Data-driven decision making for manufacturing processes using pyramidal dilation attention convolutional neural network optimized with improved dwarf mongoose optimization
Affiliations: [a] Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Hingna Road, Wanadongri, Nagpur, India | [b] Department of Electrical and Electronics Engineering, Xpertmindz Innovative Solutions Private Limited, Kuzhithurai, Tamil Nadu, India | [c] Department of Computing-AI&ML, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India | [d] Department of Electronics and Communication Engineering, Xpertmindz Innovative Solutions Private Limited, Kuzhithurai, Tamil Nadu, India
Correspondence:
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Corresponding author: Ashvin B. Amale, Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Hingna Road, Wanadongri, NAGPUR 441110, India. E-mail: profabamale@gmail.com.
Abstract: In this paper, data-driven decision making for manufacturing processes using Pyramidal Dilation Attention Convolutional Neural Network Optimized with Improved Dwarf Mongoose Optimization (DDMP-PDACNN-IDMO) is proposed. Initially data is taken from the SECOM dataset. Afterward the data is fed to pre-processing. In pre-processing, it removes noisy data using Variational Bayesian-based maximum Correntropy Cubature Kalman Filtering (VBMCCKF). The pre-processed data is given to feature selection. Here, representative features is selected based on the Lirebird Optimization Algorithm (LOA). Next, in order to successfully categorize the manufacturing process as successful or unsuccessful, the chosen feature is given to the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). The PDACNN’s learnable parameters are optimized using Improved Dwarf Mongoose Optimization (IDMO). Python is used for implementing the proposed method. The performance of the proposed technique was examined using performance metrics such as accuracy, and ROC. The performance analysis highlights that the proposed DDMP-PDACNN-IDMO model excels with 98.5% accuracy in the success class and 98.2% in the failure class. The proposed method outperforms DDMP-ANN, DDMP-RNN, and DDMP-BPNN with the fastest runtime (best: 9.4s) and lowest Mean Squared Error (MSE) for both training and testing (best: 0.0392 and 0.0526, respectively). It also achieves the highest R-squared (R2) values, indicating more accurate and reliable predictions. These results confirm the proposed method’s superior performance in speed and accuracy compared with other existing methods such as data-driven manufacturing process based artificial neural network (DDMP-ANN), data-driven manufacturing process based recurrent neural network (DDMP-RNN), and data-driven manufacturing process based back propagation neural network (DDMP-BPNN) respectively.
