Smart manufacturing for industry 4.0 using Radio Frequency Identification (RFID) technology

Industry 4.0 (I4.0) presents a unique challenge of efficiently transforming traditional manufacturing to smart and autonomous systems.Integrating manufacturing systems, materials, machinery, operators, products and consumers, improve interconnectivity and traceability across the entire product life cycle in order to ensure the horizontal and vertical integration of networked Smart Manufacturing (SM) systems. Manufacturing functions of Material Handling (MH)-control, storage, protection and transport of raw materials, work in process (WIP) and finished products- throughout a manufacturing and distribution process will need a revamp in ways they are currently being carried in order to transition them into the SM era. Radio Frequency Identification (RFID), an Automated Identification Data Capture (AIDC) technology increasingly being used to enhance MH functions in the (SM) industry, due to opportunities it presents for item tracking, out of sight data capturing, navigation and space mapping abilities. The technology readiness level of RFID has presented many implementation challenges as progress is being made to fully integrate the technology into the preexisting MH functions. Recently, many researchers in academia and industry have described various methods of using RFID for improving and efficiently carrying out MH functions as a gradual transition is being made into I4.0 era. This paper reviews and categorize research finding regarding RFID application developments according to various MH functions in SM, tabulates how various I4.0 enablers are needed to transform various traditional manufacturing functions into SM. It aims to let more experts know the current research status of RFID technology and provide some guidance for future research

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation

Empirical Thermal Performance Investigation of a Compact Lithium Ion Battery Module under Forced Convection Cooling

Lithium ion batteries (LiBs) are considered one of the most suitable power options for electric vehicle (EV) drivetrains, known for having low self-discharging properties which hence provide a long life-cycle operation. To obtain maximum power output from LiBs, it is necessary to critically monitor operating conditions which affect their performance and life span. This paper investigates the thermal performance of a battery thermal management system (BTMS) for a battery pack housing 100 NCR18650 lithium ion cells. Maximum cell temperature (Tmax) and maximum temperature difference (ΔTmax) between cells were the performance criteria for the battery pack. The battery pack is investigated for three levels of air flow rate combined with two current rate using a full factorial Design of Experiment (DoE) method. A worst case scenario of cell Tmax averaged at 36.1 °C was recorded during a 0.75 C charge experiment and 37.5 °C during a 0.75 C discharge under a 1.4 m/s flow rate. While a 54.28% reduction in ΔTmax between the cells was achieved by increasing the air flow rate in the 0.75 C charge experiment from 1.4 m/s to 3.4 m/s. Conclusively, increasing BTMS performance with increasing air flow rate was a common trend observed in the experimental data after analyzing various experiment results

Applications of machine learning to friction stir welding process optimization

Machine learning (ML) is a branch of artificial intelligent which involve the study and development of algorithm for computer to learn from data. A computational method used in machine learning to learn or get directly information from data without relying on a prearranged model equation. The applications of ML applied in the domains of all industries. In the field of manufacturing the ability of ML approach is utilized to predict the failure before occurrence. FSW and FSSW is an advanced form of friction welding and it is a solid state joining technique which is mostly used to weld the dissimilar alloys. FSW, FSSW has become a dominant joining method in aero-space, railway and ship building industries. It observed that the number of applications of machine learning increased in FSW, FSSW process which sheared the Machine-learning approaches like, artificial Neural Network (ANN), Regression model (RSM), Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main purpose of this study is to review and summarize the emerging research work of machine learning techniques in FSW and FSSW. Previous researchers demonstrate that the Machine Learning applications applied to predict the response of FSW and FSSW process. The prediction in error percentage in result of ANN and RSM model in overall is less than 5%. In comparison between ANN/RSM the obtain result shows that ANN is provide better and accurate than RSM. In application of SVM algorithm the prediction accuracy found 100% for training and testing process