Overview of PSO for Optimizing Process Parameters of Machining

In the current trends of optimizing machining process parameters, various evolutionary or meta-heuristic techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional and modern machining from 2007 to 2011. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances. From the review, the most machining process considered in PSO was multi-pass turning while the most considered machining performance was production costs

A Lightweight Multifactor Authentication Scheme for Wireless Sensor Networks in the Internet of Things

Internet of Things (IoT) has become an information bridge between societies. Wireless sensor networks (WSNs) are one of the emergent technologies that work as themain force in IoT. Applications based on WSN includeenvironment monitoring, smart healthcare, user legitimacy authentication, and data security. Recently, many multifactoruser authentication schemes for WSNs have been proposedusing smart cards, passwords, as well as biometric features. Unfortunately, these schemes are shown to be susceptibletowards several attacks and these includes password guessing attack, impersonation attack, and Man-in-the-middle (MITM) attack due to non-uniform security evaluation criteria. In this paper, we propose a lightweight multifactor authentication scheme using only hash function of the timestamp (TS) and One Time Password (OTP). Furthermore, public key and private key is incorporated to secure the communication channel. The security analysis shows that the proposed scheme satisfies all the security requirement and insusceptible towards some wellknown attack (password guessing attack, impersonation attack and MITM)

Wearable Sensor Feature Fusion for Human Activity Recognition (HAR) : A Proposed Classification Framework

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 time consuming 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

Feature Selection with Harmony Search for Classification: A Review

In the area of data mining, feature selection is an important task for classification and dimensionality reduction. Feature selection is the process of choosing the most relevant features in a datasets. If the datasets contains irrelevant features, it will not only affect the training of the classification process but also the accuracy of the model. A good classification accuracy can be achieved when the model correctly predicted the class labels. This paper gives a general review of feature selection with Harmony Search (HS) algorithm for classification in various application. From the review, feature selection with HS algorithm shows a good performance as compared to other metaheuristics algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

Estimation of optimal machining control parameters using artificial bee colony

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 (Ra) 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 Ra 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

Scienceploration 2023

The Scienceploration Camp is an initiative of the Centre for Pre-University Studies, UNIMAS (PPPU), which aims to increase the interest in science among secondary school students. It is also an effort taken by PPPU towards the achievement of Sustainable Development Goals 4 in providing equal quality education and promoting lifelong learning opportunities for all. On top of that, this camp supports Sarawak’s Digital Economy Strategy in nurturing an integrated ecosystem to foster inclusive digital society, by building the right foundations to grow our local digital economy

Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining

The machining operation can be generally classified into two types which are traditional machine and non-traditional (modem) machine. There are two types of machining employed in this research, end milling (traditional machining) and abrasive waterjet machining (non-traditional machining). Optimizing the process parameters is essential in order to provide a better quality and economics machining. This research develops an optimization algorithm using artificial bee colony (ABC) algorithm to optimize the process parameters that will lead to minimum surface roughness (Ra) value for both end milling and abrasive waterjet machining. In end milling, three process parameters that need to be optimized are the cutting speed, feed rate and radial rake angle. For abrasive waterjet, five process parameters that need to be optimized are the traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. These machining process parameters significantly impact on the cost, productivity and quality of machining parts. The ABC simulations are developed to achieve the minimum Ra value in both end milling and abrasive waterjet machining. The results obtained from the simulation are compared with experimental, regression modelling, Genetic Algorithm (GA) and Simulated Annealing (SA). In end milling, ABC reduced the Ra by 10% and 8% compared to experimental and regression. In abrasive waterjet, the performance was much better where the Ra value decreased by 28%, 42%, 2% and 0.9% compared to experimental, regression, GA and SA respectively

Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining

The machining operation can be generally classified into two types which are traditional machine and non-traditional (modern) machine. There are two types of machining employed in the research, end miling (traditional machining) and abrasive waterjet machining (non-traditional machining). Optimizing the process parameters is essential in order to provide a better quality and economics machining. This research develops an optimization algorithm using artificial bee colony (ABC) algorithm to optimize the process parameters that will lead to minimum surface roughness (Ra) value for both end miling and abrasive waterjet machining. In end miling, three process parameters that need to be optimized are the cutting speed, feed rate and radial rake angle

Evolutionary techniques in optimizing machining parameters: review and recent applications (2007-2011)

In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques

Overview of PSO for optimizing process parameters of machining

In the current trends of optimizing machining process parameters, various evolutionary or meta-heuristic techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional and modern machining from 2007 to 2011. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances. From the review, the most machining process considered in PSO was multi-pass turning while the most considered machining performance was production costs