Investigating the Effect of Cryo Treated Tool and Work Material in Ultrasonic Machining of Titanium Alloys

Abstract

Ultrasonic machining is a non-traditional, but established mechanical material removal process, generally suitable for hard and brittle materials such as quartz, ceramics, glass, semiconductors etc., carried out using shaped tools, high frequency mechanical motion and an abrasive slurry. USM neither involves chemical reaction nor is of thermal type and is also suitable for machining of electrically non-conductive and brittle work piece materials which are generally difficult to machine by conventional machining methods. Titanium is the fourth most abundant structural metal and ninth most abundant element in the earth’s crust. Titanium has been recognized as an element (Symbol Ti; atomic number 22; and atomic weight 47.9) for at least 200 years. Despite of being a difficult to machine material due to high chemical reactivity, tendency to weld to cutting tool, poor thermal conductivity that leads to accumulation of heat near the cutting edge of the tool, high retained strength and hardness at elevated temperature and low modulus of elasticity, titanium and its alloys find wide applications in aerospace, chemical, automotive, petroleum, medical and sporting goods industry. Commercially pure titanium alloys (ASTM Grades 1–4, 7, 11) are used mainly for their corrosion resistance properties in applications requiring adequate strength while high-strength alloyed forms are sub graded into three main groups: α – alloys, β- alloys and αβ – alloys and are used mostly for their superior strength-to-weight ratios and good corrosion resistance for applications in aerospace, automotive, and biomedical sector. The most popular of these, accounting for more than 50% of titanium usage worldwide and extensively used in aircraft construction for parts under low thermal stress, is Ti-6Al-4V that belongs to αβ- alloys. Using appropriate cutting tools with high hot hardness, good thermal conductivity, chemical inertness and high strength in conventional machining and use of non-conventional methods such as EDM can offer solution to these problems. Machining of Titanium with USM has also been reported by some authors. Cryogenic treatment is an inexpensive, one time, permanent treatment affecting the entire section of component unlike coatings. Cryogenic treatment is an add on process over conventional heat treatment wherein samples are cooled down to prescribed cryogenic temperature level at slow rate, maintained at this temperature for a long time and then brought back to room temperature. Depending upon the lowest temperature to which the xvii material is carried, cryogenic treatment is classified as Shallow Cryogenic Treatment (SCT) or Deep Cryogenic Treatment (DCT). The lowest temperature in the former is 193K which is near to temperature of dry ice while for the later it is 77K that is at the liquefying temperature of nitrogen. The current study was envisaged to explore the effect of cryogenic treatment together with other machining parameters on response variables such as MRR, TWR, SR, HOS and Tolerance Grades in ultrasonic machining of Ti-6Al-4V alloy. A preliminary pilot experimentation was carried out wherein various input factors were varied at several levels with one factor at a time approach. Finally the experimentation was carried out with the use of Design of Experiments (DOE) using Taguchi’s L18 Orthogonal Array. Based on the results of pilot experimentation and the objectives of the study, six factors comprising of USM power, abrasive slurry type, slurry grit size, tool material and type of cryogenic treatment given to tool and work material were selected for investigation in the DOE phase for measurement of five response variables viz Material Removal Rate (MRR), Tool Wear Rate (TWR), Surface Roughness (SR), Hole Oversize (HOS) and Tolerance Grades (TG). Whereas MRR was a higher the better characteristic, TWR, HOS and SR were lower-thebetter characteristics in the measurement of response variables. Among the five response variables, MRR was found to increase with increase in USM power and size of abrasive particles in the slurry owing to larger momentum associated with higher power and coarser particles. Stainless steel tool was found to form the best combination with the work material for maximum MRR. Harder abrasive slurry of boron carbide was also the most superior for increased MRR. USM power rating, type of abrasive slurry, tool material and abrasive grit size were found to be the significant parameters affecting material removal rate in ultrasonic machining of Ti-6Al-4V in the order of percentage contribution starting from highest. The optimum combination resulted from the design of experiments for maximum MRR corresponded to 400W of USM power, Boron Carbide Slurry, Stainless Steel tool material with #220 abrasive grit size using untreated tool material and cryogenic treated work material. However, tool wear was also found to increase at most of the points of maxima for MRR and was also found to be strongly determined by relative tool work-piece hardening. Increased power, coarser abrasive particles and harder slurry caused higher tool wear on the similar lines as with MRR. Titanium was found to minimize the tool wear while cryogenic treatment also significantly reduced the tool wear in USM. The optimum setting for minimum tool wear xviii rate was thus obtained corresponding to aluminium oxide slurry, titanium tool, 100W power, #500 abrasive grit size, deep cryogenic treated tool material and untreated work material. Surface roughness was significantly affected by abrasive particle size wherein the coarser particles led to bigger indentations causing poor surface finish. Higher power rating and harder tool were also associated with increased surface roughness which improved marginally with cryogenic treatment of the workpiece. Minimum surface roughness, and hence the best surface quality, was obtained when machining with aluminium oxide slurry, titanium tool, 20W power rating, #500 grit size and deep cryogenic treated tool and work-piece. Abrasive particle size was the dominating factor in establishing the hole oversize and hence tolerance grades. Bigger particle size led to bigger gap between tool and workpiece on the hole entrance causing oversize. Hole oversize was found to depend on rate of machining as well. Factors such as high power and coarser abrasives that lead to increased MRR also resulted in increased HOS. On the other hand HOS was also relatively more corresponding to lower power rating as compared to little higher values due to slow rate of machining. This means an optimum machining rate is essential to obtain minimum HOS. Accordingly, coarse grains, high power and hard tool material were associated with poor tolerance grades as well. The obtained tolerance grades ranged from IT 12 to IT 15. SEM Micrographs have been used to identify fracture at machined surface that comprised of brittle as well as ductile mode. The four responses have been modeled using ANN with appropriate network architecture. The correlation coefficient and deviation of ANN predicted results from the experimental results has been shown using appropriate graphs. Single hidden layer 6-7-1 neural network architecture based on LM algorithm using log-sigmoid and pure linear activation function for hidden and output layer respectively was able to effectively model the MRR, TWR and SR. The developed model exhibited reasonable accuracy of prediction within the range of the varied parameters. Entropy weight based Grey Relational Analysis hybridized with Taguchi’s methodology was used to optimize MRR and TWR. Subsequently, Analytical Hierarchical Process (AHP) for optimization was used to optimize the four responses together (MRR, TWR, SR and HOS). Weights were generated, firstly for the four responses and subsequently for the experimental outputs of all the responses as per the underlying theories of AHP.