Electrochemical Discharge Machining of Non-Conducting Ceramics

The electrochemical discharge machining (ECDM) process is mostly applied for machining nonconductingengineering ceramic materials, such as aluminium oxides, zirconium oxides, and silicon nitrides,etc. Experiments on ECDM have been carried out according to designed experimental plan based on standardorthogonal array (L,) to identify the optimal parametric conditions of ECDM process using Taguchi methodof parametric optimisation. In this study, the signal-to-noise (SIN) ratio and the ANOVA analyses are employedto find the relative contributions of the main machining parameters, such as applied voltage, electrolyteconcentration and interelectrode gap in controlling the machining performance, such as material removal rateand radial overcut of the ECDM process. The confirmation of experimental results under optimal parametriccondition are provided to ensure the improvement in quality characteristics of the ECDM process. The highlypurified non-conducting zirconium oxide is used as workpiece material and aqueous KOH in stagnant conditionas electrolyte with three different concentrations (i.e., 15 per cent, 25 per cent and 20 per cent). The appliedvoltage of pulsed d.c. power supply has three levels of 50 V, 60 V and 70 V and the three different interelectrodegap setting considered for the experiments are 20mm, 30mm and 40rnm respectively

COMPARATIVE STUDY OF SURFACE ROUGHNESS CRITERIA DURING PULSED Nd:YAG LASER MICRO-TURNING OF ALUMINA CERAMIC AT LASER FOCUSED AND DEFOCUSED CONDITIONS

Abstract Laser micromachining technology finds great potentials for successful application in the area of high precision micro-engineering. Laser micro-turning process is one of the new and emerging technologies in the area of laser material processing (LMP) of engineering materials. Laser micro-turning process is one of the latest promising laser material processing techniques which can be employed for generation of micro-turning surface of particular surface profile and dimensional accuracy on cylindrical workpiece. The present paper addresses the laser micro-turning process of cylindrical shaped 99% pure aluminium oxide (Al 2 O 3 ) ceramics of size 10 mm in diameter and 40 mm in length. The experiments have been conducted utilizing one factor at a time (OFAT) experimental scheme. The targated depth was set at 100 µm. Laser average power, pulse frequency, workpiece rotating speed and Y feed rate were considered as process variables. After each experiment, surface roughness (Ra and Rt) has been measured. An attempt has been made for comparative study and analyse the effect of focused and defocused conditions of laser beam on surface roughness criteria of laser micro-turning surface. From the experimental results, it was revealed that both surface roughness, Ra and Rt, are less at all defocusing conditions of laser beam compared with focusing condition of beam. However, other process parameters have significant effects on surface roughness criteria at both the focused and defocused conditions. Optical and SEM micrographs of the laser turned surface have also been studied comparatively to examine the quality of machined surface at various parametric settings and focusing conditions

Success evaluation factors in construction project management : some evidence from medium and large portuguese companies

The construction industry plays a very important role in the Portuguese economy. In 2009, it was among the top five economic sectors, representing 13% of total employment. Nevertheless, project failures are still frequent mainly due to inadequate management practices and to the intrinsic characteristics of projects of the construction industry. Even though Portuguese construction has improved in recent years, cost and schedule overruns, low productivity and final product quality problems are still common. In this context, project management is a crucial tool for improving construction operations and for the overall success of projects. The aim of this article is to contribute to the discussion on success evaluation factors in a field where little has been written – the construction industry. Through a survey of 40 medium and large Portuguese companies several factors were identified which are currently considered in the evaluation of project success, as found in the literature review. The results show that the traditional factors, often referred to as the “Atkinson elements triangle” (cost, time and quality), are still the most relevant for evaluating the success of a project, but others, such as customer involvement and acceptance, have gained importance in recent years

Latent provisions for building information modeling (BIM) contracts: a social network analysis approach

The effective adoption and use of Building Information Modeling (BIM) require appropriate contract design to fairly allocate the contracting parties’ rights and responsibilities. Several standards for BIM protocols and contracts have been developed for the industry. However, the awareness and the use of these are rather limited, leading to unclear provisions in BIM contracts. Therefore, the research aims to identify the influential legal aspects that serve as the latent contract provisions in BIM contracts. A questionnaire survey was conducted to survey experts and active BIM users in construction projects. The data were analyzed using social network analysis (SNA) by assuming interdependent relationships among various the legal aspects in BIM contacts. The key legal aspects associated with BIM contracts pertain to the roles and responsibilities of the project participants. The results also reveal that data security is the center of all latent legal aspects in the contracts. The study provides significant new insights into clarifying the required contract provisions in BIM contracts

Causes of delay and cost overrun in Malaysian construction industry

The construction industry in Malaysia drives the economic growth and development of the country. However, the industry is plagued with delays and cost overrun which transforms what should have been successful projects to projects incurring additional costs, disagreements, litigation and in some cases abandonment of projects. This research studied the causes of delays and cost overrun in the industry and ranked them according to their perceived importance to the contractors, with a view to establishing those to be addressed by the contractors. Online questionnaires were used for data collection for this research. A total of 69 responses were analysed using principal component analysis (PCA) (factor analysis) to identify the main causes. The result of the analysis showed that delay in preparation of design document, poor schedule and control of time, delay in delivery of material to site, lack of knowledge about the different defined execution methods, shortage of labour and material in market, and changes in scope of work were the main causes of delay and cost overrun. The identified causes if properly addressed would reduce the rate of delays and cost overrun in construction projects, thus enhancing the economic growth and development of the country

Application of Back Propagation Neural Network Model for Predicting Flank Wear of Yttria Based Zirconia Toughened Alumina (ZTA) Ceramic Inserts

A back propagation neural network model has been adopted for the flank wear prediction of zirconia toughened alumina (ZTA) insert in turning operation. The experiments are performed on AISI 4340 steel using developed yttria based ZTA inserts. These inserts are prepared through wet chemical co-precipitation route followed by powder metallurgy process. Machining conditions such as cutting speed, feed rate and depth of cut are selected as input to the neural network model and flank wear of the inserts corresponding to these conditions has been chosen as the output of the network. The experimentally measured values are used to train the feed forward back propagation artificial neural network for prediction of those conditions. The convergence of the mean square error both in training and testing come out very well. The performance of the trained neural network has been validated with experimental data. The results demonstrate that the machining model is suitable and the optimization strategy satisfies practical requirements

Predictive modeling of surface roughness in high speed machining of AISI 4340 steel using yttria stabilized zirconia toughened alumina turning insert

An attempt has been made to investigate the surface finish of AISI 4340 steel for high speed machining using indigenously prepared yttria stabilized zirconia toughened alumina (ZTA) cutting inserts. These inserts are prepared through wet chemical co-precipitation route followed by powder metallurgy process. Response surface methodology (RSM) has been used to study the effect of different machining parameters i.e. cutting speed, feed rate and depth of cut on surface roughness of the job. The machining experiments are performed based on standard RSMdesign called central composite design (CCD). Themathematicalmodel of surface roughness has been developed using second order regression analysis. The adequacy of the developed models and influence of each operating factors have been carried out based on analysis of variance (ANOVA) techniques. It can be concluded from the present study that for high speed machining this tool gives good surface finish. Key parameters and their interactive effect on each response have also been presented in graphical contours which may help for choosing the operating parameter preciously. Optimization of cutting parameters has also been carried out and 92.3% desirability level has been achieved using this optimal condition

AI-based techniques in cellular manufacturing systems: a chronological survey and analysis

This article portrays a chronological review of the influence of artificial neural network in group technology applications in the vicinity of cellular manufacturing systems. The research trend is identified and the evolvement is captured through a critical analysis of the literature accessible from the very beginning of its practice in the early 90s till the 2012. Analysis of the diverse ANN approaches, spotted research pattern, comparison of the clustering efficiencies, the solutions obtained and the tools used make this study exclusive in its class

An Immune Genetic algorithm for inter-cell layout problem in cellular manufacturing system

The objective function of inter-cell layout problem minimizes the total inter-cellular material handling cost. It is mostly significant with moderate production quantity in cellular manufacturing systems (CMS). This problem is classified as quadratic assignment problem (QAP) which is NP-Hard in nature. Heuristic techniques are extremely effective for such problems. In this paper we proposed a novel Immune Genetic algorithm (Immune-GA-RS) to obtain competent inter-cell layout in the vicinity of CMS. It exploits an elitist replacement strategy in order to improve the rate of convergence. The proposed method is successfully tested upon 8 datasets which are being widely used for inter-cell layout design problems. Proposed Immune-GA-RS is compared with two variants of the Genetic Algorithms, GA-RS and Alt-GA-RS. It is further compared with other published layout design techniques. Immune-GA-RS is shown to acquire 11.11 % improved solutions with 7.72 % reduced CPU time on an average. Further Immune-GA-RS is tested on 36 structured QAP instances available through QAPLIB and shown to outperform other two GA variants while attaining optimal solutions for 33 instances. It is also shown to outpace other published algorithms while attaining smaller solution gap for 11 test instances and obtains at least equal or better quality solutions for 24 instances. We conclude our work with a statistical data test to signify the results.submittedVersionThis is a pre-print of an article published in [Production Engineering]. The final authenticated version is available online at: https://doi.org/10.1007/s11740-015-0645-