Development of Ultrasonic Vibration-Assisted Magnetic Compound Fluid (MCF) Polishing Technology

This study investigates the development of an ultrasonic vibration-assisted magnetic compound fluid (MCF) polishing technology for final polishing. The fabrication of an experimental apparatus entails an ultrasonic polishing unit, and the experimental investigation of its performance in surface polishing is described. In addition, ultrasonic vibration-assisted MCF polishing under different applied methods of ultrasonic vibration is studied. The experimental results indicate that applying ultrasonic vibration to the workpiece improves the surface roughness and material removal rate when the ultrasonic vibrations are changed. In addition, across the range of polishing conditions employed in this study, the precision surface roughness and high material removal rate can be easily obtained on the acrylic plate by applying an elliptical vibration to the ultrasonic vibration.

EVALUATION OF SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN ELECTRICAL DISCHARGE MACHINING OF AL-ALLOY WITH 10%SIC

Aluminum-based metallic matrix compounds are widely used in industrial and aircraft manufacturing due to their advanced characteristics, such as toughness and high strength resistance to weight ratio, etc. Silicon carbide is an important industrial ceramic and it is the fourth hardest ceramic after diamond, boron nitride, and boron carbide. Owing to its low fracture toughness, it is difficult to machine silicon carbide using traditional machining processes. Electrical discharge machine can machine such materials irrespective of their hardness. Aluminum alloy 6061 and 10% SiC based-metal matrix composite were used as a workpiece that was produced by stir casting. In the experimental investigation, pulse current Pc (10, 20, and 30 A), pulse on (Pon) duration (100, 150, and 200 ?sec), and pulse off (Poff) duration (6, 12, and 24 ?sec) were treated as the input variables. The output responses were surface roughness (SR) and material removal rate (MRR). The best value for surface roughness (Ra) reached (1.032 µm) at Pc (10 A), Pon duration (100 ?sec) and Poff (15 ?sec). Also, the best result for the productivity of the process (MRR) reached (69.49 × 10-3 g/min) at Pc (30 A) Pon, (200 ?sec) and (6 ?sec) Poff. Therefore, the experimental outcomes were optimized for surface roughnes and material removal rate by adding 10% SiC to aluminum alloy 6061. ABSTRAK: Sebatian matrik logam berasaskan aluminium telah digunakan secara meluas dalam industri pembuatan dan pesawat kerana ciri-cirinya yang canggih, seperti ketahanan dan daya rintangan yang tinggi kepada nisbah berat, dan lain-lain. Silikon karbida adalah seramik industri yang penting dan ia merupakan seramik keempat terkuat setelah berlian, boron nitrida dan boron karbida. Disebabkan ketahanan frakturnya yang rendah, adalah sukar bagi menghasilkan mesin silikon karbida menggunakan proses pemesinan tradisional. Mesin pelepasan elektrik mampu menghasilkan mesin menggunakan bahan tersebut tanpa mengira kekerasan. Aloi aluminium 6061 dan komposit matrik logam berasaskan SiC 10% telah digunakan sebagai bahan kerja yang terhasil melalui tuangan kacauan. Melalui penyelidikan eksperimen, detik arus Pc (10, 20, dan 30 A), detik hadir (Pon) berdurasi (100, 150, dan 200 ?sec), dan detik henti (Poff) berdurasi (6, 12, dan 24 ?sec) dirawat sebagai pemboleh ubah input. Respon pengeluaran adalah kekasaran permukaan (SR) dan kadar penyingkiran bahan (MRR). Nilai terbaik bagi kekasaran permukaan (Ra) telah mencapai (1.032 µm) pada Pc (10 A), berdurasi Pon (100 ?sec) dan Poff (15 ?sec). Tambahan, hasil terbaik bagi proses produktiviti (MRR) mencapai (69.49 × 10-3 g/min) pada Pc (30 A) Pon, (200 ?sec) dan (6 ?sec) Poff. Oleh itu, hasil eksperimen dioptimumkan bagi permukaan kasar dan kadar penyingkiran bahan dengan tambahan 10% SiC ke aloi aluminium 6061.

Optimizing Material Removal Rate Using Artificial Neural Network for Micro-EDM

Machining can be classified into conventional and unconventional processes. Unconventional Machining Process attracts researchers as it has many processes whose physics is still not that clear and they are highly in market-demand. To predict and understand the physics behind these processes soft computing is being used. Soft computing is an approach of computing which is based on the way a human brain learns and get trained to deal with different situations. Scope of this chapter is limited to one of the soft computing optimizing techniques that is artificial neural network (ANN) and to one of the unconventional machining processes, electrical discharge machining process. This chapter discusses about micromachining on Electric Discharge Machining, its working principle and problems associated with it. Solution to those problems is suggested with the addition of powder in dielectric fluid. The optimization of Material Removal Rate (MRR) is done with the help of ANN toolbox in MATLAB.

Optimization of Machining Parameters for Milling Zirconia Ceramics by Polycrystalline Diamond Tool

Zirconia ceramics are widely used in many fields because of their excellent physical and mechanical properties. However, there are some challenges to machine zirconia ceramics with high processing efficiency. In order to optimize parameters for milling zirconia ceramics by polycrystalline diamond tool, finite element method was used to simulate machining process based on Johnson-Cook constitutive model. The effects of spindle speed, feed rate, radial and axial cutting depth on cutting force, tool flank wear and material removal rate were investigated. The results of the simulation experiment were analyzed and optimized by the response surface method. The optimal parameter combination was obtained when the spindle speed, feed rate, radial and axial cutting depth were 8000 r/min, 90.65 mm/min, 0.10 mm and 1.37 mm, respectively. Under these conditions, the cutting force was 234.81 N, the tool flank wear was 33.40 μm when the milling length was 60 mm and the material removal rate was 44.65 mm3/min.

Surface and Subsurface Quality of Titanium Grade 23 Machined by Electro Discharge Machining

Electrical Discharge Machining (EDM) is a non-traditional cutting technology that is extensively utilized in contemporary industry, particularly for machining difficult-to-cut materials. EDM may be used to create complicated forms and geometries with great dimensional precision. Titanium alloys are widely used in high-end applications owing to their unique intrinsic characteristics. Nonetheless, they have low machinability. The current paper includes an experimental examination of EDM’s Ti-6Al-4V ELI (Extra Low Interstitials through controlled interstitial element levels) process utilizing a graphite electrode. The pulse-on current (IP) and pulse-on time (Ton) were used as control parameters, and machining performance was measured in terms of Material Removal Rate (MRR), Tool Material Removal Rate (TMRR), and Tool Wear Ratio (TWR). The Surface Roughness (SR) was estimated based on the mean roughness (SRa) and maximum peak to valley height (SRz), while, the EDMed surfaces were also examined using optical and SEM microscopy and cross-sections to determine the Average White Layer Thickness (AWLT). Finally, for the indices above, Analysis of Variance (ANOVA) was conducted, whilst semi-empirical correlations for the MRR and TMRR were given using the Response Surface Method (RSM). The results show that the pulse-on time is the most significant parameter of the machining process that may increase the MRR up to 354%. Pulse-on current and pulse-on time are shown to have an impact on the surface integrity of the finished product. Furthermore, statistics, SEM, and EDX images on material removal efficiency and tool wear rate are offered to support the core causes of surface and sub-surface damage. The average microhardness of the White Layer (WL) is 1786 HV.

Hard Turning ASSESSMENT on EN31 Steel in Dry and Wet Cooling Environments USING Grey-Fuzzy Hybrid Optimization APPROACH

In recent years, machining of hard-to-cut metals by hard turning process is an embryonic technology for machining industry and research development. Hard turning is generally defined as the material removal process of hardened steel having hardness greater than 45 HRC. The current research presents a comparative hard turning investigation on EN 31 (56 ± 1 HRC) grade steel using physical vapor deposition (PVD) coated carbide tool under dry and wet cooling. The selection of a better cooling strategy among dry and wet cooling was based on the value of obtained surface roughness (Ra) and material removal rate (MRR) in hard turning. Wet cooling exhibited better performance over dry cutting as lower Ra and greater MRR are achieved with wet cooling. Further, considering Taguchi L16 orthogonal array, hard turning experiments were executed in wet cooling and responses like surface roughness (Ra), material removal rate (MRR), and diameter error were studied. Further, the Grey-fuzzy hybrid optimization tool was employed and found improved results relative to the alone grey relational analysis as about 9 % less Ra and 2.612 times more MRR is noticed at the grey fuzzy optimal set of parameters.