Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis

The theory of grey systems is a new technique for performing prediction, relational analysis and decision making in many areas. In this paper, the use of grey relational analysis for optimising the drilling process parameters for the work piece surface roughness and the burr height is introduced. Various drilling parameters, such as feed rate, cutting speed, drill and point angles of drill were considered. An orthogonal array was used for the experimental design. Optimal machining parameters were determined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (the surface roughness and the burr height). Experimental results have shown that the surface roughness and the burr height in the drilling process can be improved effectively through the new approach .     

Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method

This paper presents a new approach for the optimization of drilling parameters on drilling Al/SiC metal matrix composite with multiple responses based on orthogonal array with grey relational analysis. Experiments are conducted on LM25-based aluminium alloy reinforced with green bonded silicon carbide of size 25 μm (10% volume fraction). Drilling tests are carried out using TiN coated HSS twist drills of 10 mm diameter under dry condition. In this study, drilling parameters namely cutting speed, feed and point angle are optimized with the considerations of multi responses such as surface roughness, cutting force and torque. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in drilling process can be improved effectively through the new approach.     

Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill

This study was carried out to understand micro-milling of aluminum material with ball nose end mill and consisted of four stages: experimental work, modelling, mono and multi objective optimization. In the first stage (experimental work), micro-milling experiments were carried out using Taguchi method. The effects of spindle speed, feed per tooth and depth of cut on tool wear, force and surface roughness were investigated. Cutting tools and workpiece surfaces were also inspected via scanning electron microscope. Adhesion and abrasion wear mechanisms during micro-milling of aluminum were observed. Workpiece surfaces had the accumulations of plastically deformed workpiece material due to the high ductility of aluminum. In the second stage (modelling), all data gathered in the experimental works were utilized to formulate first-order models with interaction. These first-order models with interaction could be used to predict responses in micro-milling of aluminum with a minor error. In the third stage (mono-objective optimization), responses were used alone in optimization study as an objective function. To minimize all responses, Taguchi’s signal to noise ratio was used. The effect of control factors on responses was determined by analysis of variance. In the fourth stage (multi objective optimization), responses were optimized simultaneously using grey relational analysis.     

Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis

Electro-discharge machining (EDM) is an enormously used nonconventional process for removing material in die making, aerospace, and automobile industries. It consists of limitations like poor volumetric material removal rate (MRR) and reduced surface quality. Powder mixed EDM (PMEDM) is a new development in EDM to enhance its machining capabilities. The present work investigates the effect of powder concentration (C_p), peak current (I_p), pulse on time (T_on), duty cycle (DC) and gap voltage (V_g) on MRR, tool wear rate (TWR), electrode wear ratio (EWR), and surface roughness (SR) simultaneously for H-11 die steel using SiC powder. Taguchi's L₂₇ orthogonal array has been used to conduct the experiments. Multiobjective optimization using grey relational analysis (GRA) and technique for order of preference by similarity to ideal solution (TOPSIS) has been used to maximize the MRR and minimize the TWR, EWR, and SR and determine the optimal set of process parameters. Analysis of variance (ANOVA) has been performed to understand the significance of each process parameter. Results were verified by conducting confirmatory tests. GRA and TOPSIS exhibit an improvement of 0.1843 and 0.14308 in the preference values, respectively. Microstructure analysis has been done using scanning electron microscope (SEM) for the optimum set of parameters.     

The worst ill-conditioned silicon wafer slicing machine detected by using grey relational analysis

In the silicon slicing process, machine vibrations and the unstable wire knife motion cause the slicing precision to drift, or other ill-conditions. This process involves several synchronously occurring multiple quality characteristics, such as thickness (THK), bow, warp, total indicator reading (TIR), and total thickness variation (TTV), which must be closely monitored and controlled. In this research, grey relational analysis (GRA) is applied to prevent an ill-conditioned wire saw machine from producing an unconfirmed product that is screened from the synchronously occurring multiple quality characteristics. Five weights of those characteristics are determined by an entropy method. Finally, a case study and the exponential weighted moving average (EWMA) control chart are presented to demonstrate and verify the feasibility and effectiveness of the proposed method.     

Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics

Carbon Fibre Reinforced Plastic (CFRP) composite materials have potential applications in various domains. In machining, drilling is essentially required to join different structures. But CFRP drilling poses many problems that decrease the quality of holes. In this paper, Taguchi’s L₂₇ orthogonal array is used to perform drilling of CFRP composite plates. To improve the quality of the holes drilled, the optimal combination of drilling parameters is chosen using grey relational analysis. Grey fuzzy optimization of drilling parameters is based on five different output performance characteristics, namely, thrust force, torque, entry delamination, exit delamination and eccentricity of the holes. Analysis of variance (ANOVA) is used to find the percentage contribution of the drilling parameters and found that feed rate is the most influential factor in drilling of CFRP composites.     

Optimization of surface roughness,cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method

In this work, the dry turning parameters of two different grades of nitrogen alloyed duplex stainless steel are optimized by using Taguchi method. The turning operations were carried out with TiC and TiCN coated carbide cutting tool inserts. The experiments were conducted at three different cutting speeds (80, 100 and 120 m/min) with three different feed rates (0.04, 0.08 and 0.12 mm/rev) and a constant depth of cut (0.5 mm). The cutting parameters are optimized using signal to noise ratio and the analysis of variance. The effects of cutting speed and feed rate on surface roughness, cutting force and tool wear were analyzed. The results revealed that the feed rate is the more significant parameter influencing the surface roughness and cutting force. The cutting speed was identified as the more significant parameter influencing the tool wear. Tool wear was analyzed using scanning electron microscope image. The confirmation tests are carried out at optimum cutting conditions. The results at optimum cutting condition are predicted using estimated signal to noise ratio equation. The predicted results are found to be closer to experimental results within 8% deviations.     

Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model

The application of parameter design methodology has been considerable in recent years to make system performance robust over a wide range of input conditions. This notion has been referred to as a robust design with dynamic characteristics. Due to product complexity, multiple correlated characteristics must be simultaneously evaluated for improving product quality. Dynamic multi-response optimization is becoming an important issue to contemporary industry. This study developed a novel procedure of optimizing dynamic multi-responses using principal component analysis (PCA) and multiple criteria evaluation of the grey relation model. PCA can consider the correlations among multiple quality characteristics to obtain uncorrelated components. These components are then substituted into multiple criteria evaluation of the grey relation model to determine the optimal factor level combination. A case study demonstrates the effectiveness of the proposed procedure for optimizing dynamic multi-response processes.     

Multi-objective optimization of cutting parameters in high-speed milling based on grey relational analysis coupled with principal component analysis

This paper investigates optimization problem of the cutting parameters in high-speed milling on NAK80 mold steel. An experiment based on the technology of Taguchi is performed. The objective is to establish a correlation among spindle speed, feed per tooth and depth of cut to the three directions of cutting force in the milling process. In this study, the optimum cutting parameters are obtained by the grey relational analysis. Moreover, the principal component analysis is applied to evaluate the weights so that their relative significance can be described properly and objectively. The results of experiments show that grey relational analysis coupled with principal component analysis can effectively acquire the optimal combination of cutting parameters and the proposed approach can be a useful tool to reduce the cutting force.     

Tribological Performance Optimization of Electroless Ni–P Coatings Using the Taguchi Method and Grey Relational Analysis

The present article considers an experimental study of tribological performance of electroless Ni–P coatings and optimization of tribological test parameters based on the Taguchi method coupled with grey relational analysis. A grey relational grade obtained from the grey relational analysis is used as performance index to study the behaviour of electroless Ni–P coating with respect to friction and wear characteristics. Experiments are carried out by utilizing the combination of tribological test parameters based on L₂₇ Taguchi orthogonal design with three test parameters, viz., load, speed and time. It is observed that all the three test parameters have significant contribution in controlling the friction and wear behaviour of electroless Ni–P coating. In addition, the interaction of load and time has significant influence on tribological performance. The surface morphology, composition and wear mechanism of the coatings are studied with the help of scanning electron microscopy, X-ray diffraction analysis and energy dispersed X-ray analysis.     

基于灰色关联分析与自适应混沌差分进化算法的激光熔覆工艺参数优化方法

多道搭接激光熔覆工艺具有复杂性与耦合性,熔覆层质量受到工艺参数的直接影响.为确定最佳的工艺参数,以45钢表面多道搭接激光熔覆M2合金粉末为例,针对工艺参数对熔覆层宽度、表面平整度和稀释率的影响,设计相应的中心复合实验;通过灰色关联分析(GRA)建立熔覆层质量综合评价度,将多目标优化问题转化为单目标,并采用响应面分析法建...     

Study of grey relational grade identification for ferrography based on characteristic analysis of wear debris

Attention has been focused on how to achieve intelligent automation in ferrographic diagnosis in order to overcome the subjectivity of the diagnosis process. The present paper reports on a technique of characteristic measurement developed on the basis of the VC++ 6.0 programming platform, with characteristic parameters such as area, roundness, and aspect ratio being extracted from images of wear debris based on digital image analysis. However, the extraction of characteristic parameters from a ferrographic image is not the ultimate purpose of ferrographic diagnosis. The wear particles should be classified into several pre‐decision categories and their statistical distribution should also be calculated. The grey relational grade theory is introduced in this paper as a way to recognise wear debris and a new software system has been developed to deal with the problems occurring in the automation of ferrographic diagnosis. It is shown that the identification rules can be used to treat some real wear debris images with generally satisfactory results.     

Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM,grey relational and techno economical approach)

This paper presents the mathematical modelling and parametric optimization on flank wear and surface roughness based on response surface methodology and grey-based Taguchi method in finish hard turning of AISI 4340 steel (HRC 47 ± 1) using multilayer coated carbide (TiN/TiCN/Al₂O₃/TiN) insert under dry environment. The economical feasibility of utilizing multilayer TiN coated carbide insert has been described. Model adequacy has been checked using correlation coefficients. From main effect, it is evident that, cutting speed is the most significant factor for flank wear followed by depth of cut and feed. Again, feed is the most significant factor for surface roughness followed by cutting speed and depth of cut. The coefficient of determination (R²) is more than 75% for RSM models developed, which shows the high correlation exist between the experimental and predicted values. The experimental vs. predicted values of flank wear and surface roughness (Ra and Rz) are also found to be very close to each other implying significance of models developed. The improvement of grey relational grade from initial parameter combination (d2–f3–v4) to the optimal parameter combination (d1–f1–v3) is found to be 0.3093 using grey relational analysis coupled with Taguchi method for simultaneous optimization of responses. Flank wear (VBc) and surface roughness parameters (Ra and Rz) are decreased 1.9, 2.32 and 1.5 times respectively considering optimal parametric combinations for multi-responses. The calculated total machining cost per part is only Rs. 3.17 due to higher tool life (47 min at their optimal level) of multilayer TiN coated carbide insert. It brings to the reduction of downtime and increases the savings.     

基于小波分解灰关联的热波检测图像增强

针对热波检测图像存在的高噪声、低对比度等问题,提出一种基于小波分解和灰关联分析的图像增强方法。该方法首先采用小波变换对待处理的热波图像进行三级小波分解,得到图像相应的低频分量和高频分量,然后利用图像中干扰信号和有用信号在分解后不同分量上的分布规律,采用灰色理论中的灰色关联分析理论来区分高频分量中的干扰信号和有用信号,从而实现对图像中噪声的抑制以提高图像的质量。实验结果表明:提出的方法与常规的滤波方法、小波阈值去噪增强等方法相比,图像的对比度得到明显改善,峰值信噪比最大,因此该方法可用于热波检测图像的增强处理中。     

Experimental investigations on machinability aspects in finish hard turning of AISI 4340 steel using uncoated and multilayer coated carbide inserts

The present work deals with some machinability studies on flank wear, surface roughness, chip morphology and cutting forces in finish hard turning of AISI 4340 steel using uncoated and multilayer TiN and ZrCN coated carbide inserts at higher cutting speed range. The process has also been justified economically for its effective application in hard turning. Experimental results revealed that multilayer TiN/TiCN/Al₂O₃/TiN coated insert performed better than uncoated and TiN/TiCN/Al₂O₃/ZrCN coated carbide insert being steady growth of flank wear and surface roughness. The tool life for TiN and ZrCN coated carbide inserts was found to be approximately 19 min and 8 min at the extreme cutting conditions tested. Uncoated carbide insert used to cut hardened steel fractured prematurely. Abrasion, chipping and catastrophic failure are the principal wear mechanisms observed during machining. The turning forces (cutting force, thrust force and feed force) are observed to be lower using multilayer coated carbide insert in hard turning compared to uncoated carbide insert. From 1st and 2nd order regression model, 2nd order model explains about 98.3% and 86.3% of the variability of responses (flank wear and surface roughness) in predicting new observations compared to 1st order model and indicates the better fitting of the model with the data for multilayer TiN coated carbide insert. For ZrCN coated carbide insert, 2nd order flank wear model fits well compared to surface roughness model as observed from ANOVA study. The savings in machining costs using multilayer TiN coated insert is 93.4% compared to uncoated carbide and 40% to ZrCN coated carbide inserts respectively in hard machining taking flank wear criteria of 0.3 mm. This shows the economical feasibility of utilizing multilayer TiN coated carbide insert in finish hard turning.     

Experimental investigation,prediction and optimization of cylindricity and perpendicularity during drilling of WCB material using grey relational analysis

Manufacturing is always the heart of majority of industries. Drilling is an extremely important and an essential machining process which requires a lot of attention as in most of the cases it is required for assembly purposes. Majority of the holes produced during drilling are made with the help of Vertical Machining Centre (VMC) meant for pin- hole assembly. Though the tolerance is within limit, assembly problems arise due to the improper geometry of these holes. Various geometrical tolerances like cylindricity, circularity, perpendicularity and position errors are responsible for such assembly problems. This investigation is focussed on cylindricity and perpendicularity in the drilling of Wrought Cast Steel Grade B (WCB) material using SOMX 050204 DT insert. In this work, effect of machining variables like cutting speed, feed rate and depth of cut (canned cycle) are investigated and optimized using grey relational analysis (GRA). Reliable experiments are conducted based on a 3³ full factorial, replicated twice. Second order regression models are developed for predicting cylindricity and perpendicularity. The models’ adequacy has been checked by calculating correlation coefficient. It shows that the developed models are well fitted for the prediction of responses within the specific range of input variables.     

Experimental investigation and optimisation in drilling of GFRP composites

Glass fibre-reinforced polymer (GFRP) composite materials are one of the important materials and are economic alternative to engineering materials because of their superior properties. This paper presents an effective approach for the optimisation of drilling parameters with multiple performance characteristics based on the Tagugch’s method with grey relational analysis. Taguchi’s L₁₆, 4-level orthogonal array has been used for the experimentation. The drilling parameters such as spindle speed and feed rate are optimised with consideration of multiple performance characteristics, such as thrust force, workpiece surface roughness and delamination factor. Response table and response graph are used for the analysis. The analysis of grey relational grade indicates that feed rate is the more influential parameter than spindle speed. The results indicate that the performance of drilling process can be improved effectively through this approach.     

Optimization of multi machining characteristics in WEDM of WC-5.3%Co composite using integrated approach of Taguchi,GRA and entropy method

Wire electrical discharge machining (WEDM) is a well known process for generating intricate and complex geometries in hard metal alloys and metal matrix composites with high precision. In present work, intricate machining of WC-5.3%Co composite on WEDM has been reported. Taguchi’s design of experiment has been utilised to investigate the process parameters for four machining characteristics namely material removal rate, surface roughness, angular error and radial overcut. In order to optimize the four machining characteristics simultaneously, grey relational analysis (GRA) coupled with entropy measurement method has been employed. Through GRA, grey relational grade has been computed as a performance index for predicting the optimal parameters setting for multi machining characteristics. Using Analysis of Variance (ANOVA) on grey relational grade, significant parameters affecting the multi-machining characteristics has been determined. Confirmatory results prove the potential of present approach.     

Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution

Optimizing multi-response problems has become an increasingly relevant issue when more than one correlated product quality characteristic must be assessed simultaneously in a complicated manufacturing process. This study proposes a novel optimization procedure for multiple responses based on Taguchi’s parameter design. The signal-to-noise (SN) ratio is initially used to assess the performance of each response. Principal component analysis (PCA) is then conducted on the SN values to obtain a set of uncorrelated components. The optimization direction for each component is determined based on the corresponding variation mode chart. Finally, the relative closeness to the ideal solution resulting from the technique for order preference by similarity to ideal solution (TOPSIS) is determined as an overall performance index (OPI) for multiple responses. Engineers can easily employ the proposed procedure to obtain the optimal factor/level combination for multiple responses. A case study involving optimization of the chemical-mechanical polishing of copper (Cu-CMP) thin films from an integrated circuit manufacturer in Taiwan is also presented to demonstrate the effectiveness of the proposed procedure.