Manufacturing Technology Today

P. Srinivasa Pai ¹, Rashmi P. Shetty ¹

Affiliations
1 Department of Mechanical Engineering, NMAM Institute of Technology, Nitte, Karnataka, IN

Abstract

Tool condition Monitoring is an important aspect to maintain quality of machined products. A tool is considered as failed, when it is no longer able to produce parts within the required specifications. Tool Condition Monitoring Systems (TCMS) are an integral part of machine condition monitoring systems, which continuously monitor the condition of the tool either using 'direct' or 'indirect' methods. In this paper, acoustic emission signals, which are widely used in TCMS are used along with surface roughness measurements to monitor the condition of an insert in face milling operations on grey cast iron. Artificial Neural Networks (ANN), a widely used artificial intelligence technique in machining applications have been used to model tool flank wear using acoustic emission signal parameters, surface roughness parameters and cutting conditions. Three ANN techniques have been investigated and compared namely Multi-layer Perceptron Neural Network (MLPNN), Radial basis function Neural Network (RBFNN) and Summation Wavelet - Extreme Learning Machine (SW-ELM). The results indicate that SW-ELM performs better than other two techniques in tool wear modeling and achieves a prediction accuracy of more than 85 % on test data.

Keywords

Tool Condition Monitoring, Acoustic Emission, Surface Roughness, Multi-Layer Perceptron, Radial Basis Function, Summation Wavelet-Extreme Learning Machine.

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N. P. Puneet ¹, P. Srinivasa Pai ¹, Grynal D’Mello ¹

Affiliations
1 Dept. of Mech. Engg., NMAM Institute of Technology, Nitte, IN

Abstract

This paper reviews the literature on use of optimization techniques in machining operations, particularly turning. Optimization techniques have undergone lot of changes and developments and include conventional methods like Weighted sum method, Ellipsoid algorithm etc. and advanced techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc. The review has been carried out considering different objectives like cost, cutting forces, production time and surface roughness. The review considers application of single method and combination of methods and its use in conventional and non-conventional material machining. Finally a case study which involves application of GA and PSO for optimizing surface roughness in high speed turning of mild steel is presented.

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P. Srinivasa Pai ¹, T. N. Nagabhushana ²

Affiliations
1 Dept. of Mech. Engg., NMAM Institute of Technology, Nitte, IN
2 Dept. of Information Science & Engg., S. J. College of Engg., Mysore, IN

Abstract

Tool Condition Monitoring (TCM) is a very important aspect to maintain quality of products manufactured in any machining process. Tool wear is a complex phenomenon occurring in different and varied ways in metal cutting processes. There is a need to identify the tool condition well in advance and take necessary action like replacement, to prevent damaged tools from negatively affecting production in terms of excessive power takeoff, inaccurate tolerances and uneven workpiece surface finish and some times damage to the machine tools and also injury to the operator. There is a need to reliably detect the condition of the tool and take necessary steps. Artificial Neural Networks (ANN) has been widely used for sensor fusion in TCM. This paper presents a brief overview of ANN and followed by it presents an overview of the application of ANN for tool condition monitoring and briefly to other manufacturing processes. Conclusions at the end of the paper contain a brief summary of some of the observations relevant to the application of ANN to TCM.

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P. Srinivasa Pai ¹, Bhaskara P. Achar ¹

Affiliations
1 Dept. of Mechanical Engineering, NMAM Institute of Technology, Nitte, IN

Abstract

Magnesium and its alloys are widely used in a number of applications including automotive and aerospace. Because of its biocompatibility, it is also being used in many medical applications including orthopedics. Machining is a commonly used manufacturing process to produce components. Magnesium because of its characteristics like light weight and other mechanical properties is the easiest to machine. But there are several issues related to its machining. There has been considerable research work going on outside India regarding machinability studies of magnesium alloys, but there have been very minimal efforts going on in India. In order to understand the various issues related to machining of magnesium alloys, this review is an honest effort to review the literature available regarding the same and classify them based on areas of relevance in machining and then draw some major conclusions from the review. The authors feel this review will help prospective researchers to get necessary information about machining of magnesium alloys.

Keywords

Magnesium Machining, Cryogenic Machining, Surface Integrity, Optimization, Cutting Temperature.

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P. Srinivasa Pai ¹, T. N. Nagabhushana ²

Affiliations
1 Dept. of Mechanical Engg., NMAM Institute of Technology, Nitte-574110, IN
2 Dept. of Information Science & Engg., S.J. College of Engineering, Mysore-570006, IN

Abstract

This paper attempts to monitor the surface roughness caused by the increase of tool wear (flank wear), through the variations of acoustic emission in face milling operations under different cutting conditions and for one workpiece material. The analysis revealed that there is a good correlation between two important parameters of the acoustic emission signal namely ring down count and RMS voltage with the surface roughness parameter namely R_a. The results show that acoustic emission can be used as an effective signal for monitoring surface roughness in face milling and thereby can be useful for establishing the end of tool life in face milling operations. Artificial Neural Network (ANN) is a powerful tool that can be applied to many scientific and engineering problems. In this paper a simple feed forward ANN has been applied to predict surface roughness using acoustic emission parameters, flank wear and cutting conditions.

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P. Srinivasa Pai ¹

Affiliations
1 Dept. of Industrial & Production/Mech. Engg., NMAM Institute of Technology, Nitte-574110, Karnataka, IN

Abstract

This paper discusses about the need to have Tool Condition Monitoring (TCM) Systems for achieving automation in manufacturing processes. Based on the literature available, some of the important techniques of tool condition monitoring are discussed. The complexity of the metal cutting processes with different variables coming to play during the process, makes it impossible for use of a single technique for TCM. In order to improve the reliability of TCM systems for better monitoring of metal cutting processes, the need for 'Sensor Integration’ is emphasized. Artificial Neural Networks are one of the most widely used method for sensor integration. A brief overview of the same is presented next.

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Bhaskara P. Achar ¹, P. Srinivasa Pai ¹, G. Benaka ¹, Grynal D’mello ¹

Affiliations
1 Dept. of Mechanical Engineering, NMAM Institute of Technology, Nitte, IN

Abstract

Need for an efficient cutting tool and machining techniques are the prime requirements of modern machining industries. Certain domains such as defense, aerospace and automobile use the typical ‘difficult to cut materials’, which has specific applications. Materials’ machinability, tool life, surface finish achieved by the machining process, factors affecting surface quality and tool life are the key areas of investigations that researchers are focusing on. In this paper two difficult to machine materials namely Inconel 718 and Stellite 6 have been experimentally investigated for their machining characteristics. The effect of Flood and Jet cooling techniques on the machinability of these materials have been compared with dry machining, having a focus on tool wear, surface roughness, cutting temperature and cutting vibrations.

Keywords

Inconel 718, Stellite 6, Jet Cooling, Surface Finish, Cutting Temperature.

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Ganesha Prasad ¹, P. Srinivasa Pai ²

Affiliations
1 Department of Mechanical Engineering, Shri Devi Institute of Technology, Kenjar, Mangalore, Karnataka, IN
2 Department of Mechanical Engineering, NMAM Institute of Technology, Nitte, Karnataka, IN

Abstract

Machining processes have undergone lot of changes over the last several years to improve the quality and productivity. There are several important problems, faced by machinists when machining so called ‘difficult-to-machine’ materials, which includes metals, alloys, composites, rubber, refractory materials etc. These include excessive tool wear, temperature, severe work hardening, chip formation and breakage, bad surface finish and surface integrity. Cryogenic cooling is a relatively new cooling technique explored by researchers and industries all over the world to machine these materials and reduce the associated problems. There has been lot of research articles available in this domain. In this regard, this paper reviews research articles published during 2000 to 2018, with focus on introduction of cryogenics to machining, some details of difficult to machine materials, effect of cryogenics on surface finish, tool wear, chip formation and breakage, microstructure and hardness. About 108 articles have been reviewed and salient conclusions are drawn at the end of the paper, which establishes the fact that cryogenic machining is going to be an important machining technique, which will be adopted by industries to produce better quality products from machining of these materials and promote ‘sustainability in machining’.

Keywords

Cryogenic Machining, Difficult to Machine Materials, Tool Wear, Surface Roughness.

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P. Vasudeva Bhat ¹, P. Srinivasa Pai ¹

Affiliations
1 NMAM Institute of Technology, Nitte, Karnataka, IN

Abstract

There are lots of research happening in high speed machining especially for hard materials which find its applications in vast areas like aerospace, marine, automobiles etc. The main parameters which need to be taken care of while doing high speed machining are surface roughness and tool wear. To achieve high degree of surface finish and close tolerance in the work piece materials, care should be taken to select the right type of tool and lubrication. The conventional type flood lubrication system is found to be having more cost involved and also more time and work is involved in cleaning and disposal of used lubricants, which is also an ecological hazard. For high speed machining, reducing the temperature generated is the main concern by using suitable cooling system. In this regard Minimum Quantity Lubrication (MQL) has been found to be more effective compared to other lubrication systems. This paper presents a review of research work with regard to MQL and its systems with suitable additions for machining difficult to machine materials. An overview about different lubrication system types is presented, followed by some of the commonly used MQL systems. Further the different aspects related to use of MQL in different applications is presented, followed by the use of additives in MQL like use of nano particles is reviewed, to understand its effect on the performance of MQL. MQL with nano particles is more effective in reducing tool wear and surface roughness for all types of machining processes.

Keywords

Minimum Quantity Lubrication, Surface Roughness, Tool Wear, Difficult-To-Machine Materials, Nanoparticles.

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