Search results

Aluminium silicon carbide reinforced metal matrix composite (Al/SiC‐MMC) materials are rapidly replacing conventional materials in various automotive, aerospace and other industries. Accordingly, the need for accurate machining of composites has increased enormously. The present work analyzes the machining of Al/SiC composites for surface roughness. An empirical model has been developed to correlate the machining parameters and their interactions with surface roughness. Response surface regression and analysis of variance are used for making the model. The developed model can be effectively used to predict the surface roughness in machining Al/SiC‐MMC composites. The influences of different parameters in machining Al/SiC particulate composites have been analyzed through contour graphs and 3D plots.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

Dissimilar joining of austenitic stainless steels and ferritic steels is a challenging task and has a wide range of applications due to its excellent mechanical and thermal characteristics. They are joined mostly by using conventional modes. In the current investigation, the study and optimization of hot wire TIG welding parameters was carried out.

These parameters will govern the desired characteristics of the joint. Solutions were found out through multi-response optimization by using response surface methodology and single response optimization using particle swarm optimization.

Optimized input welding parameters that were achieved are electrode current 180 amps, wire feed rate 1870 mm/min and hot wire current 98 amps and the optimized UTS is 665.45 MPa. The results from PSO were compared with RSM and the optimized input welding parameters for the electrode current, hot wire current and wire feed rate exhibited maximum ultimate tensile strength which were also confirmed from response and contour plots.

Sensitivity analysis was also performed to understand the effect of each individual parameters on the response. Microstructure features were evaluated for the joints and was found that the characteristics are within the desired criteria.

This paper aims to consolidate the results of various researchers focusing the different applications, so that this paper could become the torch bearer for the futuristic researchers working in the domain of cold gas dynamics spray coating.

A study on the cold spray coating is presented by summarizing the data present in literature. Important factors such as coating temperature, pressure, coating thickness, particle size, which affect the erosion-corrosion (E-C) resistance, physical and mechanical properties of boiler steel are stated. This paper also addresses the use of cold spray coating and compares it with other different thermal spray processes.

From the literature review, it was noticed that cold spray technology is best as compare to other thermal spray processes to reduce porosity, increase hardness, adhesion strength and retention in properties of feedstock powders.

Cold spray coating technology has a great potential in almost every field especially in restoration of surfaces, generation of complex surface, biomedical application, resist hot corrosion, wear, oxidation and erosion corrosion.

Real-time visibility and traceability in warehousing could be accomplished by implementing the internet-of-things (IoT) technology. The purpose of this paper is to develop a roadmap for designing an IoT-based smart warehouse infrastructure and, respectively, design and apply the IoT-based smart warehouse infrastructure using a developed roadmap. More specifically, this study first identifies critical components to design an IoT-based smart warehouse infrastructure. Second, the study at hand identifies essential factors that contribute to the successful implementation of IoT-based smart warehouse infrastructure.

A qualitative-descriptive method, through a comprehensive review of the relevant studies, was used in this study to develop a roadmap. A prototype system was then designed to simulate a case company’s actual warehouse operations in one of the manufacturing companies in Indonesia.

A framework was proposed which is viable for designing an IoT-based smart warehouse infrastructure. Based on the data collected from a case company, the proposed smart warehouse infrastructure design successfully implemented real-time visibility and traceability and improved overall warehouse efficiency.

While the framework in this research was carried out in one of the developing counties, the study could be used as the basis for future research in a smart warehouse, IoT and related topics.

This research enhances the limited knowledge to establish the IoT infrastructure for a smart warehouse to enable real-time visibility and traceability. This study is also the first to specifically propose a framework for designing an IoT-based smart warehouse infrastructure. The proposed framework can motivate companies in developing countries to deploy efficient and effective smart warehouses using IoT to drive the countries’ economic growth.

The purpose of this paper is surface roughness prediction using pattern recognition for the aluminium hybrid metal matrix composite (HMMC).

Hybrid composites were manufactured using liquid metallurgy technique. The cast HMMC was machined using an industrial CNC turning centre and the machining vibration signals were acquired using an accelerometer. The acquired signals were processed and used to build a machine learning model for predicting surface finish based on the tool signature.

The authors established a technique for predicting and monitoring the surface quality during machining using a low cost accelerometer. It is capable of being integrated with the machine controller for online warning of deviations in surface roughness. The system is reconfigurable for any machining condition with a very short training period. The use of this model facilitates online surface roughness monitoring, avoiding the need for costly measuring equipment.

The model developed is innovative and not reported widely to the best of the authors' knowledge. The use of accelerometer‐based surface roughness prediction and control is an innovative approach for automation of machining process monitoring. These can be integrated into any existing machining centre as a standalone system or can be integrated into the CNC controller like Fanuc or Siemens.

The purpose of this study is to investigate the management of intragroup conflicts occurring in project design teams during pre-contract stages that affect the project deliverables of construction projects implemented in Sri Lanka.

A qualitative approach with a case study research strategy was used in the study. The required empirical data were collected by conducting expert interviews and reviewing the documents used in four selected projects implemented in Sri Lanka. All the projects used traditional separated procurement systems and had re-measurement contracts. Manual content analysis was used to analyze the collected data.

The study findings revealed the different types of intragroup conflicts that arise during the pre-contract stages of projects within project design teams of construction projects implemented in Sri Lanka and the causes and effects of those conflicts on project deliverables. The study recommends proactive strategies that can be adopted to manage those conflicts.

A dearth of literature on the association between the intragroup conflicts that arise during the design stages of construction projects and project deliverables exists. This study bridged this research gap. It is novel because it reveals the impact of intragroup conflicts that occur during the pre-contract stages of construction projects on project deliverables and identifies several proactive strategies that will assist in managing those intragroup conflicts.

Motivated by consumers' concerns about water resources, this paper studies the interactive impact of advertising efforts and water-saving on corporate profits in apparel supply chains. Moreover, this study attempts to find an effective way to improve the profit of supply chain members under different game scenarios. Therefore, this study explores the game scenarios with considering the cost-sharing contract.

This paper constructs two basic game models considering different market power in the apparel supply chains, and explores the Pareto improvement combined with cost-sharing contracts based on the basic models. Furthermore, this study extends the models by considering cost-saving and non-linear demand.

In this paper, it can be found that advertising efforts and water-saving have complex interactive relationships. Counter-intuitively, the increase in advertising efforts may increase water savings. Furthermore, it presents a Pareto improvement when considering cost-sharing contracts, and both the manufacturer and the retailer's profits may improve simultaneously. Moreover, it does not affect the main conclusions when consider the effects of cost-saving and non-linear demand.

Although some important findings have been reached, this paper can be extended in many ways in the future. For example, the coordination mechanism among supply chain members can be considered and the fair distribution of profits can be studied. Moreover, the influence of the government policies on the optimal strategy, as well as changes in social welfare can be considered.

This study offers supply chain members the guidelines on coordinating water-saving investment and advertising efforts which provided new insight into the interaction of these two factors in the apparel supply chains. Moreover, it can provide a coordination mechanism for the supply chain members to improve their profits.

This paper explores the interactive relationship between water-saving and advertising efforts. It can not only save more water resources but also enable consumers to enjoy more environmentally friendly apparel products.

The current literature mainly focuses on the impact of advertising efforts on firm profit. However, this paper studies the interaction between advertising efforts and water-saving in apparel supply chains. Furthermore, this study explores the optimal pricing strategies and Pareto improvement by considering cost-sharing contracts. It can provide theoretical and practical guidance for the decision-maker in deciding on advertising and water-saving investment.

The objective of this research is focused on the design of a new hybrid composite as well as to analyse the optimum turning conditions to minimise the surface roughness and work piece surface temperature, thereby increasing the productivity.

Mechanical properties such as hardness and tensile strength of Al-Si10Mg alloy reinforced with 3, 6 and 9 wt.% of alumina along with 3 wt.% of graphite prepared by stir casting method have been evaluated. The present study addresses the machinability parameter optimisation of Al alloy-9 per cent alumina-3 per centgraphite. Experiments were conducted based on the Taguchi parameter design by varying the feed (0.1, 0.15 and 0.2 mm/rev), cutting speed (200, 250 and 300 m/min) and depth of cut (0.5, 1.0 and 1.5 mm). The results were then analysed using analysis of variance (ANOVA).

Mechanical properties of the hybrid composite increases with reinforcement content. The surface roughness decreases with increasing cutting speed and conversely increases with increasing feed and depth of cut. The work piece surface temperature increases as cutting speed, feed and depth of cut increases. The ANOVA result reveals that feed plays a major role in minimising both surface roughness and surface temperature of work piece. The cutting speed and depth of cut follow feed in the order of importance, respectively.

The vibration of the machine tool is a factor which may contribute to poor quality characteristics. This factor has not taken been into account in this analysis since major vibrations in the machine are induced due to the machining process.

Design and development of new hybrid metal matrix composites (HMMCs) with a detailed analysis on machining conditions. The findings could help in the production of composite with a higher degree of surface finish. This will enable the adoption of HMMCs as industrial product for mass scale production.

Good quality characteristics were achieved using optimum machining conditions arrived using a statistical modelling.