19
Surface Roughness Optimisation by ALO in
CNC
Vinay Singh
1, Mr. Sanjeev
21
M.Tech. Scholar, CBS College, Jhajjar, Haryana, India
2
Assistant Professor, CBS College, Jhajjar, Haryana, India
ABSTRACT
The machining of aluminum metal matrix composites in CNC high speedconditions is significant because such composites have diverseapplications in the aeronautics industry. Because that industry requires high quality outcomes, the prediction of surfaceroughness, whichdepends on input process parameters, assumes significance in the maintaining quality of products. This work discusses the reducing the surface roughness of jobs used on CNC milling machine. For this purpose optimization algorithms are a choice which gives optimal set of values of CNC machine parameters which affect the surface roughness during milling operation. Form the literature study it is revealed that latest work on this is done by using gravitational search algorithm (GSA) and is compared with TLBO, SA and GA (in order of their performance). The values of surface roughness obtained with these are quite good but there is always scope of improvement. To get the optimal sets of input parameters of CNC machine, an experimental work done in the paper by Pare V. et.al. is considered as a reference. Their experiment gives an allowable range of speed of cutting, depth of cut, feed and step over ratio in which optimal values of these can be searched out by our proposed optimization algorithm.
INTRODUCTION
Metal cutting technology, or more specifically the machining technology, is part and parcel of any mechanical manufacturing facility. It is also considered as the most commonly employed metal shaping process. The term machining can be defined as a metal cutting process in which both the workpiece and the tool are held firm by a power-driven mechanical structure and the material removed from the workpiece in form of chips is instigated by the relative motion between tool and workpiece.
Machining process can be subdivided into following four major types, based upon the mode of material removal.
Turning.
Drilling.
Shaping/Planning.
Milling.
Among the above machining operations, turning operation is one of the most widely and commonly used cutting operation carried on a lathe machine
The modern lathe machine dates from about 1797, when Henry Maudsley produced a lathe with a lead screw that provided controlled mechanical feed of the tool.
Today mostly modern lathe machines are numerically controlled. This permits the operator to make tool setting and adjustments merely by the input of numerical data.
A manufacturing system is defined as, a complex arrangement of the following four physical elements;
Machine tool
20 People
Material handling equipments
The measureable output of a manufacturing system can be characterized by the following measurable parameters;
Productivity
Defects rate
Unit cost, etc
Machine tool, among above four physicalelements, in particular plays a very important role because its function is to add value in term of quality, cost and time in manufacturing.
Numerical control, NC, refers to the automated machine tool that is operated by coded programmed commands. With the development of technologies in computer, the NC machine was further developed to computer numerical control, CNC, machine in the 1970s. This development changed the NC machine to have A (4) automation level because of closed-loop feedback capability.
Milling is the process of machining flat, curved or irregular surface by feeding the workpiece against a rotating cutter containing a number of cutting edges. Milling process consists of a motor driven spindle, which mounts and revolves the milling cutter and a reciprocating adjustable worktable, which mounts and feeds the workpiece.Milling machines are basically classified as vertical or horizontal.
The workpiece is fastened to the milling machine table and is fed against the revolving milling cutter. The milling cutter can have cutting teeth on the periphery or side or both. Milling machine can be classified into three main headings:
i. General Purpose machines – these are mainly the column and knee type (horizontal and vertical machines). ii. High Production types with fixed beds – (horizontal types)
iii. Special purpose machines such as a duplicating, profiling, rise and fall, rotary table planetary and double end types.
LITERATURE SURVEY
1. In this paper, the cutting parameters for drilling EN24 material with the high speed steel drill are analyzed using Taguchi method. By using suitable cutting parameters like feed rate, speed and lip angle, the experiment is conducted and that the optimized cutting parameters are found with reference to the surface roughness of the part, metal removal rate and machining time for the operation.
2. In this paper the drilling of AL6463 aluminum alloy with the help of CNC drilling machine operation with Tool use high speed steel by applying Taguchi methodology has been reported. The purpose of this paper is to investigate the influence of cutting parameters, such as cutting speed and feed rate, and point angle on surface roughness, hole dia error and burr height produced when drilling AL6463 aluminum alloy.
3. In this paper, the online monitoring of the tool wear is carried out through vibration measurements. Neural network modeling of tool wear and MRR is used to determine the wear state and MRR with Speed, Feed and RMS of vibration signals as the input. Grey relational optimization determines the optimal machining parameters in on-line with changes in tool wear and MRR. A PID controller controls the spindle and feed motors of the CNC system based on the optimal values
4. In this paper, we present cutting tools movement by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) method in generation of shortest tool path. For observation of the performance of both methods, comparisons with conventional method have been carried out. The shortest path of drilling tool path adapts Travelling Salesman Problem (TSP) in determining the distance during machining
5. This paper presentsa review of applications of TLBO algorithm and a tutorial for solving the unconstrained andconstrained optimization problems.
6. An improved TLBO algorithm has been proposed for unconstrained optimization problems. Two new search mechanismsare introduced in the proposed approach in the form of tutorialtraining and self motivated learning. 7. Shape and size optimization of truss structures with multiple frequency constraints which is a highly nonlinear
21 PROBLEM DEFINATION
Surface roughness is one of the most important parameters to determine the quality of a product. Surface roughness consists of the fine irregularities of the surface texture, including feed marks generated by the machining process. The quality of a surface is significantly important factor in evaluating the productivity of machine tool and machined parts. The mechanism behind the formation of surface roughness is very dynamic, complicated, and process dependent. Several factors will influence the final surface roughness in a CNC end milling operation such as controllable factors like CNC cutting speed, feed, depth of cut and step over ratio. Because of these dependencies researchers used several optimisation algorithms and latest gravitational search algorithm (GSA) was used to tune these parameters for optimum surface roughness of material. It has been seen that although GSA gave better results than rest four but number of iterations required in GSA was very high so the convergence time. Whereas TLBO (teacher learner based algorithm) algorithm gives almost equal result (though less than GSA) but convergence time is very less. So in our work we will try to remove this issue with less convergence time and better surface roughness. Keeping this in view followings will be our key objectives:
To achieve the highest surface roughness, CNC cutting speed, feed, depth of cut and step over ratio will be tuned within their constraint limits for Al SiC composite.
To control these independent parameters, a Ant Lion optimisation (ALO) algorithm will be used.
To check the results after establishing a regression relationship in dependent and independent parameters with variable number of independent parameters and will be compared with the reference paper.
PROPOSED SOLUTION
In this work we will towards improving the surface roughness of aluminum silicon carbide (Al-SiC) composite using CNC milling machine. Our work will mainly focused on the optimizing the machine parameters to get the maximum surface smoothness of job.
To optimize input variables of CNC machine to get minimum surface roughness, gravitational search algorithm (GSA) has been used previously and tested for teacher learner based optimization (TLBO) too. Both these algorithms gave comparative results but the convergence time for TLBO is very less than GSA whereas GSA’s result is better than TLBO. So in our work we will use a method to reduce more surface roughness with less convergence time.
Algorithm & Techniques
Ant lion optimizer(ALO) is new meta heuristic algorithm.This is motivated from the hunting mechanism of ant lions .Inherit steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building are simulated to find the optimal solution of real life problems. The ALO algorithm mimics interaction between antlions and ants in the trap. To model such interactions, ants are required to move over the search space, and antlions are allowed to hunt them and become fitter using traps.
Gravitational Search Algorithm (GSA) was introduced by Rashedi et al. in 2009 and is intended to solve optimization problems. The population-based heuristic algorithm is based on the law of gravity and mass interactions. The algorithm is comprised of collection of searcher agents that interact with each other through the gravity force. The agents are considered as objects and their performance is measured by their masses. The gravity force causes a global movement where all objects move towards other objects with heavier masses. The slow movement of heavier masses guarantees the exploitation step of the algorithm and corresponds to good solutions.
22 CONCLUSION
This work is targeting the optimisation of milling machine parameters to get minimum surface roughness. To achieve this ALO (ant lion) optimisation algorithm is used in which we behaviour of lion ant and simple ant is used. Four parameters speed of cut, depth of cut, feed rate and step over ratio are the independent variables which affects the dependent surface roughness. So a linear and non linear relationship in between these independent and dependent variables is set and optimised to have minimum surface roughness. In this work we compared both linear and non linear analysis by our proposed optimisation and individual GSA and TLBO. Non linear relation between variables is giving very less surface roughness. An improvement of 88% over single GSA result in paper, by our method is achieved for non linear relation whereas in case of linear this improvement is 13%. But ALO is performing well than single GSA and TLBO in both cases. We have developed our script for all these optimisations and even then these are giving better results than paper. This optimisation of variables reduces the hassle to test the job for different set of parameters which also waste the material. So by our proposed optimisation, a very high smoothness of job can be achieved.
FUTURE SCOPE
Our work can be a basis of number of independent parameters considered for the minimisation of surface roughness.
Results obtained and four tuning variables set must be validated on CNC machine which can prove the optimisation results more realistic.
Another optimisation approach which is latest like PeSOA can also be combined with GSA or TLBO and tested for more accurate results.
There is a scope to consider moresuch composite materials with various combinationsof constituent materials under high-speed cuttingconditions in order to compute the optimum valuesof input machining conditions for better design ofmanufacturing processes
REFERENCES
[1]. C. Manikandan, B. Rajeswari “Cutting Parameters On Drilling EN24 Using Taguchi Method” International Journal of Engineering Research & Technology (IJERT) - 7, July – 2013
[2]. SrinivasaReddy,S. Suresh, F. Anand Raju, A. Gurunadham “Determination of Optimum Parameters in CNC Drilling of Aluminium Alloy Al6463 by Taguchi Method” International Journal of Engineering Research & Technology (IJERT-) - 2, February - 2014
[3]. Prof. Dr. NabeelKadimAbid Al-Sahib, Hasan Fahad Abdulrazzaq,Thi-Qar University,Al-Jadriya , Baghdad, Iraq “Tool Path Optimization of Drilling Sequence in CNC MachineUsing Genetic Algorithm” Innovative Systems Design and Engineering.1, 2014
[4]. Nakandhrakumar R S “Adaptive Optimization using Grey Relational Analysis and PID Control of CNC Drilling Process”International Conference on Robotics, Automation, Control and Embedded Systems -18-20 February 2015 [5]. Kunal Sharma, Mr. Abhishek Jatav “Optimization of Machining Parameters in Drilling of Stainless Steel International
Journal of Scientific Research Engineering & Technology (IJSRET)” - 8, August 2015
[6]. Krupal Pawar and R. D. Palhade “ Multi-objective Optimization of CNC Turning Process Parameters for High Speed Steel (M2) Using Taguchi and ANOVA Method” International Journal of Hybrid Information Technology (2015)
[7]. K.G.Durga Prasad, M.V.Prasad and K.Venkata Subbaiah “ Optimization of Process Parameters in CNC End Milling of Glass - Fibre Reinforced Plastic” International Journal For Research In Emerging Science And Technology” - 6, JUN-2015
[8]. H. Abdullah1, R. Ramli1, D. A. Wahab1, J. A. Qudeiri “Simulation Approach Of Cutting Tool Movement Using Artificial Intelligence Method” Journal of Engineering Science and Technology - June 2015
[9]. Khashayar DaneshNarooei “Tool Path Optimization for Drilling Process by CNC Milling Machine Using Ant Colony Optimization (ACO)”JANUARY 2014
[10]. Vikas Pare – Geeta Agnihotri – Chimata Krishna “Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques” Journal of Mechanical Engineering-2015
[11]. N. V. Mahesh Babu Talupula “Experimental Investigation Ofoptimal Machining Parameters Of Mild Steel In Cnc Milling Using Particle Swarm Optimization” IPASJ International Journal of Computer Science (IIJCS)1, January 2015
[12]. R. Venkata Rao,” Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems”, Decision Science Letters 5(1):1-30 · January 2016
23 [14]. Baghlani, M. H. Makiabadi,”Teaching-Learning-Based Optimization Algorithm For Shape And Size Optimization Of
Truss Structures With Dynamic Frequency Constraints”, IJST, Transactions of Civil Engineering, Vol. 37, pp 409-421 [15]. Kai-Lin Wang , Hui-Bin Wang , Li-Xia Yu , Xue-Yu Ma , Yun-Sheng Xue,”Toward Teaching-Learning-Based
Optimization Algorithm for Dealing with Real-Parameter Optimization Problems”, Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
