Machining Process Conditions Research Articles

Assessment of Machining of Hastelloy Using WEDM by a Multi-Objective Approach

Superalloys are a much-needed material for abundant engineering fields, such as nuclear-powered reactor components and aeronautics. Owing to their exceptional characteristics, such as higher thermal conductivity, they can be difficult to machine using conventional processes. Modern approaches to machining have evolved to utilize these materials. One of the techniques studied in this project is electrical discharge in a wire machine. This process can help to reduce the energy consumption during machining and negative impact on the environment. In addition, shortening the operation time of the machine can help to minimize its impact on the environment. The duration of the pulse and applied current are independent factors considered in this study. Material removal rate, surface roughness, dimensional deviation, and form/orientation tolerance errors are deemed as performance measures. The goal of this investigation is to reduce the time required to machine and improve the surface finish of components by implementing a Grey-based artificial neural network model. This method is useful in foretelling the conditions of the Wire Electro Discharge Machining (WEDM) process. This paper uses the Taguchi design and Analysis of Variance (ANOVA) framework to analyze the model’s variable inputs. The overall best coefficient of correlation (R = 0.9981) is fetched with an RMSE value of 0.0086. The material removal rate has been increased by decreasing the time taken for removal, which gives the possibility of consuming minimum energy. The finishing of the machined surface also improved. Moreover, this paper shows how to use an Artificial Neural Network (ANN) model with Grey Analysis. The results of the comparative analysis show that the values envisaged are closer with the actual values. The foretelling capacity of the evolved model is confirmed with the performance analysis of the developed model.

Optimisation of surface residual stresses using ultrasonic-assisted milling for wire-arc additive manufactured Ni alloy components

Nickel alloys are cost intensive materials and generally classified as difficult-to-cut material. However, machining of these materials is needed especially in case of alloy 36 (1.3912), which is commonly used in mould construction for the production of fibre-reinforced composites. With regard to repair, modification and manufacturing of such components, additive manufacturing offers significant economic advantages. Nevertheless, subsequent machining steps are needed to achieve the final component contour and defined surface conditions. Dependent on the material and machining process conditions, detrimental tensile residual stresses may be the result on the machined surface, having negative impact on the component performance and safety. In this investigation, machining experiments were carried out on wire arc additive manufactured components made of alloy 36, varying the cutting speed and the feed rate. In addition, the conventional milling process (CM) was compared with a modern, hybrid machining process, the ultrasonic-assisted milling (US). The cutting forces and the surface-near residual stresses were analysed using X-ray diffraction. A significant improvement of the machinability as well as the surface integrity by using the ultrasonic assistance was observed, especially at low cutting speeds. The CM induced mainly tensile residual stresses, the US mainly compressive residual stresses.

Machining process condition monitoring based on ordinal pattern analysis and image matching

Machining process condition monitoring based on ordinal pattern analysis and image matching

PREDICTION AND ANALYSIS OF THE ROUGHNESS OF MILLED SURFACES BASED ON FUZZY LOGIC

The influence of the cutting conditions of machining process on the surface roughness has been the subject of several scientific works in order to optimize the milling process to get the best-finished surface machined by milling. During the last decades, many methods of artificial intelligence have been carried out to investigate the effect of milling conditions like the cutting speed, feed per tooth and depth of cut on surface integrity of machined surfaces by milling process. However, the progress on the use of Numerical Approaches to predict the surface integrity of machined surfaces like roughness, microhardness, residual stress and cutting temperature still lagging behind the other advances in the industry. The aim of this work is to use the fuzzy logic to predict the surface roughness of the milled surfaces and to study the effect of cutting parameters (cutting speed, feed per tooth and depth of cut) on the roughness of the surfaces machined by milling. a new model was created using fuzzy logic based on an experimental database. The database includes the variation of the surface roughness of machined surfaces of the Ti-6Al-4V by milling according to the cutting parameters (cutting speed, feed per tooth and depth of cut) on which the model was develop on MATLAB using fuzzy tool. The inputs of the fuzzy inference model were the three cutting parameters of milling: the cutting speed, feed per tooth and depth of cut, and the output of the fuzzy system was the roughness of the machined surfaces by milling of the Ti-6Al-4V. The predicted values of roughness obtained by the fuzzy model were compared to the experimental values and the result was very good, the average error rate was verry low that’s mean that the prediction model based on fuzzy logic works correctly and with high accuracy and can be used as a solution to predict the surface roughness before starting milling provided to respect a very specific range of parameters (defined by the universe of discourse) when using this model. The approach based on fuzzy logic can be used also to predict other phenomena of milling process like cutting temperature and microhardness.

An analytical formulation of ZOA-based machining stability for complex tool geometries: application to 5-axis ball-end milling

The paper describes a computationally convenient analytical formulation of the stability of the cutting process with respect to self-excited vibrations in the case of five-axis milling based on the commonly used zero-order approximation. In the case of five-axis milling with general milling cutters, it is difficult to calculate stable machining process conditions for two main reasons. The first reason is the difficulty of calculating the mean value of the cutting force Jacobian with respect to the regenerative displacement (closely related to a milling directional matrix) for a generally inclined tool, and the second reason is the nonlinearity of this Jacobian with respect to the process parameters, which means that the problem cannot be reduced to a linear eigenvalue problem as is usual for linear cases (e.g. cylindrical milling with respect to the axial depth of cut). In the first part, this paper presents a modification of the calculation of the machining stability limits for a nonlinearly dependent cutting force Jacobian. A new formulation of this Jacobian for a general tool based on the surface integral over the tool and workpiece engagement region is presented which leads directly to the mean value of the Jacobian of the cutting force (direction matrix) without the need to calculate it as a function of time and then calculate the mean value over one revolution. The advantage is that if we can analytically describe the engagement area, we also obtain an analytical relation for the cutting force Jacobian. This is presented with the practical example of a generally inclined ball-end mill. This analytical formulation of the force Jacobian allows the calculation of its derivatives with respect to the technological parameters (depth of cut, step over, tilt and lead angles), which is useful both for the calculation of stability diagrams and for solving optimization problems related to machining stability.

State of the art of tool texturing in machining

In most of the machining processes, the application of surface texturing is one of the most remarkable and promising research fronts in relation to the use of tribological concepts for improving machinability. This paper aims to review the most recent articles on this theme (from 2016 to date) to assist the researchers in the state of the art of this technology, guide the forthcoming related investigations, and thus, fill the identified research gaps. The texture effect in machining was comprehensively reviewed considering chip formation, cutting forces, cutting temperature, lubrication, surface integrity, and tool life. The present review highlights and summarizes the major findings with emphasis on the influencing parameters, such as workpiece and tool material, cutting process, lubri-cooling condition, texture geometry, and texture manufacturing technique. The overwhelming majority of the studies presented tool surface textures manufactured by laser ablation on the rake face of uncoated WC-Co tools. In this relation, turning was identified as mostly used machining process and dry condition as the chiefly implemented lubri-cooling environment. Moreover, most of the textures were noticed with linear grooves and dimple geometries, whereas the work materials mostly investigated were the hardened/high alloy steels and titanium alloys.

Machining parameter optimization using Adam – Gene Algorithm while turning lightweight composite using ceramic cutting tools

The machining of lightweight composite materials is a challenging process in the field of materials and manufacturing. The composites have materials with distinct phases of hard fibre and soft matrix, inducing the cutting tool edge to face varying cutting pressure. In this paper, alumina based ceramic cutting tools in different combinations [(i) Al2O3 + Ti [C,N] and (ii) Al2O3 + SiCw] are used to machine a glass fibre rod at different process conditions. From the experimentation, the performance of the tool is observed to measure the flank wear at individual process conditions. The tool wear found to be rapid and failure occurred with increase in cutting velocity. On other hand, it was found that the tool life decreases with increase cutting velocity and the performance of cutting tools is similar in both cases. Wear morphology was found to be smooth and regular on machining fibre material. A modified Adam – Gene algorithm (AGA) was used with a constraint, to find the optimal machining process condition. It is found that the Al2O3 + Ti [C,N] cutting tool has a better tool life with minimal production cost for an optimal process condition of 250 m/min and 0.1 mm/rev. Economically, the Al2O3 + Ti [C,N] cutting tool has lower production cost, however, Al2O3 + SiCw cutting tool has better performance.

Analysis of machining process and thermal conditions during vibration-assisted cortical bone drilling based on generated bone chip morphologies

When the temperature during bone drilling exceeds the safety threshold, the bone tissue surrounding the drilling site can be irreversibly damaged. To investigate the influence of vibration-assisted drilling (VAD) methods on the temperature increase during bone drilling and the causes for temperature increase, drilling experiments were performed on fresh bovine femur samples. The morphology and granularity distribution of the generated bone chips were innovatively used to directly compare the machining processes and thermal conditions of conventional drilling (CD), low-frequency vibration-assisted drilling (LFVAD), and ultrasonic vibration-assisted drilling (UVAD). The experimental results indicated that LFVAD produced the lowest temperature increase of 31.4°C, whereas UVAD produced the highest temperature increase of 44.1°C with the same drilling parameters. Additionally, the morphologies and granularity distributions of the bone chips significantly differed among these methods. We concluded that the smaller temperature increase in LFVAD was mainly attributed to the improved thermal conditions resulting from the periodic cutting/separation motion and the reliable geometric chip-breaking mechanism. In contrast, the unfavourable thermal conditions of UVAD were caused by the higher applied frequency, which created a significantly larger amount of friction heat. This was the main cause for the highest observed temperature increase, resulting in bone crushing processes that generated additional heat.

A Time-Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer.

Innovative monitoring systems based on sensor signals have emerged in recent years in view of their potential for diagnosing machining process conditions. In this context, preliminary applications of fast-response and low-cost piezoelectric diaphragms (PZT) have recently emerged in the grinding monitoring field. However, there is a lack of application regarding the grinding of ceramic materials. Thus, this work presents an analysis of the feasibility of using the acoustic emission signals obtained through the PZT diaphragm, together with digital signal processing in the time–frequency domain, in the monitoring of the surface quality of ceramic components during the surface grinding process. For comparative purpose, an acoustic emission (AE) sensor, commonly used in industry, was used as a baseline. The results obtained by the PZT diaphragm were similar to the results obtained using the AE sensor. The time–frequency analysis allowed to identify irregularities throughout the monitored process.

Experimental investigation and optimization of PAC parameters on Monel 400™ superalloy

ABSTRACTNickel-based superalloys are predominantly used to manufacture marine, automotive, and aerospace parts due to its excellent corrosion resistance in acidic environments and high-strength characteristics. Machining of these alloys are extremely difficult through conventional process because of their tendency to quickly work harden and poor thermal conductivity. Plasma arc cutting (PAC) is a disruptive metal-cutting process to perform cutting of difficult to cut materials even for intricate profiles. The present work examines the effect of PAC process variables such as arc current, gas pressure, cutting speed, and stand-off distance on evaluating the surface roughness, kerf width, and micro hardness of Monel 400™ superalloy. Experiments are designed and conducted using Box–Behnken design (BBD) of response surface methodology. The quadratic models are developed and assessed for its performance using analysis of variance (ANOVA). Optimal machining process conditions, cutting speed of 2427.08 mm/min, gas pressure of 3.83 bar, arc current of 45 A, and stand-off distance of 2.14 mm are obtained through multiobjective desirability approach. From the confirmation experiments, the relative error is found to be 2.68% for surface roughness, 4.45% for kerf width, and 4.36% for micro hardness which confirms the feasibility and efficiency of proposed methodology.

Multi-Objective Optimization of Surface Roughness and Cutting Forces in Hard Milling Using Taguchi and Response Surface Methodology

Cutting forces and surface roughness are important output parameters affecting the machining performance and quality of any machined surface in hard milling. In order to obtain the best surface quality and highest productivity, the input-cutting parameters need be considered and chosen properly whenever hard milling is involved. Therefore, in this paper, an attempt is made to conduct the multi-objective optimization of the surface roughness (Ra) and the resultant cutting force (Ft) in hard milling of SKD61 steel by Taguchi method and Response Surface Methodology (RSM). Values of the input parameters for milling tests are chosen through the stability lobe diagram of a machine tool simulated by the use of Cutpro software. The Taguchi method is used for designing all of the milling experiments. The values of Ra and Ft are measured by a Surftest SJ-400 and a dynamometer, respectively, and then analysis of variance is conducted to find out the effect of machining process conditions on Ra and Ft. In order to get the low Ft and Ra, a multi-objective optimization is implemented with the use of the desirability function. The results reveal that the optimized machining conditions for Ra and Ft are a cutting speed of 100 m/min, a feed rate of 0.015 mm/tooth, and a depth of cut of 0.44 mm, with predicted Ra of 0.206 µm and Ft of 66.58 N.

Sensitivity analysis-based process stability evaluation for one-of-a-kind production

Quality controlling and process stability evaluating for one-of-a-kind production are the key aspects in improving the major technical equipment production capability. However, it is difficult to find the relationships between upstream machining errors and the machining process conditions and the machining quality due to the samples’ insufficiency. A novel sensitivity analysis-based process stability evaluation for one-of-a-kind production is proposed to solve this problem. First, a variation evaluation model for one-of-a-kind production is established. Second, this model will be analyzed, and the safety and real feasible spaces are described with performance distribution analyses. Finally, the process stability index for one-of-a-kind production is calculated by comparing the relationship between the two spaces. Also, a case validates the effectiveness of the proposed method. It is expected that this paper would contribute to the research of innovative QC methods and process stability evaluation of one-of-a-kind production.

Turning induced residual stress prediction of AISI 4130 considering dynamic recrystallization

ABSTRACTMachining-induced residual stress distribution is strongly influenced by the machining process condition, tool geometry and workpiece material mechanical properties. The high temperature, large strain and high strain rate environment will promote the material micro-structural attribute changes. The material micro-structural attribute changes could directly affect the material mechanical properties. An analytical model is proposed for the residual stress prediction in the orthogonal turning by considering the material dynamical recrystallization induced grain growth effect. The grain size effect on the material flow stress behavior is included by adding a grain size dependent term into the traditional Johnson–Cook model. The Johnson–Mehl–Avrami–Kolmogorov model calculates the recrystallized volume fraction and grain size as a function strain, strain rate and time. The average grain size is calculated with a rule of mixture by volume. Then the modified Johnson–Cook model is embedded into a classic residual stress prediction model for the machining induced residual stress profile prediction on the machined workpiece surface. Experimental tests are conducted for the model validation. The predicted residual stress shows good approximation with the measurement in both the trend and magnitude of the residual stress. Also, the effects of cutting speed and feed rate on the residual stress profile are investigated.

The influence of the cooling conditions on the cutting tool wear and the chip formation mechanism

The paper presents the influence of the cutting zone cooling method on the wear of a cutting tool made of sintered carbide P25 during the process of turning AISI 1045 steel. The following cooling variants have been considered: dry machining, MQCL method and MQCL+EP/AW. The tests have been performed with different cutting speeds. This work also emphasizes the influence of the cutting tool wear on the chip shape and the indices of the chip formation zone: the chip thickening coefficient and the friction coefficient on the rake face. It has been found that during MQCL+EP/AW cooling, the tool wear decreases by 20% at the lowest cutting speed to 51% at the highest cutting speeds as compared to dry machining. Application of the EP/AW additive also results in a reduction of the friction coefficient on the rake face. In consequence it advantageously influences the chip shape. In a scanning analysis of the tool point, it has been found that a thin layer of a tribofilm is formed on the wedge surfaces, which results in an improvement of the machining process conditions.

Distortion caused by residual stresses in machining aeronautical aluminum alloy parts: recent advances

The distortion in machining aeronautical aluminum alloy parts (AAAPs) is one of the serious challenges in the aviation industry, and the residual stresses produced in multimanufacturing steps are the main cause. In order to get a comprehensive understanding of the problems about residual stresses and distortion in machining AAAPs, the state-of-the-art in several aspects including the generation reasons of residual stresses, the factors influencing distortion during machining, the measurement methods of residual stresses, the prediction and controlling methods of distortion are summarized in this paper. The generation mechanism of the bulk residual stress inner materials and the machining-induced residual stresses, as well as the factors affecting two kinds of residual stresses are stated. Also, the influences of residual stresses and machining process conditions on distortion are analyzed. Furthermore, the common residual stress measurement methods and its application scope are summarized. Significantly, the differences, advantages, and disadvantages of various prediction methods are analyzed. The methods of controlling distortion before and after machining are summarized. Finally, the paper gives out further research on the distortion in machining AAAPs in aeronautical manufacturing.

Twin-Control: A New Concept Towards Machine Tool Health Management

Twin-Control (http://twincontrol.eu/) is a new concept for machine tool and machining process performance optimization. It is based on a new simulation model that integrates the different aspects that affect machine tool and machining performance. This holistic approach will allow a better estimation of machining performance than single featured simulation packages, including lifecycle concepts like energy consumption and end-life of components.This theoretical representation of the machine is complemented with real process data by the monitoring of the most important variables of the machining process and machine condition. This monitored information, combined with the developed models, is used at machine level to perform model-based control actions and/or warn about damaged components of the machine tool. In addition, a fleet-level data management system is used for a proper health management and optimize the maintenance actions on the machine tools.

Multiobjective Optimization Under Uncertainty in Advanced Abrasive Machining Processes Via a Fuzzy-Evolutionary Approach

This paper considers multiobjective optimization under uncertainty (MOOUC) for the selection of optimal cutting conditions in advanced abrasive machining (AAM) processes. Processes considered are water jet machining (WJM), abrasive water jet machining (AWJM), and ultrasonic machining (USM). Decisions regarding the cutting conditions can involve optimization for multiple competing goals, such as surface finish, machining time, and power consumption. In practice, there is also an issue of variations in the ability to attain the performance goals. This can be due to limitations in machine accuracy or variations in material properties of the workpiece and/or abrasive particles. The approach adopted in this work relies on a strength Pareto evolutionary algorithm (SPEA2) framework, with specially tailored dominance operators to account for probabilistic aspects in the considered multiobjective problem. Deterministic benchmark problems in the literature for the considered machining processes are extended to include performance uncertainty and then used in testing the performance of the proposed approach. Results of the study show that accounting for process variations through a simple penalty term may be detrimental for the multiobjective optimization. On the other hand, a proposed fuzzy-tournament dominance operator appears to produce favorable results.

Research on Mathematical Model of Composite Micromachining of Laser and Electrolysis Based on the Electrolyte Fluid

A new technology of composite micromachining of laser and electrolysis is presented through a combination of technological advantages of laser processing and electrolytic machining. The implication of its method is that laser processing efficiently removes metallic materials and that pulse electrolytic machining removes recast layer and controls shape precisely. Machining accuracy and efficiency can be improved. The impacts that electrolyte fluid effectively cools the microstructure edge in the laser machining process and that gas-liquid two-phase flow makes the electrolyte conductivity produce uneven distribution in the electrolytic processing are considered. Some approximate assumptions are proposed on the actual conditions of machining process. The mathematical model of composite micromachining of laser and electrolysis based on the electrolyte fluid is built. The validity of the model can be verified by experimentation. The experimental results show that processing accuracy meets accuracy requirements which are ±0.05 mm. Machining efficiency increases more than 20 percent compared to electrolytic processing.

Architecture and implementation of closed-loop machining system based on open STEP-NC controller

Effective approach of collecting, transmitting, and handling complete product manufacturing information during machining process is necessary for realizing high efficiency manufacturing. In this paper, the architecture and implementation of closed-loop machining system (CLMS) is discussed and the method of realizing machining process control is introduced. An integrated information flow is built based on object-oriented description method to transfer complete product manufacturing information in CLMS. The functional and informational model of CLMS is established by using integration definition method. Online and real-time machining process control is implemented on an open STEP-NC controller which is collecting and analyzing information of machining process condition and inspection results during machining process. The software kernel of CNC controller implemented on a computer platform integrates with machine tool, sensors, and probe for real-time data collecting and online inspection. Finally, test parts are machined and inspected to verify the proposed machining process control method and open STEP-NC controller.

Advanced IT Methods of Signal Processing and Decision Making for Zero Defect Manufacturing in Machining

In the cutting zone of a machining process, several variables are influenced by process conditions: cutting force, vibrations, temperature, acoustic emission, power absorption. Some variables, useful for process monitoring, can be measured by sensors installed on the machine tool. However, when assessing a particular process variable, a single sensory source may not be able to meet all the requirements. A solution is sensor data fusion, the purpose of which is to combine sensory information from disparate sources so that the resulting intelligence is reinforced. Multi-sensor signal processing provides for the extraction and selection of signal features, relevant for the machining monitoring scope, that are assembled into sensor fusion pattern feature vectors functional for pattern recognition through knowledge based methods. Cognitive paradigms, such as artificial neural networks, can map input information fed by pattern feature vectors to output determinations for decision making on machining process conditions, including the adoption of corrective actions. Application cases of multi-sensor monitoring of machining process conditions investigated at the Fh-J_LEAPT Naples are reported with reference to: (a) workpiece residual stress assessment in turning of nickel base alloys; (b) tool wear state identification in machining of fiber reinforced composites; (c) chip form control in turning of C steel.