Studies on Machining Parameters While Milling Particle Reinforced Hybrid (Al6061/Al2O3/Gr) MMC

In this article, response surface methodology has been used for finding the optimal machining parameters values for cutting force, surface roughness, and tool wear while milling aluminum hybrid composites. In order to perform the experiment, various machining parameters such as feed, cutting speed, depth of cut, and weight (wt) fraction of alumina (Al₂O₃) were planned based on face-centered, central composite design. Stir casting method is used to fabricate the composites with various wt fractions (5%, 10%, and 15%) of Al₂O₃. The multiple regression analysis is used to develop mathematical models, and the models are tested using analysis of variance (ANOVA). Evaluation on the effects and interactions of the machining parameters on the cutting force, surface roughness, and tool wear was carried out using ANOVA. The developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. The optimum machining parameters obtained from the experimental results showed that lower cutting force, surface roughness, and tool wear can be obtained by employing the combination of higher cutting speed, low feed, lower depth of cut, and higher wt fraction of alumina when face milling hybrid composites using polycrystalline diamond insert.     

A study on milling of glass fiber reinforced plastics manufactured by hand-lay up using statistical analysis (ANOVA)

Milling is the most practical machining (corrective) operation for removing excess material to produce a well defined and high quality surface. However, milling composite materials presents a number of problems such as surface delamination associated with the characteristics of the material and the cutting parameters used. In order to minimize these problem is presented a study with the objective of evaluating the cutting parameters (cutting velocity and feed rate) related to machining force in the workpiece, delamination factor, surface roughness and international dimensional precision in two GFRP composite materials (Viapal VUP 9731 and ATLAC 382-05). A plan of experiments, based on an orthogonal array, was established considering milling with prefixed cutting parameters. Finally an analysis of variance (ANOVA) was preformed to investigate the cutting characteristics of GFRP composite materials using a cemented carbide (K10) end mill.     

高强玻璃纤维复合材料的铣削加工性能研究

由于复合材料具有各向异性,在加工过程中产生的分层破坏严重影响其装配质量、降低其结合强度,因此需要对复合材料加工过程中的分层情况进行研究.从手糊成型层叠厚板高强玻璃纤维增强复合材料的铣削加工入手,通过单因素法改变切削参数测试作用在试件上的切削力和扭矩的大小来考察加工表面的分层破坏情况.利用正交回归原理建立了切削力、扭矩和分层因子的经验公式,预测了切削力、扭矩和分层因子随切削参数变化的趋势.     

Machinability analysis in drilling woven GFR/epoxy composites: Part II – Effect of drill wear

The current paper is a continuation of the author’s work on machinability analysis in drilling woven glass fiber reinforced epoxy (GFRE) composites. The present paper deals the effect of drill pre-wear on the machinability parameters in drilling GFRE composites, at different cutting conditions. Machinability parameters were characterized by thrust force, torque, peel-up and push-out delaminations, and surface roughness of drilled holes. The results showed that, the behavior of thrust force during drilling process was greatly affected by the drill pre-wear. This effect becomes extreme at high cutting speed and feed, which in turn increases delaminations and surface roughness. The scanning electron photographs demonstrate that, peaks and valleys in surface roughness profile were due to burning the matrix. The multi-variable linear regression models were fair to fit the experimental data. Therefore, considerable attention should be paid to record different models that improve the correlation between the machinability parameters and machining conditions.     

Tool condition monitoring in milling based on cutting forces by a neural network

Automated machining systems require reliable online monitoring processes. The application of a multilayered neural network for tool condition monitoring in face milling is introduced and evaluated against cutting force data. The work uses the back-propagation algorithm for training neural network of 5 2 10 2 2 architecture. An artificial neural network was used for feature selection in order to estimate flank wear ( Vb ) and surface roughness ( Ra ) during the milling operation. The relationship of cutting parameters with Vb and Ra was established. The sensor selection using statistical methods based on the experimental data helps in determining the average effect of each factor on the performance of the neural network model. This model, including cutting speed, feed rate, depth of cut and two cutting force components (feed force and vertical Z -axis force), presents a close estimation of Vb and Ra . Therefore, the neural network with parallel computation ability provides a possibility for setting up intelligent sensor systems.     

Effects of Filler Concentration and Cutting Parameters on Material Properties and Machinability of SiC-Filled Epoxy Tooling Board

An experimental study was conducted to examine the material properties and machinability of a silicon carbide (SiC)-filled epoxy conductive tooling system (RP4037 CAST-IT^TM). Specifically, the effects of SiC filler concentration and machining process parameters (cutting speed and feed) on the physical and material properties, resultant cutting force, surface integrity, and tool wear were studied. Machinability evaluation was carried out using the end milling process. The study showed that an increase in filler concentration significantly increased the density, thermal conductivity, resultant machining forces, surface roughness of the machined surface, and tool wear. However, it had insignificant impact on the glass transition temperature, strength, or hardness. A decrease in material strength was observed with increasing cutting speed and feed. Increasing filler concentration was also found to degrade the machined surface morphology. Possible explanations for the observed effects are discussed.     

Prediction of cutting force based on machining parameters on AL7075-T6 aluminum alloy by response surface methodology in end milling

Cutting forces modeling is the basic to understand the cutting process, which should be kept in minimum to reduce tool deflection, vibration, tool wear and optimize the process parameters in order to obtain a high quality product within minimum machining time. In this paper a statistical model has been developed to predict cutting force in terms of geometrical parameters such as rake angle, nose radius of cutting tool and machining parameters such as cutting speed, cutting feed and axial depth of cut. Response surface methodology experimental design was employed for conducting experiments. The work piece material is Aluminum (Al 7075-T6) and the tool used is high speed steel end mill cutter with different tool geometry. The cutting forces are measured using three axis milling tool dynamometer. The second order mathematical model in terms of machining parameters is developed for predicting cutting forces. The adequacy of the model is checked by employing ANOVA. The direct effect of the process parameter with cutting forces are analyzed, which helps to select process parameter in order to keep cutting forces minimum, which ensures the stability of end milling process. The study observed that feed rate has the highest statistical and physical influence on cutting force.     

Machinability analysis in drilling woven GFR/epoxy composites: Part I – Effect of machining parameters

The present paper deals with the effect of machining parameters (feed, speed and drill diameter) on the thrust force and machinability of woven glass fiber-reinforced epoxy (GFRE) composites. The selected machinability parameters were delamination size, surface roughness, and bearing strength. The results show that, delamination-free in drilling GFRE composites was not observed, in the range of the investigated cutting parameters. Surface roughness instrument can be used as an indication for the position of the internal delamination damage in drilling GFRE composites. The high values of correlation coefficients between thrust force and the machinability parameters confirm the importance of reducing the thrust force to improve the load carrying capacity of composite structure assembled by rivets or bolted joints.     

Study of milling force variation in ultrasonic vibration-assisted end milling

Ultrasonic vibration cutting has been proved to be an effective cutting technology for its excellent cutting performance and has been widely applied in turning and drilling process. However, this kind of technology is rarely tried in milling process. In cutting process, cutting force is an important process parameter, which affects surface finish and tool wear. This paper investigates the milling force variation in ultrasonic vibration-assisted end milling process through a series of slot-milling experiments. The main research contents include two parts, one is the effect of the externally excited vibration on milling force in milling process, and the other is the influence of milling and vibrating parameters matching on milling force value. Experimental results show that ultrasonic vibration can change traditional milling conditions, realize separate-type milling, obtain similar pulse-like profiles of cutting forces, reduce average cutting force value; and the peak value of the feed direction cutting force can also be greatly decreased by adopting reasonable vibration amplitude, an optimal combination of machining parameters is of great benefit to achieving small cutting force. According to the experimental findings, ultrasonic vibration-assisted milling is a prospective technology to achieve precision milling of small part.     

Optimization for Turning of Al-6061-SiC-Gr Hybrid Nanocomposites Using Response Surface Methodologies

Metal matrix composites have cemented their applicability in industrial sector by virtue of their excellent mechanical properties. However, work has largely been done on the studies related to macro/microsize particles. This work has been aimed to evaluate the influence of input parameters in turning of Al-6061-SiC-Gr hybrid nanocomposites. This article evaluates the effect of process parameters on the cutting force and average roughness of the machined surface in turning of Al-6061-SiC-Gr nanocomposites. The experiments were designed using CCD, and cutting force and roughness were evaluated using response surface methodology. Statistical models were generated. The results of the study indicated that feed rate and depth of cut are the major influencing factors in descending order for the cutting force. The analysis of surface roughness revealed that both these factors are having identical effect. The cutting speed had little effect on cutting force and an improvement is seen in surface finish. The experiments also revealed that tool wear is negligible for nanocomposites. The software-predicted values and the experimentally obtained values of the responses were acceptably close to each other with an error percentage of less than 5%. Using response surface optimization, optimal combinations of machining parameters are also obtained.     

Effect of cryogenic CO2 and LN2 coolants in milling of aluminum alloy

The research work was carried out on the end milling of Al 6082-T6 alloy with cryogenic CO₂, LN₂ and wet conditions. The highest axial force (F_z), normal force (F_y) and feed force (F_x) values were recorded on cryogenic LN₂ machining. Use of cryogenic LN₂ helped to reduce the cutting temperatures up to 38.29% and 32.8% when compared with wet and cryogenic CO₂ conditions, respectively. The conventional fluid coolant offered a better surface roughness value (R_a) over cryogenic coolants at a feed rate of 0.015 and 0.02 mm/tooth and cutting speed of 100 m/min. The workpiece surface quality degraded in cryogenic machining conditions during the slot end milling operation of aluminum alloy under the given machining parameters.     

Experimental investigation on drilling of natural flax fiber-reinforced composites

Natural fiber composites (NFCs) have strong potential to replace glass fiber-reinforced plastics. An instrumental operation in machining composite structures is hole making which facilitates assembly of parts. Understanding the effects of drilling process parameters on feature properties of NFCs has great benefits. In this regard, to make a good quality and accurate hole in composite structures, appropriate selection of drill bit and cutting parameters is important. This paper investigates delamination behavior and hole quality of flax/epoxy composite laminates in response to feed, spindle speed, and three different types of drill bit. As indicated by analysis of variance results, drill bit type and feed have greater influences on the thrust force. It appeared that delamination factor and surface roughness were significantly influenced by drill bit, but not by feed and spindle speed. The choice of drill bit has great impact on the delamination factor (67.27%) and surface roughness (74.44%), respectively.     

Nose radius and cutting speed effects during milling of AISI 304 material

Not only milling parameters, but also cutting tool properties affect the machining performance. Therefore, in the current work, the effect of nose radius and cutting speed on the wear, force, surface roughness and chip morphology in down and up milling of AISI 304 stainless steel was investigated. Machining experiments were conducted with cutting tools with radii of 0.4, 0.8 and 1.2?mm at various cutting speeds in both down and up milling. Experimental results showed that the main tool failure mechanisms and modes were adhesion, abrasion, chipping and fracture during milling with various nose radii. Cutting forces dropped with the increment in nose radius regardless of the cutting speed and milling direction, except for up milling at 100?m/min. From the experimental results, it was found that roughness diminished with increase in both nose radius and speed. Surface roughness and the resultant forces during up milling were found to be lower than that during down milling. It was observed that the increment in nose radius increased the edge serration in chip morphology.     

Development of a novel cooling system-assisted minimum quantity lubrication method for improvement of milling performance

This paper presents a novel lubrication method for milling processes that employs cooling system-assisted minimum quantity lubrication (CSMQL) using a thermoelectric cooling system. The CSMQL method improves the cooling effect in the cutting area and enhances processing quality, in addition to reducing energy consumption. Four different coolant strategies including CSMQL, dry, minimum quantity lubrication (MQL), and wet methods were compared in processing mill die steel (SKD11), which is widely used in industry. Different aspects of the milling performance (e.g. surface roughness, morphology, milling temperature, and milling forces) were investigated using these coolant strategies. The experimental results show that not only is the surface roughness of steel milled using CSMQL better than that of steel milled using dry and MQL methods, but CSMQL also produces fewer tool marks on the workpiece surface. In addition, it was found from observations of chip color that using the CSMQL method reduced the cutting temperature by 27% and the cutting force by 22%, compared with dry machining. In summary, the use of CSMQL can not only improve the surface roughness and reduce the cutting force and cutting temperature, but also promote processing quality. This study will help researchers develop more efficient cooling strategies in the future.     

Influence of specimen preparation by machining on the failure of polymer matrix off-axis tensile coupons

Milling is a usual operation to obtain composite coupons for mechanical tests. However, the machining of composite materials using cutting tools leads to high temperatures and machined surface damage that will tend to reduce their mechanical performances. This study presents an investigation of the influence of cutting parameters on the damage level and mechanical response of off-axis glass/epoxy unidirectional coupons machined by side milling. The analysis of cut surface roughness and micrographic observations reveals the primary importance of the relative orientation of the fibres with respect to the cutting direction. An analysis based on design of experiments underlines the influence of the cutting parameters and cutting edge geometry on the ultimate tensile stresses of the off-axis coupons. The results show the importance of the cutting configuration and fibre orientation on the mechanical performances. Two parameters have been defined to quantify the cut surface damages and relate it to the decrease of failure stress. A thorough statistical analysis is also included to separate significant from non-significant results in this study.     

复合材料的切削加工表面结构与表面粗糙度

普通金属材料的切削加工理论表面粗糙度可以用公式计算。复合材料经切削加工后其表面留有各种凹凸缺陷,这些谷峰轮廓并非由刀刃直接切出,故不宜用现有普通材料的公式计算其理论表面粗糙度。纤维增强复合材料的切削加工表面结构和粗糙度与切削方向密切相关。颗粒增强复合材料无方向性,其已加工表面结构和粗糙度主要受增强颗粒硬度和粒度以及含量控制。增强体与基体的界面强度及切削刀具和工艺条件对复合材料加工表面粗糙度有很大影响。      

A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites

Among the various types of metal matrix composites, SiC particle-reinforced aluminum matrix composites (SiC_p/Al) are finding increasing applications in many industrial fields such as aerospace, automotive, and electronics. However, SiC_p/Al composites are considered as difficult-to-cut materials due to the hard ceramic reinforcement, which causes severe machinability degradation by increasing cutting tool wear, cutting force, etc. To improve the machinability of SiC_p/Al composites, many techniques including conventional and nonconventional machining processes have been employed. The purpose of this study is to evaluate the machining performance of SiC p/Al composites using conventional machining, i.e., turning, milling, drilling, and grinding, and using nonconventional machining, namely electrical discharge machining (EDM), powder mixed EDM, wire EDM, electrochemical machining, and newly developed high-efficiency machining technologies, e.g., blasting erosion arc machining. This research not only presents an overview of the machining aspects of SiC_p/Al composites using various processing technologies but also establishes optimization parameters as reference of industry applications.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00313-2     

Endbearbeitung von Aluminiummatrix‐Verbundwerkstoffen durch Drehen

Final Machining of Aluminium Matrix Composites by means of Turning Aluminium matrix composites usually consist of a relatively soft as well as ductile matrix alloy and a hard ceramic reinforcing component in the form of fibers, particles or whiskers, which are embedded in it. The very different characteristics of the composite elements on the one hand facilitate the specific aiming of appropriate combinations of material properties by selection of the components as well as their quantitative ratio. But on the other hand, during machining they cause permanently changing loads on the cutting edge of the tool. Apart from the rapid tool wear, damages of the workpiece surface due to ripped out or destroyed particles proves to be a problem during final machining of such components. The reliable generation of an extensively flawless surface with a small roughness sets high demands on the cutting material, tool geometry and cutting parameters. In order to achieve a long tool life super‐hard CVD diamond tippings (chemical vapour deposition = CVD) are used for the research. On the basis of turning tests with aluminium matrix composites the potential of chip breakers as well as an especial tool nose geometry to improve the workpiece surface is described under consideration of the feed.     

Tool wear mechanisms in axial ultrasonic vibration assisted milling in-situ TiB2/7050Al metal matrix composites

The in-situ TiB₂ particle reinforced aluminum matrix composites are materials that are difficult to machine, owing to hard ceramic particles in the matrix. In the milling process, the polycrystalline diamond (PCD) tools are used for machining these materials instead of carbide cutting tools, which significantly increase the machining cost. In this study, ultrasonic vibration method was applied for milling in-situ TiB₂/7050Al metal matrix composites using a TiAlN coated carbide end milling tool. To completely understand the tool wear mechanism in ultrasonic-vibration assisted milling (UAM), the relative motion of the cutting tool and interaction of workpiecetool-chip contact interface was analyzed in detail. Additionally, a comparative experimental study with and without ultrasonic vibration was carried out to investigate the influences of ultrasonic vibration and cutting parameters on the cutting force, tool life and tool wear mechanism. The results show that the motion of the cutting tool relative to the chip changes periodically in the helical direction and the separation of tool and chip occurs in the transverse direction in one vibration period, in ultrasonic vibration assisted cutting. Large instantaneous acceleration can be obtained in axial ultrasonic vibration milling. The cutting force in axial direction is significantly reduced by 42%-57%, 40%-57% and 44%-54%, at different cutting speeds, feed rates and cutting depths, respectively, compared with that in conventional milling. Additionally, the tool life is prolonged approximately 2-5 times when the ultrasonic vibration method is applied. The tool wear pattern microcracks are only found in UAM. These might be of great importance for future research in order to understand the cutting mechanisms in UAM of in-situ TiB₂/7050Al metal matrix composites.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00294-2     

Optimization of Machining Characteristics for Al／SiCp Composites using ANN／GA

The present work is focused on optimization of machining characteristics of Al/SiCp composites.The machining characteristics such as specific energy,tool wear and surface roughness were studied.The parameters such as volume fraction of SiC,cutting speed and feed rate were considered.Artificial neural networks(NN) was used to train and simulate the experimental data.Genetic algorithms(GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics .Validation of optimized results was also performed by confirmation experiments.