Statistical Optimization by Response Surface Methodology of Process Parameters During the CNC Turning Operation of Hybrid Metal Matrix Composite (original) (raw)
Related papers
In modern era of manufacturing, the Hybrid Metal matrix composites (HMMC) are most advanced material, which are mostly used in today's industries. In this paper we calculate the influence of most prominent parameters of CNC turning machine on material removal rate (MRR) and surface roughness (SR) of the hybrid composite material. Turning process parameters i.e. Feed, Speed and Depth of Cut are considered and have calculated their response in term of MRR and SR. To develop a hybrid metal matrix composite material Aluminium Al6061 as a base material and silicon carbide (10%wt) and graphite (3%wt) particles as reinforcements are used. Stir casting process was used to fabricate the hybrid composite because of easy setup and cheap method of fabrication. To optimize our output parameters, RSM (response surface methodology) is used. We diagnosed the best combination of input parameters for the maximum output (MRR) and minimum (SR). TNMG160408, TNMG2000 and K10 tool inserts are used as cutting tool. The purpose of the present study is to calculate the optimum setting of process parameters for better output results.
Optimizing the machining variables in CNC turning of aluminum based hybrid metal matrix composites
SN Applied Sciences
Aluminum based hybrid metal matrix composites (HMMC) are being employed nowadays in automobile, aeronautical, sports equipment etc. In this study, three different Al6063 based hybrid metal matrix composites (HMMC) samples having reinforcement with 3%, 6% and 9% by weight respectively are fabricated using stir casting method. Reinforcements used are waste products, namely, jute ash, groundnut shell and sugarcane. Surface roughness of these fabricated composite are is tested by varying machining parameters. The design of experiment is constructed by Taguchi method using three factors and two level, with L8 orthogonal array in which surface roughness is the output response parameter. These recorded results are analysed using analysis of variance and optimization of process parameters is done using response surface methodology and genetic algorithm. The Genetic algorithm optimization is achieved within 102 generations which is quite fast. On comparing it was found that results obtained from GA closely agreed with those obtained from the Response surface methodology.
This manuscript presents the influencing parameters of CNC turning conditions to get high removal rate and minimal response of surface roughness in turning of AA7075-TiC-MoS2 composite by response surface method. These composites are particularly suited for applications that require higher strength, dimensional stability and enhanced structural rigidity. Composite materials are engineered materials made from at least two or more constituent materials having different physical or chemical properties. In this work seventeen turning experiments were conducted using response surface methodology. The machining parameters cutting speed, feed rate, and depth of cut are varied with respect to different machining conditions for each run. The optimal parameters were predicted by RSM technique. Turning process is studied by response surface methodology design of experiment. The optimal parameters were predicted by RSM technique. The most influencing process parameter predicted from RSM techniques in cutting speed and depth of cut.
The main controllable parameters for the CNC turning machines are cutting tool variables, work piece material variables, and cutting conditions. The desired output is surface roughness, material removal rate and tools wear. Optimization of machining parameters needs to determine the most significant parameter for required output. Various techniques are used for optimization of machining parameters including Taguchi, RSM and ANOVA approach to determine most significant parameter. This work presents the findings of an experimental investigation into the effects of cutting speed, feed rate, and depth of cut in CNC turning of Aluminium KS 1275. Response surface methodology (RSM) is used to accomplish the objective of the experimental study. Face centered central composite design has been used for conducting the experiments. The result from RSM reveals that feed is the most significant factor followed by depth of cut.
Procedia Engineering, 2014
Metal matrix composites, in particular, Aluminium Hybrid Composites are gaining increasing attention for applications in air and land because of their superior strength to weight ratio, density and high temperature resistance. This paper presents the results of experimental investigation on machinability properties of Silicon Carbide and Boron Carbide reinforced Aluminium γ56 hybrid metal matrix composite. The composites were prepared by varying weight fraction of SiC (5%, 10%, 15%) and keeping the Boron Carbide weight fraction (5%) is constant using modified stir casting technique. Four layer coated carbide insert (TiN. Al β O γ , TICN, TiN) designated as CNMG 1β0408 FR was used to machine the fabricated composites. Face centered central composite experimental design coupled with Response Surface Methodology (RSM) was used for modeling that the process output characteristics that influence by weight fraction, speed, feed rate, cutting depth. The experimental results imply that surface Roughness criteria are found to increase with increase of feed. At 0.β06.mm/rev feed, the Surface Roughness deteriorated rapidly. Roughness decreases at higher cutting speed during machining. With the help of Mintab software, RSM showed an accuracy of 95%. Moreover, a good agreement was observed between the experimental and the predicted values of surface roughness and cutting force. Optimal cutting condition which leading to the minimum surface roughness and cutting force were highlighted. Selection and peer-review under responsibility of the Organizing Committee of GCMM β014.
The International Journal of Advanced Manufacturing Technology, 2010
Metal matrix composites (MMC) have become a leading material among composite materials, and in particular, particle reinforced aluminum MMCs have received considerable attention due to their excellent engineering properties. These materials are known as the difficult-to-machine materials because of the hardness and abrasive nature of reinforcement element-like silicon carbide particles (SiC p). In this study, an attempt has been made to model the machinability evaluation through the response surface methodology in machining of homogenized 20% SiC p LM25 Al MMC manufactured through stir cast route. The combined effects of four machining parameters including cutting speed (s), feed rate (f), depth of cut (d), and machining time (t) on the basis of two performance characteristics of flank wear (VB max) and surface roughness (Ra) were investigated. The contour plots were generated to study the effect of process parameters as well as their interactions. The process parameters are optimized using desirability-based approach response surface methodology.
Optimization of Surface Roughness during Turning of Aluminium based Metal Matrix Composites
Application of aluminum based metal matrix composites can be found in many manufacturing industries such as aircraft and aerospace components, marine fittings, transport, drive, shafts, brake components, valves, couplings. Metal matrix composites are heterogeneous material and they impose machinability issues with conventional turning process. This causes the deterioration of surface finish after machining. Thus, this paper aimed at conducting experiments on aluminum based metal matrix composites and investigating the influence of machining process parameters such as cutting speed (m/min), feed rate(mm/rev), depth of cut (mm) and nose radius (mm) on surface roughness. Experiments were carried out on CNC machine tool and coated tungsten carbide inserted cutting tool was used for machining. Further in this study an empirical model was developed for predicting surface roughness in terms of spindle speed, feed rate, depth of cut and nose radius using multiple regressions modeling method. At last, genetic algorithm has been employed to find out the optimal setting of process parameters that optimize surface roughness value. This provides a good flexibility to the manufacturing industries by selecting the good parameters setting as per the requirement.
Rsm: A Key to Optimize Machining: Multi-Response Optimization of Cnc Turning with Al-7020 Alloy
Parametric optimization, especially in machining of non-ferrous alloys seems to be quite rare and needs an immediate attention because of its associated downstream financial and non-financial losses. This book tries to fill the gap and presents an optimization problem of commonly used Al-7020 Alloy. Principles of Response Surface Methodology (RSM) have been implemented through Minitab software to bring necessary multi-response optimization, while turning on a CNC turner. The present study focuses on to enhance Material Removal Rate (MRR) while simultaneously reducing the Surface Roughness (Ra), during turning of Al-alloy. Such opposite natured response optimization is much difficult to achieve, particularly when uncoated carbide tip has been used as a cutting tool. Intensive literature survey helps to pin point parameters like; Cutting Speed, Feed Rate and Depth of Cut as a most critical to machining parameters, as far as effective and efficient optimization of selected responses are concerned. All these control-parameters are directly or inversely related to each other. If the depth of cut is increased MRR increases at the same time we get poor surface finish. Increase in the cutting speed has positive impact on both material removal rate and surface finish. Shortlisted parameters are conflicting, so we have to optimize these for further enhancement of the overall turning performance. At last, the optimized results are verified by using ANOVA as a statistical tool. This book provides quite rare Case-study of multi-response optimization (while non-ferrous CNC turning) to practioners, machinists and SME owners appropriately.
Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN
Engineering and Technology Journal, 2020
The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod. The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology. The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) were analyzed through analysis of variance (ANOVA). The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut. Surface response methodology developed between the machining parameters and response and confirmation experiments reveals that the good agreement with the regression models. The coefficient of determination value for RSM model is found to be high (R2 = 0.961). It indicates...
INVESTIGATION OF MACHINING PARAMETERS IN CNC TURNING USING RESPONSE SURFACE METHODOLOGY (RSM)
CNC turning is one among the metal cutting process in which quality of the finished product depends mainly upon the machining parameters such as feed, speed, depth of cut, type of coolant used, types of inserts used etc. Similarly the work piece material plays an important role in the metal cutting process. This study involves in identifying the optimized parameters in CNC turning of Aluminium and Stainless Steel. To identify and measure the formation of burrs samples are examined under scanning electron microscope (SEM).The optimization techniques used in this study are Response surface methodology, and Genetic algorithm. These optimization techniques are very helpful in identifying the optimized control factors with high level of accuracy. Brass and Copper (Non Ferrous) materials are taken for this investigation.