Intelligent machining methods for Ti6Al4V: a review

Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision- making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence- based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.publishe

Estimation of surface roughness on Ti-6Al-4V in high speed micro end milling by ANFIS model

379-389Titanium and its alloys are a few of the most suitable materials in medical applications due to their biocompatibility, anticorrosion and desirable mechanical properties compared to other materials like commercially pure Nb & Ta, Cr-Co alloys and stainless steels. High speed micro end milling is one of the favorable methods for accomplishing micro features on hard metals/alloys with better quality products delivering efficiently in shorter lead and production times. In this paper, experimental investigation of machining parameters influence on surface roughness in high speed micro end milling of Ti-6Al-4V using uncoated tungsten carbide tools under dry cutting conditions and prediction of surface roughness using adaptive neuro- fuzzy inference system (ANFIS) methodology has been presented. Using MATLAB tool box - ANFIS approach four membership functions - triangular, trapezoidal, gbell, gauss has been chosen during the training process in order to evaluate the prediction accuracy of surface roughness. The model’s predictions have been compared with experimental data for verifying the approach. From the comparison of four membership functions, the prediction accuracy of ANFIS has been reached 99.96% using general bell membership function. The most influential factor which influences the surface roughness has the feed rate followed by depth of cut

Impact of Palm Oil based Minimum Quantity of Lubrication on Machinability of Ti and its Alloy (Ti-6AI-4V)

This project investigates the usage of palm oil as a metal cutting fluid in minimum quantity lubrication assisted turning operations and its effect on surface roughness, tool wear and cutting temperature for Titanium alloy Ti-6Al-4V. Artificial Neural Network models were developed to determine the optimum cutting parameters considering the sustainability of palm oil in titanium alloy machining to improve future manufacturing costs and qualities

Comparison between the machinability of different titanium alloys (Ti-6Al-4V and Ti-6Al-7Nb) employing the multi-objective optimization

Titanium and its alloys are amongst the most important metallic materials used by many industries, such as those pertaining to the aerospace, automotive, and biomedical sectors. This is due to the reliability and functionality of titanium components, in addition to their high strength-to-weight ratio and corrosion resistance. Thus, titanium and its alloys are of great importance to the challenging operations of these sectors. The manufacturing of titanium requires great accuracy to ensure that resulting products meet quality requirements, due to its difficult machinability. In this study, the cutting forces and surface roughness of the turning were analysed to compare different titanium alloys, Ti–6Al–4V and Ti–6Al–7Nb, with CVD-coated and uncoated inserts. The effect of control factors on the response variables was measured using ANOVA. Response surface methodology was applied to the creation of a model of responses and to a bi-objective optimization process via the normalized normal constraint method. The Pareto-optimal sets of both alloys were achieved, which may be applied to practical situations to achieve optimal results for these responses. The models and optimization results confirmed the similarity of machinability values between the Ti–6Al–4 V and Ti–6Al–7Nb alloys. The uncoated inserts yielded the best surface roughness and cutting force results when used with both titanium alloys.publishe

Numerical modeling and analysis of Ti6Al4V alloy chip for biomedical applications

The influence of cutting forces during the machining of titanium alloys has attained prime attention in selecting the optimal cutting conditions to improve the surface integrity of medical implants and biomedical devices. So far, it has not been easy to explain the chip morphology of Ti6Al4V and the thermo-mechanical interactions involved during the cutting process. This paper investigates the chip configuration of the Ti6Al4V alloy under dry milling conditions at a macro and micro scale by employing the Johnson-Cook material damage model. 2D modeling, numerical milling simulations, and post-processing were conducted using the Abaqus/Explicit commercial software. The uncut chip geometry was modeled with variable thicknesses to accomplish the macro to micro-scale cutting by adapting a trochoidal path. Numerical results, predicted for the cutting reaction forces and shearing zone temperatures, were found in close approximation to experimental ones with minor deviations. Further analyses evaluated the influence of cutting speeds and contact friction coefficients over the chip flow stress, equivalent plastic strain, and chip morphology. The methodology developed can be implemented in resolving the industrial problems in the biomedical sector for predicting the chip morphology of the Ti6Al4V alloy, fracture mechanisms of hard-to-cut materials, and the effects of different cutting parameters on workpiece integrity

Machinability of cobalt-based and cobalt chromium molybdenum alloys - a review

Cobalt chrome molybdenum alloy is considered as one of the advanced materials which is widely gaining popularity in various engineering and medical applications. However, it is categorized as difficult to machine material due to its unique combination of properties which include high strength, toughness, wear resistance and low thermal conductivity. These properties tend to hinder the machinability of this alloy which results in rapid tool wear and shorter tool life. This paper presents a general review of the materials’ characteristics and properties together with their machinability assessment under various machining conditions. The trend of machining and future researches on cobalt-based and cobalt chromium molybdenum alloys are also discussed adequately

Mono and Multi-Objective Optimization and Modeling of Machining Performance in Face Milling of Ti6Al4V Alloy

Titanium alloys are extensively used in numerous industries like aerospace, automotive, military, etc., due to their exclusive characteristics. But machining these alloys has always been challenging for manufacturers. This research investigates the effect of radial depth of cut on cutting forces, tool life, surface roughness (Ra), and material removal rate (MRR) during face milling of Ti6Al4V alloy. It also aims to perform mono and multi-objective optimization of response characteristics to determine the optimal input parameters, namely cutting speed, feed rate, and radial depth of cut. Taguchi method and analysis of variance (ANOVA) have been used for mono-objective optimization, whereas Taguchi-based Grey relational analysis (GRA) and Genetic algorithm (GA) have been used for multi-objective optimization. Regression analysis has been performed for developing mathematical models to predict Ra, tool life, average cutting forces, and MRR. According to ANOVA analysis, the most significant parameter for tool life is cutting speed. For MRR and average cutting force (Avg. FY), the most influential parameter is the radial depth of cut. On the other hand, feed rate is the most significant parameter for Ra and average feed force (Avg. FX). The optimal combination of input parameters for tool life and Avg. FY is 50 m/min cutting speed, 0.2 mm/rev feed rate, and 7.5 mm radial depth of cut. However, the optimal parameters for Ra are 65 m/min cutting speed, 0.2 mm/rev feed rate, and 7.5 mm radial depth of cut. For Avg. FX, the optimal conditions are 57.5 m/min cutting speed, 0.2 mm/rev feed rate, and 7.5 mm radial depth of cut. Similarly, for MRR, the optimal parameters are 65 m/min cutting speed, 0.3 mm/rev feed rate, and 12.5 mm radial depth of cut. A validation experiment has been conducted at the optimal Ra parameters, which shows an improvement of 31.29% compared to the Ra measured at the initial condition. A minor error has been found while comparing the experimental data with the predicted values calculated from the mathematical models. GRA for multi-objective (3 objectives: tool life, Ra, and Avg. FY) optimization has improved 55.81% tool life, 6.12% Ra, and 23.98% Avg. FY. ANOVA analysis based on grey relational grade has demonstrated that radial depth of cut is the most significant parameter for multi-objective (three objectives) optimization during the face milling of Ti6Al4V. The results obtained from the GRA considering four output characteristics (tool life, Ra, Avg. FY, and MRR) are compared with GA optimization results for both roughing and finishing, and a negligible deviation has been observed

Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

© 2019 by SAGE Publications Ltd.Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication–assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication–assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.Peer reviewedFinal Published versio

The selected laser melting production and subsequent post-processing of Ti-6Al-4V prosthetic acetabular

&nbsp;Processing and post processing of human prosthetic acetabular cup by using 3D printing. The results showed using 3D printers leads to fabrication customized implants with higher quality.<br /