Machining operation optimization improves the quality of the product, reduces cost, enhances overall efficiency by reducing human error, and enables consistent and efficient operation. It is a vital decision-making process and achieves the best solution within constraints. It reduces reliance on machine-tool technicians and handbooks to identify cutting parameters, as a lack of awareness of the optimal combination of machining parameters leads to several machining inefficiencies. Subsequently, the optimization of the machining process is more useful for units of production, particularly machining units. In multi-objective optimization (MOO) problems, weights of importance are assigned, mostly identical. But, nowadays, the weights assignment techniques have received a lot of consideration from the professionals and researchers in MOO problems. Various techniques are developed to assign weights of significance to responses in MOO. The Entropy weights method (EWM) continues to work pleasingly across diverse machining operations to allocate objective weights. In this paper, a literature review is conducted to classify the articles on EWM applications in machining operations. The categorization proposal for the EWM reviews included 65 academic articles from different journals, books, and conferences since the year 2009. The EWM applications were separated into 18 categories of conventional and non-conventional machining operations. The implementation procedure of EWM is presented with an example along with method development. Scholarly articles in the EWM applications are further inferred based on (1) implementation of EWM in different machining operations, (2) MOO methods used with entropy weights in machining operations, (3) application of entropy weights by citation index and publication year, and (4) entropy weights applications in other fields. The review paper provided constructive insight into the EWM applications and ended with suggestions for further research in machining and different areas.

Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: A critical review

The enormous use of cutting fluid in machining leads to an increase in machining costs, along with different health hazards. Cutting fluid can be used efficiently using the MQL (minimum quantity lubrication) method, which aids in improving the machining performance. This paper contains multiple responses, namely, force, surface roughness, and temperature, so there arises a need for a multicriteria optimization technique. Therefore, in this paper, multiobjective optimization based on ratio analysis (MOORA), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and technique for order of preference by similarity to ideal solution (TOPSIS) are used to solve different multiobjective problems, and response surface methodology is also used for optimization and to validate the results obtained by multicriterion decision-making technique (MCDM) techniques. The design of the experiment is based on the Box–Behnken technique, which used four input parameters: feed rate, depth of cut, cutting speed, and nanofluid concentration, respectively. The experiments were performed on AISI 304 steel in turning with minimum quantity lubrication (MQL) and found that the use of hybrid nanofluid (Alumina–Graphene) reduces response parameters by approximately 13% in forces, 31% in surface roughness, and 14% in temperature, as compared to Alumina nanofluid. The response parameters are analyzed using analysis of variance (ANOVA), where the depth of cut and feed rate showed a major impact on response parameters. After using all three MCDM techniques, it was found that, at fixed weight factor with each MCDM technique, a similar process parameter was achieved (velocity of 90 m/min, feed of 0.08 mm/min, depth of cut of 0.6 mm, and nanoparticle concentration of 1.5%, respectively) for optimum response. The above stated multicriterion techniques employed in this work aid decision makers in selecting optimum parameters depending upon the desired targets. Thus, this work is a novel approach to studying the effectiveness of hybrid nanofluids in the machining of AISI 304 steel using MCDM techniques.

Study of a Multicriterion Decision-Making Approach to the MQL Turning of AISI 304 Steel Using Hybrid Nanocutting Fluid

The constant investigation, as well as dispensation of data among various processing, has been influenced by computerized strategies enabled by artificial neural network associated with Internet of Things, as well as cloud-dependent organizations. Multihoming is also a category of an organization that brings together several categories of organizations in its atmosphere during the dealing with various information. These days, the massive amounts of information being handled also observed in multihoming systems are given less thought, reducing the security risk and effectiveness of data processing and observation. The application of ANN-associated frameworks among multihoming massive information using IoT as well as AI-associated frameworks could be beneficial in a variety of ways. The constant investigation and dispensation of information among various processing are influenced by computerized strategies enabled by ANN associated with IoT, as well as cloud-associated organizations. Multihoming is also a kind of organization that brings together several kinds of organizations into a unique situation while dealing with a large volume of information. When multihoming enormous amounts of data with IoT and AI-integrated systems, there are various advantages to employing AI-based solutions. Despite the fact that multihoming security difficulties and their evaluation have been carefully explored by various scientists and researchers, big data security analysis in multihoming, particularly when using automated methodologies and systems, receives little attention. These days, the massive amounts of information being handled and observed in multihoming systems are given less thought, reducing the security risk and effectiveness of data processing and observation. The application of AI-based frameworks in multihoming massive information with IoT associated with AI frameworks could be beneficial in a variety of ways. Even though multihoming security challenges and their analysis have been widely focused on by many researchers and specialists, little attention has been paid to them.

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Secure Big Data Processing in Multihoming Networks with AI-Enabled IoT

This article describes the multi-response optimization of electrochemical machining operating parameters such as voltage, concentration of the electrolyte, and current to maximize the rate of mater...

Multi-response optimization of electrochemical machining parameters in the micro-drilling of AA6061-TiB2in situ composites using the Entropy–VIKOR method:

As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

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AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

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Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology

To improve the speed of global optimization algorithm, a class of global optimization algorithms for intelligent electromechanical control system with improved filling function is proposed. By attaching the intelligent managing system improving algorithm and the filling function procedure, the algorithm can stand out from the current particular optimal solution, avoid the phenomenon of falling into the local favorable solution in the process of algorithm iteration, make the algorithm find a better solution, and improve the efficiency of solving the multiextremum global improving problem. Multiextremum-seeking is an optimal control technique that works with unknown conditions while assuming that measurements of the plant’s input and output signals are accessible. The presented work is for an electromechanical system which will handle the low accuracy and untimely tendency of conventional systems which are used in various practical applications. Few learning algorithms have been developed to explicitly optimize mean average precision (MAP) due to computational constraints. The outcomes show that the convergence of the test functions F6 and F7 is not good when the MAPID algorithm is only used for optimization. The MAPID_FF algorithm not only ensures the convergence and optimization precision of the two test functions, but also reduces the optimization time compared with the filling function method. Compared with the filling function method, the improved algorithm has higher accuracy and faster speed, and it is not simple to fall into the local optimum, so the global optimal value is more accurate.

A Global Optimization Algorithm for Intelligent Electromechanical Control System with Improved Filling Function

In the present study, vibration and damping characteristics of the multi-walled carbon nanotubes (MWCNT) reinforced honeycomb embedded sandwich composite shell structures have been investigated numerically using finite element (FE) method. The efficacy of the FE method by deriving the governing equations using higher order theory is verified by comparing the natural frequencies assessed using ABAQUS 3D FE model. The influence of MWCNT reinforcement, support condition, and radius of curvature on the dynamic performance of honeycomb sandwich shell with carbon fiber reinforced polymer (CFRP) composite face sheets are explored. In addition, the optimal ply orientations of the various configurations of CFRP sandwich shells with MWCNT/GFRP honeycomb are identified using the developed FE model coupled with genetic algorithm (GA) to enrich the natural frequencies and loss factors. Further, it can be observed that the reinforcement of MWCNTs in honeycomb core, geometry of shell structure, and optimal ply orientations significantly influences the natural frequencies and loss factors of the sandwich composite shell structures. 

Influence of multi‐walled carbon nanotubes reinforced honeycomb core on vibration and damping responses of carbon fiber composite sandwich shell structures

Optimization of Wire Electric Discharge Machining Parameters in Machining of Ti-6Al-4V Alloy

This article reveals the experimental investigation on the machinability of A356-TiB2/TiC in-situ composites prepared by a mixed salt reaction system. The fabricated composites are characterized by Energy dispersive analysis (EDAX), X-ray Diffraction (XRD), scanning electron microscopy (SEM) and micro-hardness analysis. Multi-coated tungsten carbide tool was used to examine the influence of TiB2/TiC reinforcement ratio on machinability behaviour of composites. The variations in cutting speed, feed rate and depth of cut upon cutting force, surface roughness and flank wear were examined. The experimental results revealed that the enhancement of a reinforcement ratio causes the decrease in cutting force and increase in flank wear and surface roughness. Higher flank wear is observed, when machining the A356-TiB2/TiC composites at higher cutting speed due to the generation of high temperature at the machining interface. The increment in surface roughness, flank wear and cutting force is experienced at higher depth of cut and feed rate. Further, the mechanisms of chip formation and surface generation under different machining parameters are addressed. The outcome of this experimental investigation helps to utilize the turning process for machining the in-situ composites at economic machining rate without compromising the surface quality.

Ismail Kakaravada

Papers

Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology

A Global Optimization Algorithm for Intelligent Electromechanical Control System with Improved Filling Function

Influence of multi‐walled carbon nanotubes reinforced honeycomb core on vibration and damping responses of carbon fiber composite sandwich shell structures

Optimization of Wire Electric Discharge Machining Parameters in Machining of Ti-6Al-4V Alloy

Investigation on Cutting Force, Flank Wear, and Surface Roughness in Machining of the A356-TiB2/TiC in-situ Composites