Search Results (5)

The burr removal and finishing of drilled hole walls typically require multiple post-processing steps. This experimental study introduces a novel single-step drilling approach using modified drill bits for simultaneous burr removal and surface finishing in aluminum 6061-T6. The odified-1 drill, equipped with a deburring micro-insert, achieved superior results, with a chamfer height of −2.829 mm, drilling temperature of 40.28 ◦C, and surface roughness of 0.082 µm under optimal conditions. Multi-objective optimization using the RSM and MOGA-ANN identified the optimal drilling parameters for the Modified-1 drill at 3000 rpm under water lubrication as compared to dry conditions and kerosene. Experimental validation confirmed the high prediction accuracy, with deviations under 6%. These results establish the Modified-1 twist drill bit with a deburring
 micro-insert as a highly effective tool for burr-free high-quality drilling in a single operation. This innovative drill design presents an efficient, single-step solution for burr elimination, chamfer formation, and surface finishing in drilling operations. Full article

Modern Aircraft structures use titanium alloys where the processing of holes becomes essential to assemble aerospace parts. Considering the limitations of drilling, the study evaluates the helical milling for hole processing in Ti6Al4V. The experimental evaluation was conducted by considering burr size, surface roughness, machining temperature, and microhardness under coolant-free conditions. The axial feed and cutting speed were varied at three levels, and nine experiments were conducted. The results exhibit a lower machining temperature during helical milling than during drilling. In addition, the helical milling helped to lower the surface roughness and size of the exit burrs. However, helical-milled holes showed higher subsurface microhardness than conventionally drilled holes. The process variables were influential on machining temperature magnitude. The highest recorded temperature of 234.7 °C was observed at 60 m/min of cutting speed and 0.6 mm/rev feed. However, the temperature rise did not affect the microhardness. Strain hardening associated with mechanical deformation was the primary mechanism driving the increase in microhardness. Helical-milled holes exhibited an excellent surface finish at lower axial feeds, while chatter due to tool deformation at higher feeds (0.6 mm/rev) diminished the surface finish. The surface roughness increased by 98% when the cutting speed increased to 60 m/min from 20 m/min, while a moderate increment of 28% was observed when the axial feed increased to 0.6 mm/rev from 0.2 mm/rev. Furthermore, the formation of relatively smaller burrs was noted due to significantly lower thrust load and temperature produced during helical milling. Full article

In recent years, the use of hybrid composite stacks, particularly CFRP/Al assemblies, and fiber metal laminates (FMLs) has progressively become a convincing alternative to fiber-reinforced polymers (FRPs) and conventional metal alloys to meet the requirements of structural weight reduction in the modern aerospace industry. These new structural materials, which combine greater mechanical properties with low specific mass, are commonly assembled by riveted and bolted joints. The drilling operation, which represents the essential hole-making process used in the aerospace industry, proves particularly challenging when it comes to achieving damage-free holes with tight tolerances for CFRP/Al stacks in one-shot operations under dry conditions due to the dissimilar mechanical and thermal behavior of each constituent. Rapid and severe tool wear, heat damage, oversized drilled holes and the formation of metal burrs are among the major issues induced by the drilling of multi-material stacks. This paper provides an in-depth review of recent advancements concerning the selection of optimized strategies for high-performance drilling of multi-material stacks by focusing on the significant conclusions of experimental investigations of the effects of drilling parameters and cutting tool characteristics on the drilling performance of aerospace assemblies with CFRP/Al stacks and FML materials. The feasibility of alternative drilling processes for improving the hole quality of hybrid composite stacks is also discussed. Full article

Electrochemical machining is a promising non-traditional manufacturing process to make high-quality parts. The benefits of minimal thermally and mechanically induced stresses, free of burr, and a low surface roughness are appealing for industry and research institutes. However, the combined chemical reaction, electric field, fluid mechanics, and material properties involve a significant number of independent parameters which are difficult to analyze in order to draw comprehensive conclusions. To our current knowledge, process responses such as the material removal rate, optimal feed rate, and cutting profile cannot be represented accurately by analytical solutions. In recent years, deep learning has had tremendous success in analyzing sophisticated systems. The improved computation efficiency and reduced size of the training dataset required for deep learning have enabled various prediction models in the manufacturing industry. In this paper, a new approach is developed using the deep convolutional network with the Bayesian optimization algorithm to predict the diameters of the drilled hole from an electrochemical machining process. The Keras application programming interface (API) was used to build the deep convolutional network; the feed rate, pulse-on time, and voltage were used as input parameters to provide a fair comparison with a neural network from previous research. Random dropout layers were added to prevent overfitting of the network. Instead of tuning the network parameter by trial and error, the Bayesian parameter optimization algorithm was implemented to find the optimal set of parameters of the deep convolutional network that yields the minimum mean square error. The proposed algorithm was compared with a previously developed neural network with partially embedded physical knowledge. Improved training speed and accuracy were observed in comparison with the traditional neural network. The prediction model using the proposed deep learning algorithm demonstrated better prediction accuracy and provided a more systematic way to select the hyperparameter for the deep convolutional network. Full article

Hole quality in drilling is considered a precursor for reliable and secure component assembly, ensuring product integrity and functioning service life. This paper aims to evaluate the influence of the key process parameters on drilling performance. A series of drilling tests with new TiN-coated high speed steel (HSS) bits are performed, while thrust force and torque are measured with the aid of an in-house built force dynamometer. The effect of process mechanics on hole quality, e.g., dimensional accuracy, burr formation, surface finish, is evaluated in relation to drill-bit wear and chip formation mechanism. Experimental results indicate that the feedrate which dictates the uncut chip thickness and material removal rate is the most dominant factor, significantly impacting force and hole quality. For a given spindle speed range, maximum increase of axial force and torque is 44.94% and 47.65%, respectively, when feedrate increases from 0.04 mm/rev to 0.08 mm/rev. Stable, jerk-free cutting at feedrate of as low as 0.04 mm/rev is shown to result in hole dimensional error of less than 2%. A low feedrate along with high spindle speed may be preferred. The underlying tool wear mechanism and progression needs to be taken into account when drilling a large number of holes. The findings of the paper clearly signify the importance and choice of drilling parameters and provide guidelines for manufacturing industries to enhance a part’s dimensional integrity and productivity. Full article