Informatics Publishing Limited

G. Senthil Kumar ¹, U. Natarajan ², G. Sankaranarayanan ³

Affiliations
1 Department of Mechanical, Velammal College of Engineering & Technology, Madurai, Tamil Nadu, IN
2 Department of Mechanical, A. C College of Engineering & Technology, Karaikudi, Tamil Nadu, IN
3 Department of Mechanical, Raja College of Engineering & Technology, Madurai, Tamil Nadu, IN

Abstract

Metal Inert Gas (MIG) welding is one of the major metal joining process used to fabricate many engineered artifacts and structure such as cars, ships, space shuttles and pipe lines. Flaws resulted from welding operations are detrimental to the integrity of the fabricated artifacts or structure. Although the welding process is carried out as manually or automatically, flaws are formed during the welding operations.These flaws include lack of fusion, porosities, cracks, lack of penetration, excess weld, insufficient weld, inclusions, gas holes etc. To maintain the desirable level of structural integrity, welds must be inspected according to the established standards. In this paper, a machine vision system is introduced to extract the various features of the MIG welded joint by capturing image through CCD camera with proper illumination, and then various image processing techniques and classifier is used to calssify the defects accoriding to the international standards. This vision system is connected to the host computer and classification is done by artificial neural network based on predefined one. In this proposed method, a comparison is made between the accuracy of the single image by turn on four zones LEDs of the illumination at a time with the accuracy of the multiple images by the zones LEDs are turned on, one after the other This proposed method enables overall accuracy of the four zones of the images as 95% from the 40 samples of the welded images and finally parameters are used to evaluate the performance of the proposed system.

Keywords

MIG Welding,Welding Defects, Vision System, Feature Extraction.

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G. Senthil Kumar ¹, U. Natarajan ², M. Srinivasagan ²

Affiliations
1 Dept. of Mech., Velammal College of Engg & Tech., Madurai, Tamil Nadu, IN
2 Dept. of Mech., A. C College of Engg & Tech., Karaikudi, Tamil Nadu, IN

Abstract

In this paper, an efficient technique has been described for inspection of Metal Inert Gas welding (MIG). A machine vision system has been developed for identifying and classifying the surfaces of butt joint as per standard EN25817 in MIG welding.Images of welded surfaces are captured through CCD camera. Then regions of interest are segmented and the average gray levels of the characteristic features of these images are calculated using 2D feature vector and Gaussian distribution based features. Finally, welded joints can be classified into one of the four pre-defined images based on the back propagation neural network. In this work, 80 real samples of images are tested and performance of the vision system is compared with twodifferent feature extractions. vision inspection system using Gaussian based feature extraction method produced 93.75% than 2D feature extraction method which is produced 92.5%.

Keywords

Machine Vision, Weld Classification, Industrial Inspection, Back Propagation Neural Network (BPN).

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G. Senthil Kumar ¹, U. Natarajan ², G. Sankaranarayanan ³

Affiliations
1 Department of Mechanical, Velammal College of Engineering & Technology, Madurai, Tamil Nadu, IN
2 Department of Mechanical, A. C. College of Engineering & Technology, Karaikudi, Tamil Nadu, IN
3 Department of Mechanical, Raja College of Engineering & Technology, Madurai, Tamil Nadu, IN

Abstract

Metal Inert Gas (MIG) welding is one of the major metal joining process used to fabricate many engineered artifacts and structure such as cars, ships, space shuttles and pipe lines. Flaws resulted from welding operations are detrimental to the integrity of the fabricated artifacts or structure. Although the welding process is carried out as manually or automatically, flaws are formed during the welding operations.These flaws include lack of fusion, porosities, cracks, lack of penetration, excess weld, insufficient weld, inclusions, gas holes etc. To maintain the desirable level of structural integrity, welds must be inspected according to the established standards. In this paper, a machine vision system is introduced to extract the various features of the MIG welded joint by capturing image through CCD camera with proper illumination, and then various image processing techniques and classifier is used to calssify the defects accoriding to the international standards .This vision system is connected to the host computer and classification is done by artificial neural network based on predefined one. In this proposed method, a comparison is made between the accuracy of the single image by turn on four zones LEDs of the illumination at a time with the accuracy of the multiple images by the zones LEDs are turned on, one after the other This proposed method enables overall accuracy of the four zones of the images as 95% from the 40 samples of the welded images and finally parameters are used to evaluate the performance of the proposed system.

Keywords

MIG Welding, Welding Defects, Vision System, Feature Extraction.

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U. Natarajan ¹, A. Kumaravadivel ²

Affiliations
1 Department of Mechanical Engg. in ACCE & Tech, Karaikudi, Tamil Nadu, IN
2 Department of Mechanical Engg. In., Sudharsan Engineering. College, Pudukkottai, Tamilnadu, IN

Abstract

An attempt has been made to implement a systematic six-sigma DMAIC (Define–Measure-Analyze-Improve-Control) methodology and enhancing job satisfaction in the production of flywheel at CPC foundry- located at Tamilnadu- India, which provides a framework to identify, quantify and eliminate sources of variation in an operational process to reduce defects in the castings and on time delivery to increase the customer satisfaction. This research defines a step-by-step guide using DMAIC methodology and it is evaluated with a case study resulting in an overall defect rejection percentage decreased from 13.73 to 4.68 and sigma level of the process has been increased from 3.19σ to 3.65σ. The effect of job satisfaction of employees on six sigma implementation was also enhanced and the study generated an 83 percent responses rate from 72 employees with purpose to increase the sigma level of the company. Therefore, the objective of this study is to improve the sigma level, on time delivery of products to maximize the customer satisfaction. In the proceeding stage, FMEA and design of experiment (DOE) were conducted to determine risk factors and optimal settings of the critical-to quality factors in the casting process. The performance measurement system was also employed to justify the six sigma implementation procedure to improve the confidence level of the company regarding customer satisfaction. Based on the findings the improvement steps was taken and applied effectively thereby better yield is obtained in the production process. Finally, the higher sigma level and customer satisfaction were achieved and standardizes the methodology after analyzing, optimizing the process variables. The results shows that the proposed six sigma can effectively implemented to achieve higher level of customer satisfaction and also its gain. This implementation model experiences along with directions for future research are also provided.

Keywords

Six Sigma, DMAIC, Pareto, Ishikawa Diagram, Job Satisfaction, Taquichi Technique,FMEA, Performance Measurement.

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G. Senthil Kumar ¹, U. Natarajan ², G. Veera Senthil Kumar ³

Affiliations
1 Department of Mechanical Engineering, Velammal College of Engineering and Technology, Madurai-625009, IN
2 Department of Mechanical Engineering, A.C College of Engineering and Technology, Karaikudi, IN
3 Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai-625009, IN

Abstract

In general, a variety of vision inspection system is used to detect the surface defects such as problem of inaccuracy in images, non-uniform illumination, noise and deficient contrast in welding joints. In this work, a new machine vision inspection system is introduced to inspect the quality level for imperfection of Metal Inert Gas (MIG) welded joints. In this proposed system, images of welded surfaces are captured through CCD camera. From these images, the regions of interest are segmented and features using principal component of the images (Eigen vector) are extracted. Principal component analysis provides good dimensionality reduction than other features. This procedure is repeated for four different types of welding joints. Finally, welded joints are classified using Differential Evolution Algorithm based Artificial Neural Networks (ANN). Eigen vectors of images are considered as input of ANN and different types of welded joints are considered as output of network. In this work, welding standard EN25817 is considered for surface quality level for imperfections. Differential Evolution Algorithm (DEA) based Artificial Neural Network is population based search algorithm, which is an improved version of genetic algorithm. It is found to be faster and robust in optimization. The result of this proposed system is 98.15 in overall accuracy level. This proposed system assures that convergence rate of DEA based ANN holds goods.

Keywords

Principal Component Analysis (PCA), Weld Classification, Back Propagation Neural Network (BPNN), Differential Evolution Algorithm (DEA), Multi-Layer Perception (MLP).

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P. R. Periyanan ¹, U. Natarajan ², P. V. Shasi Kumar ³, M. Chellamalai ³, S. Gopikrishna ³

Affiliations
1 Sudarshasan Engineering College, IN
2 A C College of Engineering & Technology, IN
3 Central Manufacturing Technology Institute, Bangalore, IN

Abstract

There is a growing demand for industrial products not only with increased number of functions but also of reduced dimensions. Micro-machining is an emerging technology for the production of such miniaturized parts and components. In this present trend, new fabrication methods for producing miniaturized components are gaining popularity due to the recent advancements in micro electro mechanical systems (MEMS). Micro manufacturing include both conventional and nonconventional material removal process. The conventional machining process like micro-drilling, micro-milling etc. are used in fabricating micro components. The non-conventional process includes etching, various lithographic techniques like LIGA and laser micro machining etc. This literature report focuses on the recent trends in micro machining processes, process parameters and its quality characteristics.

Keywords

Micromachining, Cutting Tools, Cutting Forces, Cutting Fluid, Tool Wear.

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S. Jayabal ¹, U. Natarajan ¹, S. Sathiyamurthy ², V. Thirukumaran ³, M. Chellamalai ⁴

Affiliations
1 Dept. of Mechanical Engg., A. C. College of Engg. and Technology, Karaikudi, IN
2 Dept. of Mechanical Engg., Dhaanish Ahmed College of Engg., Chennai, IN
3 Dept. of Mechanical Engg., Sri Raaja Raajan College of Engg. and Technology, Karaikudi, IN
4 Central Manufacturing Technology Institute, Bangalore, IN

Abstract

The development of natural fiber composites in India is based on a two-pronged strategy of preventing depletion of forest resources as well as ensuring good economic returns for the cultivation of natural fibers. This paper analyzed the possible applications of coir fiber reinforced composites for manufacturing Engineering components. The coir fiber reinforced composites were manufactured using hand lay-up process using treated coir fibers and polyester resin matrix. The three mechanical properties such as tensile strength, flexural strength and Impact strength were tested as per ASTM standards. The manufactured engineering components are presented and possible applications are also suggested in this investigation.

Keywords

Tensile Strength, Flexural Strength, Impact Strength, Polyester Resin.

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S. Sathiyamurthy ¹, A. Syed Abu Thaheer ², U. Natarajan ², P. V. Shashikumar ³, M. Chellamalai ³

Affiliations
1 A C College of Engg. and Technology, Karaikudi, IN
2 Dept. of Mechanical Engineering, A C College of Engg. and Technology, Karaikudi, IN
3 Central Manufacturing Technology Institute, Tumkur Road, Bangalore, IN

Abstract

In this paper, the influence of micro-EDM-drilling process variables on surface roughness using an integrated multi-process micro machining machine (IMMM) is presented. The machining parameters considered are feed rate, capacitance & voltage and response parameter is surface roughness. Experiments are designed as per full factorial design using Design of experiments. Response surface methodology approach is used for modeling the surface roughness and finding out the optimal values of drilling parameters. The influence of each drilling parameter in micro-EDM process is characterized and the optimum value of parameters is determined. The interaction effects of parameters on surface roughness are discussed and the mathematical model and optimized parameters are validated for micro-EDM-drilling process.

Keywords

Surface Roughness, Full Factorial Design, Response Surface Methodology Micro-EDM.

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