Chicken Swarm Optimization and Deep Learning for Manufacturing Processes

In this paper we propose an approach that uses the Multilayer Perceptron Classifier (MPC), a type of deep neural network, for classifying the products generated by the manufacturing processes as faulty or not faulty. The number of nodes from each hidden layer of the MPC was determined using an adapted version of the Chicken Swarm optimization (CSo) algorithm. The proposed method was integrated in an experimental prototype and was tested and validated on two representative datasets for manufacturing processes, namely the SECOM and the SETFI datasets. The main contributions of this article are (1) the use of a MPC for classifying the products resulted after the application of the manufacturing processes as faulty or not faulty, (2) the identification of the number of nodes of the hidden layers of the MPC using CSO and (3) the testing and the validation of the proposed approach using an experimental prototype developed in-house.

[1]  Irene C. L. Ng,et al.  The Internet-of-Things: Review and research directions , 2017 .

[2]  Nittaya Kerdprasop,et al.  Tool Sequence Analysis and Performance Prediction in the Wafer Fabrication Process , .

[3]  Balakrishnan Ramadoss,et al.  Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process , 2016 .

[4]  Achille Fokoue,et al.  An effective algorithm for hyperparameter optimization of neural networks , 2017, IBM J. Res. Dev..

[5]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[6]  Jianyu Long,et al.  Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study , 2017 .

[7]  Axel-Cyrille Ngonga Ngomo,et al.  Big data architecture for the semantic analysis of complex events in manufacturing , 2016, GI-Jahrestagung.

[8]  Majdi Maabreh,et al.  Parameters optimization of deep learning models using Particle swarm optimization , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[9]  Vittaldas V. Prabhu,et al.  A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis , 2017, APMS.

[10]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[11]  Thorsten Meinl,et al.  KNIME - the Konstanz information miner: version 2.0 and beyond , 2009, SKDD.

[12]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[13]  Hassani Messaoud,et al.  A new online fault detection method based on PCA technique , 2013, IMA J. Math. Control. Inf..

[14]  Yu Liu,et al.  A New Bio-inspired Algorithm: Chicken Swarm Optimization , 2014, ICSI.