Manjunath Patel | National Institute of Technology Karnataka,Surathkal (original) (raw)

Papers by Manjunath Patel

Advances in Materials and Processing Technologies, 2021

In aluminum alloy, Titanium carbide (TiC) reinforcements act as nucleation sites and refine grain... more In aluminum alloy, Titanium carbide (TiC) reinforcements act as nucleation sites and refine grain structure during solidification. Al-TiC particulate composites find major applications in structural and automobile parts due to their attractive features such as high elastic modulus and hardness. Therefore, attempts are made in the present work to know the fracture behavior of metal matrix composites prepared with varied fraction of reinforcement particles (3 wt%, 5 wt%, 7 wt% of TiC) under various notch sizes of the compact tension (CT) specimen. Al6061-TiC MMCs compact tension specimens are prepared as per ASTM E399 standards, to examine the fracture toughness (KIC). The CT specimens are machined to obtain the desired geometry i.e. crack to width (a/W) ratio of 0.3, 0.4 and 0.5 and thickness to width (B/W) ratio of 0.3, 0.4, and 0.5. Taguchi L9 orthogonal array experiments are conducted to study the effect of the factors (TiC reinforcements, a/W and B/W) on the performance of fractu...

Advances in Materials and Processing Technologies, 2021

Metal matrix composites (MMCs) processed with significant technological interventions offer excel... more Metal matrix composites (MMCs) processed with significant technological interventions offer excellent properties at reduced cost resulted in a wide range of engineering applications. In MMCs, ensur...

Materials Today: Proceedings, 2021

Abstract Sustainable machining of metallic parts offers significant economic, environmental and s... more Abstract Sustainable machining of metallic parts offers significant economic, environmental and societal benefits to metal cutting industries. Although dry machining technology bring sustainability in machining industries but requires significant attention to get high productivity without affecting the geometrical accuracy and surface finish. The present work proposed a unified framework to model and optimize the process, when applied to sustainability. A CNC machine is used to turn the mild steel parts with the help of carbide inserts (TNMG 160408CQ). Taguchi L27 orthogonal array is used to model the turning process and analyze the factors (cutting speed, depth of cut and feed rate) on material removal rate, MRR, surface roughness, SR and circularity error, CE. Further, attempts are made to optimize the sustainability index taken into account of economic (higher MRR), social (desired lower SR and CE) and environmental (dry machining) aspects. To attain the said goal two popular optimization models (data envelopment analysis ranking, DEAR and multi-objective optimization on the basis of ratio analysis, MOORA) are considered. The said models are compared among themselves and proposed a single optimal condition for the turning process.

Advances in Materials and Processing Technologies, 2020

The microstructure and mechanical attributes of the friction stir welded aluminium metal matrix c... more The microstructure and mechanical attributes of the friction stir welded aluminium metal matrix composites (AMCs) are reported in this paper. Impacts of friction stir welding (FSW) process variables on the mechanical properties are evaluated. Metallographic studies showed that variation in welding process variables' plays a vital role in obtaining recrystallised equiaxed fine-grain structures. The formed joint region indicates a gradual reduction in grain size as it moves from top to bottom of the weld region due to variation in the heat generation. Process variables like tool movement along the joint direction and tool revolution speed govern the joint strength of AMCs. Beyond the optimum values of process variables, the weld quality and joint strength of the welded part deteriorate due to the inappropriate stirring of the material at the weld region. The highest joint strength obtained for tool movement along the direction was 80 mm/min, and the revolution of the tool was 1000 rpm.

Australian Journal of Mechanical Engineering, 2020

The consumption of fossil fuels is continuously increasing due to large number of automobiles and... more The consumption of fossil fuels is continuously increasing due to large number of automobiles and high demand for energy. Fossil fuels will not only become extinct in near future, but also pollute the environment. The present research work is focused on finding an eco-friendly alternate energy source through the production of bio-diesel. Naturally available low-cost Garcinia Gummi-Gutta seed was used to produce the biodiesel employing the transesterification process widely utilised in industries. The maximum biodiesel yield was primarily dependent on the optimisation of transesterification parameters (reaction time, percent of methanol and sodium hydroxide). Response Surface Methodologies (RSM) such as Box-Behnken design (BBD) and Central composite design (CCD) were used to conduct the experiments and analyse the transesterification parameters relationship with biodiesel yield. Results showed that, the methanol contribution was more as compared to sodium hydroxide and reaction time towards the yield of the biodiesel. The mathematical regression equations developed, related to biodiesel yield and transesterification parameters by utilising both the CCD and BBD models. The performance of BBD was found to be slightly better than that of CCD in making prediction of the biodiesel yield. Experimentally, the Teaching Learning-based Optimisation (TLBO) determined optimal transesterification parameters resulting to 96.2% biodiesel yield.

Machining of Hard Materials, 2020

Planning and conducting experiments is the key in effective monitoring of system, which leads to ... more Planning and conducting experiments is the key in effective monitoring of system, which leads to success in manufacturing. The traditional approach of experimental study (i.e. one factor at a time, OFAT) requires more number of experiments and consequently consumes more resources. Moreover, the interpretations and analysis that can be made from the experimental data are also limited. Design of experiments (DOE) is a statistical tool, which uses well-planned set of experiments to collect the input–output data. Further, DOE can be used to analyse the experimental data, establish input–output relations, and optimize the process. Figure 3.1 shows the general steps followed in designing a statistical-based experiment.

International Journal of Computational Materials Science and Surface Engineering, 2019

In recent past, adhesive bonding gain much attention worldwide in joining of engineered parts nam... more In recent past, adhesive bonding gain much attention worldwide in joining of engineered parts namely, automotive and aerospace structures. The strength of adhesive bonded composite joints is studied by conducting experiments based on the matrices of central composite design. The collected data was analysed using response surface methodology. The mathematical model was established to express the load carrying capacity and joint strength as a function of input variables. Further, analysis of variance was carried out to ensure good fit to the experimental data. Moreover, the statistical methods determine significant interaction effects among the factors. Finally, genetic algorithm was used to locate the optimum points of joint strength for the set of inputs, i.e., 40 mm overlap length, 0.2 mm adhesive thickness and 4.526 μm surface roughness. The results showed that adhesively bonded single lap joints strength was influenced by overlap length (8%), adhesive thickness (78.56%), and surface roughness (1.43%). Statistical experimental design techniques were found to be useful in understanding the complex relationships seen in the data, and also in interpreting the results.

Measurement, 2019

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Critical Developments and Applications of Swarm Intelligence

This chapter is focused to locate the optimum squeeze casting conditions using evolutionary swarm... more This chapter is focused to locate the optimum squeeze casting conditions using evolutionary swarm intelligence and teaching learning-based algorithms. The evolutionary and swarm intelligent algorithms are used to determine the best set of process variables for the conflicting requirements in multiple objective functions. Four cases are considered with different sets of weight fractions to the objective function based on user requirements. Fitness values are determined for all different cases to evaluate the performance of evolutionary and swarm intelligent methods. Teaching learning-based optimization and multiple-objective particle swarm optimization based on crowing distance have yielded similar results. Experiments have been conducted to test the results obtained. The performance of swarm intelligence is found to be comparable with that of evolutionary genetic algorithm in locating the optimal set of process variables. However, TLBO outperformed GA, PSO, and MOPSO-CD with regard ...

Applied Soft Computing, 2017

Input-output model for squeeze casting process Forward mapping

Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2017

The present research work is focussed on establishing the complex nonlinear input–output relation... more The present research work is focussed on establishing the complex nonlinear input–output relations of a furan resin-based molding sand system. Further, a set of input parameters, which will result in optimized mold properties, is determined. Grain fineness number, setting time, percentage of resin, and hardener are considered as process variables. Mold properties, such as green compression strength, shear strength, mold hardness, gas evolution, permeability, and collapsibility are treated as the process outputs. Nonlinear input–output relations have been developed and statistical analysis has been carried out by utilizing design of experiments, central composite design. Surface plots are developed to study and analyze the input–output relations. The input parameters that will result in best molding conditions and improve casting quality characteristics are determined by utilizing desirability function approach and multiple particle swarm optimization-based crowding distance (MOPSO-C...

International Journal of Advances in Engineering Sciences, May 17, 2013

Artificial Neural networks (ANNs) have potential challenges in the eve of prediction, optimizatio... more Artificial Neural networks (ANNs) have potential challenges in the eve of prediction, optimization, control, monitor, identification, classification, modelling and so on particularly in the field of manufacturing. This paper presents a selective review on use of ANNs in the application of casting and injection moulding processes. We discuss number of key issues which must be addressed when applying neural networks to practical problems and steps followed for the development of such models are also outlined. These includes data collection, division of data collected and pre-processing of available data, selection of appropriate model inputs, outputs, network architectures, network parameters, training algorithms, learning scheme, training modes, network topology defined, training termination, choice of performance criteria for training and model validations. The suitable options available for network developers were discussed and recommended suggestions to be considered are highlighted. Keywords: Metal casting processes, Injection moulding processes, Casting Materials/Alloys, Artificial Neural Networks

Advances in Automobile Engineering, 2015

The growing demand in today's competitive manufacturing environment has encouraged the researcher... more The growing demand in today's competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology.

Applied Computational Intelligence and Soft Computing, 2014

The present research work is focussed to develop an intelligent system to establish the input-out... more The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equati...

Procedia Technology, 2014

The near net shape manufacturing capability of squeeze casting process have the potential to prod... more The near net shape manufacturing capability of squeeze casting process have the potential to produce high dense components with refined micro-structure. However, squeeze cast micro-structure is influenced by large number of process variables such as squeeze pressure, time delay, pressure duration, die temperature and pouring temperature. In the present work, an attempt is made to develop the model by considering aforementioned process variables. Further, significant contribution of each process parameter on the secondary dendrite arm spacing is studied by using statistical regression tool. The mathematical relationship has been developed for secondary dendrite arm spacing was used to generate the training data artificially at random and tested with the help of few test cases. It is to be noted that the test cases chosen were different from training data. Scaled conjugate gradient, Levenberg-Marquardt algorithm and regression model predictions were compared. It is interesting to note that, all models were capable to make good prediction with an average of 5 percentage deviation. Levenberg-Marquardt algorithm outperforms in terms of prediction compared to other models in the present work. The reason might be due to the nature of error surface.

Procedia Technology, 2014

The near-net shape manufacturing capabilities of squeeze casting process have greater potential t... more The near-net shape manufacturing capabilities of squeeze casting process have greater potential to achieve smooth uniform surface and internal soundness in the cast components. In squeeze casting process, casting density and surface finish is influenced majorly by process variables. Proper control of the process variables is essential to achieve better results. Hence in the present work an attempt made using taguchi method to analyze the squeeze cast process variables such as squeeze pressure, die and pouring temperature considering at three different levels using L 9 orthogonal array. Pareto analysis of variance performed on each response to find out optimum process parameter levels and significant contribution of each individual process parameter towards surface roughness and density of LM20 alloy. Grey relation analysis used as a multi-response optimization technique to obtain the single optimal process parameter setting for both the responses surface roughness and casting density.

Journal for Manufacturing Science and Production, 2014

In the present work, efforts are made to develop the input-output relationships for squeeze casti... more In the present work, efforts are made to develop the input-output relationships for squeeze casting process by utilizing the fuzzy logic based approaches. Casting density in Squeeze casting is expressed as function of process parameters, such as time delay before pressurizing the metal, pressure durations, squeeze pressure, pouring temperature and die temperature. It is to be noted that, Mamdani based model and Takagi and Sugeno's model have been developed to model density in squeeze casting process. Manually constructed Mamdani based fuzzy logic controller and Takagi and Sugeno's based fuzzy logic controller have been used in approach 1 and approach 2 respectively. Training of FLC is carried with the help of five hundred input-output data set generated artificially through regression equations, obtained earlier by the same authors. The performance of the developed models was tested for both the linear and non-linear membership function distributions with the help of ten tes...

Frattura ed Integrità Strutturale

Increased material demand in all sectors is primarily due to exponential growth in population to ... more Increased material demand in all sectors is primarily due to exponential growth in population to fulfill human needs and comforts. Recycling of collected aluminium beverage cans and Al 6061 alloy scraps from industries ensures energy savings with reduced environmental problems in fabricating composite parts economically. The iron oxide (α-Fe2O3) nanoparticles were prepared by precipitation method using ferric chloride and ammonia as a precursor. The prepared nanoparticles were characterized by using Transmission Electron Microscope (TEM), X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR). Stir cast processing route ensures uniform mix of reinforcement nanoparticles in matrix material. The prepared nanocomposites (matrix: Al Scrap (90% Scrap Al 6061 alloy + 10% Waste Al can); reinforcement: 2%, 4% and 6% wt. of Al matrix) were mechanically characterized for hardness and tensile strengths. It was observed that, increased percent of Fe2O3 nanoparticles in the metal matrix n...

Advanced Materials Research, Jan 1, 2012

... and Cobby Ang Teck Khong, Development of a hybrid neural network system for prediction of pro... more ... and Cobby Ang Teck Khong, Development of a hybrid neural network system for prediction of process parameters in injection moulding, Journal of Materials Processing Technology, 2001, p. 110-116. [3]. Wen-Chin Chen, Gong-Loung Fu, Pei-Hao Tai and Wei-Jaw Deng ...

Advances in Materials and Processing Technologies, 2021

In aluminum alloy, Titanium carbide (TiC) reinforcements act as nucleation sites and refine grain... more In aluminum alloy, Titanium carbide (TiC) reinforcements act as nucleation sites and refine grain structure during solidification. Al-TiC particulate composites find major applications in structural and automobile parts due to their attractive features such as high elastic modulus and hardness. Therefore, attempts are made in the present work to know the fracture behavior of metal matrix composites prepared with varied fraction of reinforcement particles (3 wt%, 5 wt%, 7 wt% of TiC) under various notch sizes of the compact tension (CT) specimen. Al6061-TiC MMCs compact tension specimens are prepared as per ASTM E399 standards, to examine the fracture toughness (KIC). The CT specimens are machined to obtain the desired geometry i.e. crack to width (a/W) ratio of 0.3, 0.4 and 0.5 and thickness to width (B/W) ratio of 0.3, 0.4, and 0.5. Taguchi L9 orthogonal array experiments are conducted to study the effect of the factors (TiC reinforcements, a/W and B/W) on the performance of fractu...

Advances in Materials and Processing Technologies, 2021

Metal matrix composites (MMCs) processed with significant technological interventions offer excel... more Metal matrix composites (MMCs) processed with significant technological interventions offer excellent properties at reduced cost resulted in a wide range of engineering applications. In MMCs, ensur...

Materials Today: Proceedings, 2021

Abstract Sustainable machining of metallic parts offers significant economic, environmental and s... more Abstract Sustainable machining of metallic parts offers significant economic, environmental and societal benefits to metal cutting industries. Although dry machining technology bring sustainability in machining industries but requires significant attention to get high productivity without affecting the geometrical accuracy and surface finish. The present work proposed a unified framework to model and optimize the process, when applied to sustainability. A CNC machine is used to turn the mild steel parts with the help of carbide inserts (TNMG 160408CQ). Taguchi L27 orthogonal array is used to model the turning process and analyze the factors (cutting speed, depth of cut and feed rate) on material removal rate, MRR, surface roughness, SR and circularity error, CE. Further, attempts are made to optimize the sustainability index taken into account of economic (higher MRR), social (desired lower SR and CE) and environmental (dry machining) aspects. To attain the said goal two popular optimization models (data envelopment analysis ranking, DEAR and multi-objective optimization on the basis of ratio analysis, MOORA) are considered. The said models are compared among themselves and proposed a single optimal condition for the turning process.

Advances in Materials and Processing Technologies, 2020

The microstructure and mechanical attributes of the friction stir welded aluminium metal matrix c... more The microstructure and mechanical attributes of the friction stir welded aluminium metal matrix composites (AMCs) are reported in this paper. Impacts of friction stir welding (FSW) process variables on the mechanical properties are evaluated. Metallographic studies showed that variation in welding process variables' plays a vital role in obtaining recrystallised equiaxed fine-grain structures. The formed joint region indicates a gradual reduction in grain size as it moves from top to bottom of the weld region due to variation in the heat generation. Process variables like tool movement along the joint direction and tool revolution speed govern the joint strength of AMCs. Beyond the optimum values of process variables, the weld quality and joint strength of the welded part deteriorate due to the inappropriate stirring of the material at the weld region. The highest joint strength obtained for tool movement along the direction was 80 mm/min, and the revolution of the tool was 1000 rpm.

Australian Journal of Mechanical Engineering, 2020

The consumption of fossil fuels is continuously increasing due to large number of automobiles and... more The consumption of fossil fuels is continuously increasing due to large number of automobiles and high demand for energy. Fossil fuels will not only become extinct in near future, but also pollute the environment. The present research work is focused on finding an eco-friendly alternate energy source through the production of bio-diesel. Naturally available low-cost Garcinia Gummi-Gutta seed was used to produce the biodiesel employing the transesterification process widely utilised in industries. The maximum biodiesel yield was primarily dependent on the optimisation of transesterification parameters (reaction time, percent of methanol and sodium hydroxide). Response Surface Methodologies (RSM) such as Box-Behnken design (BBD) and Central composite design (CCD) were used to conduct the experiments and analyse the transesterification parameters relationship with biodiesel yield. Results showed that, the methanol contribution was more as compared to sodium hydroxide and reaction time towards the yield of the biodiesel. The mathematical regression equations developed, related to biodiesel yield and transesterification parameters by utilising both the CCD and BBD models. The performance of BBD was found to be slightly better than that of CCD in making prediction of the biodiesel yield. Experimentally, the Teaching Learning-based Optimisation (TLBO) determined optimal transesterification parameters resulting to 96.2% biodiesel yield.

Machining of Hard Materials, 2020

Planning and conducting experiments is the key in effective monitoring of system, which leads to ... more Planning and conducting experiments is the key in effective monitoring of system, which leads to success in manufacturing. The traditional approach of experimental study (i.e. one factor at a time, OFAT) requires more number of experiments and consequently consumes more resources. Moreover, the interpretations and analysis that can be made from the experimental data are also limited. Design of experiments (DOE) is a statistical tool, which uses well-planned set of experiments to collect the input–output data. Further, DOE can be used to analyse the experimental data, establish input–output relations, and optimize the process. Figure 3.1 shows the general steps followed in designing a statistical-based experiment.

International Journal of Computational Materials Science and Surface Engineering, 2019

In recent past, adhesive bonding gain much attention worldwide in joining of engineered parts nam... more In recent past, adhesive bonding gain much attention worldwide in joining of engineered parts namely, automotive and aerospace structures. The strength of adhesive bonded composite joints is studied by conducting experiments based on the matrices of central composite design. The collected data was analysed using response surface methodology. The mathematical model was established to express the load carrying capacity and joint strength as a function of input variables. Further, analysis of variance was carried out to ensure good fit to the experimental data. Moreover, the statistical methods determine significant interaction effects among the factors. Finally, genetic algorithm was used to locate the optimum points of joint strength for the set of inputs, i.e., 40 mm overlap length, 0.2 mm adhesive thickness and 4.526 μm surface roughness. The results showed that adhesively bonded single lap joints strength was influenced by overlap length (8%), adhesive thickness (78.56%), and surface roughness (1.43%). Statistical experimental design techniques were found to be useful in understanding the complex relationships seen in the data, and also in interpreting the results.

Measurement, 2019

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Critical Developments and Applications of Swarm Intelligence

This chapter is focused to locate the optimum squeeze casting conditions using evolutionary swarm... more This chapter is focused to locate the optimum squeeze casting conditions using evolutionary swarm intelligence and teaching learning-based algorithms. The evolutionary and swarm intelligent algorithms are used to determine the best set of process variables for the conflicting requirements in multiple objective functions. Four cases are considered with different sets of weight fractions to the objective function based on user requirements. Fitness values are determined for all different cases to evaluate the performance of evolutionary and swarm intelligent methods. Teaching learning-based optimization and multiple-objective particle swarm optimization based on crowing distance have yielded similar results. Experiments have been conducted to test the results obtained. The performance of swarm intelligence is found to be comparable with that of evolutionary genetic algorithm in locating the optimal set of process variables. However, TLBO outperformed GA, PSO, and MOPSO-CD with regard ...

Applied Soft Computing, 2017

Input-output model for squeeze casting process Forward mapping

Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2017

The present research work is focussed on establishing the complex nonlinear input–output relation... more The present research work is focussed on establishing the complex nonlinear input–output relations of a furan resin-based molding sand system. Further, a set of input parameters, which will result in optimized mold properties, is determined. Grain fineness number, setting time, percentage of resin, and hardener are considered as process variables. Mold properties, such as green compression strength, shear strength, mold hardness, gas evolution, permeability, and collapsibility are treated as the process outputs. Nonlinear input–output relations have been developed and statistical analysis has been carried out by utilizing design of experiments, central composite design. Surface plots are developed to study and analyze the input–output relations. The input parameters that will result in best molding conditions and improve casting quality characteristics are determined by utilizing desirability function approach and multiple particle swarm optimization-based crowding distance (MOPSO-C...

International Journal of Advances in Engineering Sciences, May 17, 2013

Artificial Neural networks (ANNs) have potential challenges in the eve of prediction, optimizatio... more Artificial Neural networks (ANNs) have potential challenges in the eve of prediction, optimization, control, monitor, identification, classification, modelling and so on particularly in the field of manufacturing. This paper presents a selective review on use of ANNs in the application of casting and injection moulding processes. We discuss number of key issues which must be addressed when applying neural networks to practical problems and steps followed for the development of such models are also outlined. These includes data collection, division of data collected and pre-processing of available data, selection of appropriate model inputs, outputs, network architectures, network parameters, training algorithms, learning scheme, training modes, network topology defined, training termination, choice of performance criteria for training and model validations. The suitable options available for network developers were discussed and recommended suggestions to be considered are highlighted. Keywords: Metal casting processes, Injection moulding processes, Casting Materials/Alloys, Artificial Neural Networks

Advances in Automobile Engineering, 2015

The growing demand in today's competitive manufacturing environment has encouraged the researcher... more The growing demand in today's competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology.

Applied Computational Intelligence and Soft Computing, 2014

The present research work is focussed to develop an intelligent system to establish the input-out... more The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equati...

Procedia Technology, 2014

The near net shape manufacturing capability of squeeze casting process have the potential to prod... more The near net shape manufacturing capability of squeeze casting process have the potential to produce high dense components with refined micro-structure. However, squeeze cast micro-structure is influenced by large number of process variables such as squeeze pressure, time delay, pressure duration, die temperature and pouring temperature. In the present work, an attempt is made to develop the model by considering aforementioned process variables. Further, significant contribution of each process parameter on the secondary dendrite arm spacing is studied by using statistical regression tool. The mathematical relationship has been developed for secondary dendrite arm spacing was used to generate the training data artificially at random and tested with the help of few test cases. It is to be noted that the test cases chosen were different from training data. Scaled conjugate gradient, Levenberg-Marquardt algorithm and regression model predictions were compared. It is interesting to note that, all models were capable to make good prediction with an average of 5 percentage deviation. Levenberg-Marquardt algorithm outperforms in terms of prediction compared to other models in the present work. The reason might be due to the nature of error surface.

Procedia Technology, 2014

The near-net shape manufacturing capabilities of squeeze casting process have greater potential t... more The near-net shape manufacturing capabilities of squeeze casting process have greater potential to achieve smooth uniform surface and internal soundness in the cast components. In squeeze casting process, casting density and surface finish is influenced majorly by process variables. Proper control of the process variables is essential to achieve better results. Hence in the present work an attempt made using taguchi method to analyze the squeeze cast process variables such as squeeze pressure, die and pouring temperature considering at three different levels using L 9 orthogonal array. Pareto analysis of variance performed on each response to find out optimum process parameter levels and significant contribution of each individual process parameter towards surface roughness and density of LM20 alloy. Grey relation analysis used as a multi-response optimization technique to obtain the single optimal process parameter setting for both the responses surface roughness and casting density.

Journal for Manufacturing Science and Production, 2014

In the present work, efforts are made to develop the input-output relationships for squeeze casti... more In the present work, efforts are made to develop the input-output relationships for squeeze casting process by utilizing the fuzzy logic based approaches. Casting density in Squeeze casting is expressed as function of process parameters, such as time delay before pressurizing the metal, pressure durations, squeeze pressure, pouring temperature and die temperature. It is to be noted that, Mamdani based model and Takagi and Sugeno's model have been developed to model density in squeeze casting process. Manually constructed Mamdani based fuzzy logic controller and Takagi and Sugeno's based fuzzy logic controller have been used in approach 1 and approach 2 respectively. Training of FLC is carried with the help of five hundred input-output data set generated artificially through regression equations, obtained earlier by the same authors. The performance of the developed models was tested for both the linear and non-linear membership function distributions with the help of ten tes...

Frattura ed Integrità Strutturale

Increased material demand in all sectors is primarily due to exponential growth in population to ... more Increased material demand in all sectors is primarily due to exponential growth in population to fulfill human needs and comforts. Recycling of collected aluminium beverage cans and Al 6061 alloy scraps from industries ensures energy savings with reduced environmental problems in fabricating composite parts economically. The iron oxide (α-Fe2O3) nanoparticles were prepared by precipitation method using ferric chloride and ammonia as a precursor. The prepared nanoparticles were characterized by using Transmission Electron Microscope (TEM), X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR). Stir cast processing route ensures uniform mix of reinforcement nanoparticles in matrix material. The prepared nanocomposites (matrix: Al Scrap (90% Scrap Al 6061 alloy + 10% Waste Al can); reinforcement: 2%, 4% and 6% wt. of Al matrix) were mechanically characterized for hardness and tensile strengths. It was observed that, increased percent of Fe2O3 nanoparticles in the metal matrix n...

Advanced Materials Research, Jan 1, 2012

... and Cobby Ang Teck Khong, Development of a hybrid neural network system for prediction of pro... more ... and Cobby Ang Teck Khong, Development of a hybrid neural network system for prediction of process parameters in injection moulding, Journal of Materials Processing Technology, 2001, p. 110-116. [3]. Wen-Chin Chen, Gong-Loung Fu, Pei-Hao Tai and Wei-Jaw Deng ...