain Montavon & Michel Vardelle

  3. IRIS, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Hawthorn, 3122, Australia

    Christopher C. Berndt

  4. Department of Materials Science and Engineering, Stony Brook University, Stony Brook, NY, 11794, USA

    Christopher C. Berndt

Authors
  1. Abdoul-Fatah Kanta
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  2. Ghislain Montavon
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  3. Michel Vardelle
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  4. Marie-Pierre Planche
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  5. Christopher C. Berndt
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  6. Christian Coddet
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Correspondence to Abdoul-Fatah Kanta or Ghislain Montavon.

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Kanta, AF., Montavon, G., Vardelle, M. et al. Artificial Neural Networks vs. Fuzzy Logic: Simple Tools to Predict and Control Complex Processes—Application to Plasma Spray Processes. J Therm Spray Tech 17, 365–376 (2008). https://doi.org/10.1007/s11666-008-9183-3

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  • DOI: https://doi.org/10.1007/s11666-008-9183-3

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