Enhancing the Performance Measures of Abrasive Water Jet Machining on Drilling Acrylic Glass Material

Abrasive Water jet Machining finds its application on extensive range of materials. Both ductile and brittle materials can be machined by this process, but the material removal is different in both the cases, i.e., by Ductile fracture and Brittle fracture respectively. When it comes to brittle materials, conventional machining processes cannot be used due to a number of limitations of the material. Thus, non-conventional machining is a rather preferable choice for brittle materials. However, the quality of machining might vary, as it is dependent on some input parameters such as — Abrasive size, Water jet Pres-sure and Abrasive Flow rate. The quality of the machining for a drilling operation is evaluated based on the hole parameters i.e. — Circularity, Taper ratio, Overcut and Material Removal Rate (MRR). The process parameters are varied in accordance to the desirable outcome to be obtained. Outcomes like MRR and circularity are required to be maximized, whereas Taper ratio and Overcut have to be minimized. The effects and interactions between different parameters on the outcomes are studied using Analysis of Variance (ANOVA)

Machinability and surface integrity investigation during helical hole milling in AZ31 magnesium alloy

Conventional drilling has been widely used for making holes in structural materials. However, drawbacks like high cutting forces, poor surface finish, high cutting temperatures, excessive tool wear, and undesirable burr formation while drilling magnesium alloys have necessitated the development of alternative hole-making methods. Lately, the helical milling process has attracted interest in facilitating hole-making for assembly applications. However, the machinability of magnesium alloys using the helical milling process needs more investigation. Therefore, the presented work analyzed the influence of axial pitch, tangential feed, and spindle speed on cutting forces and surface integrity while milling AZ31 magnesium alloy. Axial feed was the most crucial factor contributing to the thrust force (71.8%), followed by tangential feed (13.2%). All three process variables impacted the radial force. Spindle speed was the most influential variable affecting the surface roughness (48.7%), followed by axial pitch (31.4%) and tangential feed (12.5%). Microhardness closer to the free surface of the hole was higher than the subsurface hardness. Moreover, microhardness showed an upward trend with the rise in axial pitch and tangential feed; however, it reduced with increased spindle speed

Machining Temperature, Surface Integrity and Burr Size Investigation during Coolant-Free Hole Milling in Ti6Al4V Titanium Alloy

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

Addressing Uncertainty in Tool Wear Prediction with Dropout-Based Neural Network

Data-driven algorithms have been widely applied in predicting tool wear because of the high prediction performance of the algorithms, availability of data sets, and advancements in computing capabilities in recent years. Although most algorithms are supposed to generate outcomes with high precision and accuracy, this is not always true in practice. Uncertainty exists in distinct phases of applying data-driven algorithms due to noises and randomness in data, the presence of redundant and irrelevant features, and model assumptions. Uncertainty due to noise and missing data is known as data uncertainty. On the other hand, model assumptions and imperfection are reasons for model uncertainty. In this paper, both types of uncertainty are considered in the tool wear prediction. Empirical mode decomposition is applied to reduce uncertainty from raw data. Additionally, the Monte Carlo dropout technique is used in training a neural network algorithm to incorporate model uncertainty. The unique feature of the proposed method is that it estimates tool wear as an interval, and the interval range represents the degree of uncertainty. Different performance measurement matrices are used to compare the proposed method. It is shown that the proposed approach can predict tool wear with higher accuracy

Comparative Study of Mechanical Properties of Aluminum Alloy A356 (Al-12Si) Fabricated by Directed Energy

Additive Manufacturing technology is rapidly gaining traction in many manufacturing applications due to its process parameters control and wide range of applications. There are other AM technologies accessible, however Direct energy deposition (DED) is a critical approach in metal matrix additive manufacturing. The significant two mechanical properties tensile strength and hardness of additively created Al-12Si Aluminum alloy by DED process and pressure die casted Al-12Si components are compared in this study. The strength and modulus of the DED and PDC manufactured components were identical if the load direction in the UTM machine was the same as the construction directions, however other mechanical parameters differed slightly. Mechanical qualities of fabricated products made from reused powders were also comparable to those made from unused powder.Mechanical Engineerin

Strategized friction stir welded AA6061-T6/SiC composite lap joint suitable for sheet metal applications

Friction Stir Welding (FSW) is one of the best choices of joining light weight metal structures involving lap joints as conventional welding methods find difficult in getting sound weld joints. Aluminium alloys are lightweight materials relatively used in space frame structures, ship buildings and panels of automotive vehicles. Main objective of this research is to investigate Al–SiC FSW lap joints and to examine the suitable pre-fill nanoparticles in the configured grooves to augment the weld strength. Three different volume additions of SiC nanoparticles and tool rotational speed were considered to fabricate lap joints for the investigations. Tool rotational speed and volume of SiC reinforcement have a significant impact on the dissemination of SiC in the weldment and weld quality. The spread of SiC nanoparticles in the weld area strongly affects the weld microstructure and mechanical characteristics. Nano SiC filled lap joint has shown a significant improvement in mechanical properties as compared to a joint without nanoparticles. The best lap joint is achieved with 20 vol% of SiC and 1500 rpm of rotational speed. The investigations imply that the tensile strength is decreased for joints with 26 vol% of SiC, due to clustering of reinforcement particles. The supplement of SiC nanoparticles restricts the growth of grains and refines the stir zone (SZ) microstructure and hence this method of weld process is recommended for lap weld joints in metal structures

Impact of print orientation on morphological and mechanical properties of L-PBF based AlSi7Mg parts for aerospace applications

The Laser-Powder Bed Fusion (L-PBF) based AlSi7Mg parts are adopted for the aerospace industries, especially for making antenna, RF components, gyroscopes, and waveguides. The primary use of additive manufacturing in the aerospace industry is because it enables producing very lightweight components with complex designs. However, the mechanical properties of manufactured L-PBF components are not yet fully validated because numerous challenges posed by the process itself and defects that occur on the manufactured materials by L-PBF. In this research, to further elucidate the printing mechanism of AlSi7Mg lightweight alloy a detailed investigation was carried out to analyze the influence of print orientation on the physical, morphological, and mechanical behavior of L-PBF based AlSi7Mg parts. AlSi7Mg parts were manufactured with various print orientations from vertical, inclined and horizontal. The fabricated parts were analyzed for microstructural behavior using an optical microscope (OM), and scanning electron microscope (SEM) with energy dispersive X-ray analysis (EDAX) proving the elemental composition of the AlSi7Mg parts were studied. Mechanical testing such as tensile, hardness, wear, fracture toughness, and shear tests was carried out for various orientations manufactured in order to evaluate their properties. The vertically-oriented AlSi7Mg parts shows 13.8 %, 58.4 % and 7.9 % higher tensile, toughness and shear strength when compared with the horizontally-oriented parts. But the maximum wear resistance was observed in the horizontal parts and it was 52.9 % higher wear resistance than the vertical parts. The results can be used as a guide in the aerospace industry in order to design components with high structural integrity

Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance

Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance

A Comprehensive Review of Self-Healing Polymer, Metal, and Ceramic Matrix Composites and Their Modeling Aspects for Aerospace Applications

Composites can be divided into three groups based on their matrix materials, namely polymer, metal and ceramic. Composite materials fail due to micro cracks. Repairing is complex and almost impossible if cracks appear on the surface and interior, which minimizes reliability and material life. In order to save the material from failure and prolong its lifetime without compromising mechanical properties, self-healing is one of the emerging and best techniques. The studies to address the advantages and challenges of self-healing properties of different matrix materials are very limited; however, this review addresses all three different groups of composites. Self-healing composites are fabricated to heal cracks, prevent any obstructed failure, and improve the lifetime of structures. They can self-diagnose their structure after being affected by external forces and repair damages and cracks to a certain degree. This review aims to provide information on the recent developments and prospects of self-healing composites and their applications in various fields such as aerospace, automobiles etc. Fabrication and characterization techniques as well as intrinsic and extrinsic self-healing techniques are discussed based on the latest achievements, including microcapsule embedment, fibers embedment, and vascular networks self-healing