Modern machining processes such as abrasive waterjet (AWJ) are widely used in manufacturing industries nowadays. Optimizing the machining control parameters are essential in order to provide a better quality and economics machining. It was reported by previous researches that artificial bee colony (ABC) algorithm has less computation time requirement and offered optimal solution due to its excellent global and local search capability compared to the other optimization soft computing techniques. This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (R a ) value for AWJ machining. Five machining control parameters that are optimized using ABC algorithm include traverse speed (V), waterjet pressure (P), standoff distance (h), abrasive grit size (d) and abrasive flow rate (m). From the experimental results, the performance of ABC was much superior where the estimated minimum R a value was 28, 42, 45, 2 and 0.9 % lower compared to actual machining, regression, artificial neural network (ANN), genetic algorithm (GA) and simulated annealing (SA) respectively.
IntroductionThe manufacturing industries nowadays face many challenges such as market competition, expensive machining cost, customer high request and complexity of the product. For manufacturers, the main objective is to produce high quality of product with less cost and time constraints. Today, modern machining processes are widely used in manufacturing industries because it has some advantages (for example in terms of cost) compared to traditional machining processes (Ridwan et al. 2012; Mokhtar and Xu 2011; Zain et al. 2012a). According to Nagendra Parashar and Mittal 2007, traditional machining processes are costly and inefficient because it is incapable to machine the materials cost-effectively because of the tools is harder than the workpiece. The alteration or new traditional machining methods are also needed because in several cases, the methods might not be operated. Roy and Mehnen (2008) suggest that new method need to be developed in order to guarantee fast, safe and cost efficient production. The modern machining process can be categorized into four types which are (i) mechanical (e.g. abrasive waterjet (AWJ), ultrasonic machining (USM)), (ii) chemical (e.g. chemical machining (CHM)), (iii) electrochemical (e.g. electrochemical machining (ECM), electrochemical grinding (ECG)) and (iv) thermoelectric (e.g. electrobeam machining (EBM), laserbeam machining (LBM)).AWJ machining was considered in this research to compute a minimum R a value. (Zain et al. 2012b) AWJ used a high powerful flow of water in order to cut the workpiece. The high pressure of water (usually more than 900 mph) enables it to cut metal, non-metal, composite and heat sensitive workpiece. The advantage of AWJ is that it never gets dry 123
Human Activity Recognition (HAR) focuses on detecting people's daily regular activities based on time-series recordings of their actions or motions. Due to the extensive feature engineering and human feature extraction required by traditional machine learning algorithms, they are timeconsuming to develop. To identify complicated human behaviors, deep learning approaches are more suited since they can automatically learn the features from the data. In this paper, a feature-fusion concept on handcrafted features and deep learning features is proposed to increase the recognition accuracy of diverse human physical activities using wearable sensors. The deep learning model Long-Short Term Memory based Deep Recurrent Neural Network (LSTM-DRNN) will be used to extract deep features. By fusing the handcrafted produced features with the automatically extracted deep features through the use of deep learning, the performance of the HAR model can be improved, which will result in a greater level of accuracy in the HAR model. Experiments conducted on two publicly available datasets show that the proposed feature fusion achieves a high level of classification accuracy.
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