Strength modeling and optimizing ultrasonic welded parts of ABS-PMMA using artificial intelligence methods

This study analyzes variations in metal removal rate (MRR) and quality performance of roughness average (R _a) and corner deviation (CD) depending on parameters of wire electrical discharge machining (WEDM) process in relation to the cutting of pure tungsten profiles. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrated simulated annealing algorithm (SAA) were proposed to determine an optimal parameter setting. The results of 18 experimental runs via a Taguchi orthogonal table were utilized to train the BPNN to predict the MRR, R _a, and CD properties. Simultaneously, RSM and SAA approaches were individually applied to search for an optimal setting. In addition, analysis of variance was implemented to identify significant factors for the processing parameters. Furthermore, the field-emission scanning electron microscope images show that a lot of built-edge layers were presented on the finishing surface after the WEDM process. Finally, the optimized result of BPNN with integrated SAA was compared with that obtained by an RSM approach. Comparisons of the results of the algorithms and confirmation experiments show that both RSM and BPNN/SAA methods are effective tools for the optimization of parameters in WEDM process.     

Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology

A study on the radial-mode abrasive waterjet turning (AWJT) of 96 % alumina ceramic is presented and discussed. An experimental investigation is carried out to explore the influence of process parameters (including water pressure, jet feed speed, abrasive mass flow rate, surface speed, and nozzle tilted angle) on the material removal rate (MRR) when turning 96 % alumina ceramic. The experiments are conducted on the basis of response surface methodology (RSM) and sequential approach using face-centered central composite design. The quadratic model of RSM associated with the sequential approximation optimization (SAO) method is used to find optimum values of process parameters in terms of surface roughness and MRR. The results show that the MRR is influenced principally by the water pressure P and the next is abrasive mass flow rate m _a . The optimization results show that the MRR can be improved without increasing the surface roughness when machining 96 % alumina ceramic in the radial-mode abrasive waterjet turning process.     

Modelling and analysis of hybrid electrochemical turning-magnetic abrasive finishing of 6061 Al/Al2O3 composite

This paper integrates the electrochemical turning (ECT) process and magnetic abrasive finishing (MAF) to produce a combined process that improves the material removal rate (MRR) and reduces surface roughness (SR). The present study emphasizes the features of the development of comprehensive mathematical models based on response surface methodology (RSM) for correlating the interactive and higher-order influences of major machining parameters, i.e. magnetic flux density, applied voltage, tool feed rate and workpiece rotational speed on MRR and SR of 6061 Al/Al₂O₃ (10% wt) composite. The paper also highlights the various test results that also confirm the validity and correctness of the established mathematical models for in-depth analysis of the effects of hybrid ECT- MAF process parameters on metal removal rate and surface roughness. Further, optimal combination of these parameters has been evaluated and it can be used in order to maximize MRR and minimize SR. The results demonstrate that assisting ECT with MAF leads to an increase machining efficiency and resultant surface quality significantly, as compared to that achieved with the traditional ECT of some 147.6% and 33%, respectively.     

Fabrication of stepped hole on zirconia bioceramics by ultrasonic machining

Zirconia (ZrO₂) is a highly biocompatible ceramic material providing fracture strength properties that allow application as dental implants in biomedical engineering. In this present research, experimental analysis has been made for generating stepped hole on zirconia bioceramics with desired quality using ultrasonic machining (USM) process. Four independent controllable input process parameters are abrasive grain diameter, power rating, concentration of abrasive slurry, and tool feed rate. Material removal rate (MRR), overcut of larger diameter (OLD) hole, and overcut of smaller diameter (OSD) hole of stepped hole are considered as the responses. Response surface methodology (RSM) is used for modeling the performance of USM process. Multiobjective optimization has been performed to maximize the MRR and minimize the OLD hole and OSD hole of stepped holes. All the responses are improved at the optimal parametric condition and verified by confirmation test. The present research opens up the application feasibility of USM process for stepped hole generation on bioceramics and its utilization in biomedical field.     

Determining the optimum process parameter for grinding operations using robust process

We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine optimum parameters for minimum surface roughness (Ra) and vibration (Vb) in external cylindrical grinding. First, an experiment was conducted in a CNC cylindrical grinding machine. The TM using L ₂₇ orthogonal array was applied to the design of the experiment. The three input parameters were workpiece revolution, feed rate and depth of cut; the outputs were vibrations and surface roughness. Second, to minimize wheel vibration and surface roughness, two optimized models were developed using computer-aided single-objective optimization. The experimental and statistical results revealed that the most significant grinding parameter for surface roughness and vibration is workpiece revolution followed by the depth of cut. The predicted values and measured values were fairly close, which indicates (R _Ra ² =94.99 and R _Vb ² =92.73) that the developed models can be effectively used to predict surface roughness and vibration in the grinding. The established model for determination of optimal operating conditions shows that a hybrid approach can lead to success of a robust process.     

The multi-objective optimization of medium-speed WEDM process parameters for machining SKD11 steel by the hybrid method of RSM and NSGA-II

In the medium-speed wire electrical discharge machining (MS-WEDM) process, the extremely high temperature and massive electrical discharges in a fraction of 1 s result in the poor surface quality such as high tensile residual stresses, high surface roughness, white layers, and micro cracks. In this paper, an experimental plan for central composite design (CCD) of processing the tool steel (SKD11) has been conducted according to response surface methodology (RSM). The aim is to develop the mathematical model that can correlate the main process parameters of MS-WEDM with machining performance and to seek the optimal parameters on material removal rate (MRR) and 3D surface quality (Sq) by integrated RSM and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Then, a set of pareto-optimal solutions is obtained. Moreover, from the confirmation experiment, it has been proved that the optimal process-parameter combinations are suitable on the MRR and the 3D surface texture. Eventually, it has also demonstrated that the method of integrated RSM and NSGA-II is an effective way for multi-objective optimization.     

A multivariate surface roughness modeling and optimization under conditions of uncertainty

Correlated responses can be written in terms of principal component scores, but the uncertainty in the original responses will be transferred and will influence the behavior of the regression function. This paper presents a model building strategy that consider the multivariate uncertainty as weighting matrix for the principal components. The main objective is to increase the value of R² predicted to improve model’s explanation and optimization results. A case study of AISI 52100 hardened steel turning with Wiper tools was performed in a Central Composite Design with three-factors (cutting speed, feed rate and depth of cut) for a set of five correlated metrics (R_a, R_y, R_z, R_q and R_t). Results indicate that different modeling methods conduct approximately to the same predicted responses, nevertheless the response surface to Weighted Principal Component – case b – (WPC1^b) presented the highest predictability.     

Investigation of wire electrical discharge machining characteristics of Al6063/SiCp composites

In this investigation, the effect of wire electrical discharge machining (WEDM) parameters such as pulse-on time (T _ON), pulse-off time (T _OFF), gap voltage (V) and wire feed (F) on material removal rate (MRR) and surface roughness (R _a) in metal matrix composites (MMCs) consisting of aluminium alloy (Al6063) and silicon carbide (SiC_p) is discussed. The Al6063 is reinforced with SiC_p in the form of particles with 5%, 10% and 15% volume fractions. The experiments are carried out as per design of experiments approach using L₉ orthogonal array. The results were analysed using analysis of variance and response graphs. The results are also compared with the results obtained for unreinforced Al6063. From this study, it is found that different combinations of WEDM process parameters are required to achieve higher MRR and lower R _a for Al6063 and composites. Generally, it is found that the increase in volume percentage of SiC resulted in decreased MRR and increased R _a. Regression equations are developed based on the experimental data for the prediction of output parameters for Al6063 and composites. The results from this study will be useful for manufacturing engineers to select appropriate WEDM process parameters to machine MMCs of Al6063 reinforced with SiC_p at various proportions.     

An integrated optimization of cutting parameters and tool path generation in ultraprecision raster milling

This paper provides a new methodology for the integrated optimization of cutting parameters and tool path generation (TPG) based on the development of prediction models for surface roughness and machining time in ultraprecision raster milling (UPRM). The proposed methodology simultaneously optimizes the cutting feed rate, the path interval, and the entry distance in the feed direction to achieve the best surface quality in a given machining time. Cutting tests are designed to verify the integrated optimization methodology. The experimental results show that, in the fabrication of plane surface, the changing of entry distance improves surface finish about 40 nm (R _a ) and 200 nm (R _t ) in vertical cutting and decreases about 8 nm (R _a ) and 35 nm (R _t ) in horizontal cutting with less than 2 s spending extra machining time. The optimal shift ratio decreases surface roughness about 7 nm (R _a ) and 26 nm (R _t ) in the fabrication of cylinder surfaces, while the total machining time only increases 2.5 s. This infers that the integrated optimization methodology contributes to improve surface quality without decreasing the machining efficiency in ultraprecision milling process.     

Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites

In this study, the application of response surface methodology (RSM) and central composite design (CCD) for modeling, optimization, and an analysis of the influences of dominant machining parameters on thrust force, surface roughness and burr height in the drilling of hybrid metal matrix composites produced through stir casting route. Experiments are carried out using Al 356-aluminum alloy reinforced with silicon carbide of size 25 μm and Mica of size 45 μm. Drilling test is carried out using carbide drill of 6 mm diameter. The design of experiment concept has been used to optimize the experimental conditions. The experimental data are collected based on a three-factor-three-level full central composite design. The multiple regression analysis using RSM is used to establish the input–output relationships of the process. The mathematical models are developed and tested for adequacy using analysis of variance and other adequacy measures using the developed models. The main and interaction effect of the input variables on the predicted responses are investigated. The predicted values and measured values are fairly close, which indicate that the developed models can be effectively used to predict the responses in the drilling of hybrid metal matrix composites. The optimized drilling process parameters have been obtained by numerical optimization using RSM by ensuring the minimum thrust force of 84 N, surface roughness of 1.67 μm, and the burr height of 0.16 mm. After the drilling experiments, a scanning electron microscope (SEM) is used to investigate the machined surface and tool wear.     

Grinding-aided electrochemical discharge machining of particulate reinforced metal matrix composites

A grinding-aided electrochemical discharge machining (G-ECDM) process has been developed to improve the performance of the conventional ECDM process in machining particulate reinforced metal matrix composites (MMCs). The G-ECDM process functions under a combined action of electrochemical dissolution, spark erosion, and direct mechanical grinding. The tool electrode has a coating containing a hard reinforcement phase of diamond particles. The MMC employed in this study was Al₂O₃ particulate reinforced aluminum 6061 alloy. The material removal mechanism of this hybrid process has been analyzed. The results showed that the grinding action can effectively remove re-cast material deposited on the machining surface. The surface roughness (R _a) measured for the G-ECDM specimen was ten times smaller than that of the specimen machined without grinding aid (i.e., ECDM alone). Moreover, the material removal rate (MRR) of G-ECDM was about three times higher than that of ECDM under the experimental conditions of this study. The voltage waveform and crater distribution were also analyzed, and the experimental results showed that the G-ECDM process operates in a stable condition. The relative importance of the various processing parameters on MRR was established using orthogonal analysis. The results showed that MRR is influenced by the machining parameters in the order of duty cycle?>?current?>?electrolyte concentration. This study showed that the G-ECDM process is superior to the ECDM process for machining particulate reinforced MMCs, where a higher machining efficiency and a better surface quality can be obtained.     

Evaluation of the drilling-induced delamination of compound core-special drills using response surface methodology based on the Taguchi method

The hole-making process in composite parts today is required to be more accurate and efficient, which can affect the in-service life and decrease manufacturing cost. Thus understanding the key factors affecting their qualities is of vital importance to develop effective machining strategies. To this end, this study proposes a model with response surface methodology (RSM) based on the Taguchi method to evaluate the influence of drilling parameters on delamination by compound core-special drills. The model with RSM includes three steps: (1) design and experiments, (2) response surface modeling through regression, and (3) optimization. A series of experiments were conducted to test the proposed model. It was found that the key factors affecting drilling-induced delamination include: cutting velocity ratio, feed rate, inner drill type, and inner drill diameter. Therefore, some experimental implications can be proposed accordingly.     

Modeling and analysis of the effects of machining parameters on the performance characteristics in the EDM process of Al2O3+TiC mixed ceramic

Electric discharge machining (EDM) has achieved remarkable success in the manufacture of conductive ceramic materials for the modern metal industry. Mathematical models are proposed for the modeling and analysis of the effects of machining parameters on the performance characteristics in the EDM process of Al₂O₃+TiC mixed ceramic which are developed using the response surface methodology (RSM) to explain the influences of four machining parameters (the discharge current, pulse on time, duty factor and open discharge voltage) on the performance characteristics of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The experiment plan adopts the centered central composite design (CCD). The separable influence of individual machining parameters and the interaction between these parameters are also investigated by using analysis of variance (ANOVA). This study highlights the development of mathematical models for investigating the influences of machining parameters on performance characteristics and the proposed mathematical models in this study have proven to fit and predict values of performance characteristics close to those readings recorded experimentally with a 95% confidence interval. Results show that the main two significant factors on the value of the material removal rate (MRR) are the discharge current and the duty factor. The discharge current and the pulse on time also have statistical significance on both the value of the electrode wear ratio (EWR) and the surface roughness (SR).     

Finite element prediction of material removal rate due to traveling wire electrochemical spark machining

Manufacturing engineers are facing new challenges during machining of electrically nonconducting or partially conducting materials such as glass, quartz, ceramics, and composites. Traveling wire electrochemical spark machining (TW-ECSM), a largely unknown technology, has been applied successfully for cutting these types of materials. However, hardly any theoretical work has been reported related to machining performance of TW-ECSM process. The present work is an attempt in this direction. In the present work, a 3-D finite element transient thermal model has been developed to estimate the temperature field and material removal rate (MRR) due to Gaussian distributed input heat flux of a spark during TW-ECSM. First, the developed code calculates the temperature field in the workpiece and then MRR is calculated using this temperature field. The calculated MRR has been compared with the experimental MRR for verifying the approach. Computational experiments have been performed for the determination of energy partition and spark radius of a single spark. The effects of various process parameters such as energy partition, duty factor, spark radius, and ejection efficiency on MRR have been reported. It has been found that MRR increases with increase in energy partition, duty factor, and ejection efficiency but decreases with increase in spark radius.     

Application of non-dominated sorting genetic algorithm for multi-objective optimization of electrical discharge diamond face grinding process

Hybrid machining processes (HMPs), having potential for machining of difficult to machine materials but the complexity and high manufacturing cost, always need to optimize the process parameters. Our objective was to optimize the process parameters of electrical discharge diamond face grinding (EDDFG), considering the simultaneous effect of wheel speed, pulse current, pulse on-time and duty factor on material removal rate (MRR) and average surface roughness (Ra). The experiments were performed on a high speed steel (HSS) workpiece at a self developed face grinding setup on an EDM machine. All the experimental results were used to develop the mathematical model using response surface methodology (RSM). The developed model was used to generate the initial population for a genetic algorithm (GA) during optimization, non-dominated sorting genetic algorithm (NSGA-II) was used to optimize the process parameters of EDDFG process. Finally, optimal solutions obtained from pareto front are presented and compared with experimental data.     

Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique

The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness (R_a). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGA-II gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.     

Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite

This paper reports on an experimental investigation of small deep hole drilling of Inconel 718 using the EDM process. The parameters such as peak current, pulse on-time, duty factor and electrode speed were chosen to study the machining characteristics. An electrolytic copper tube of 3 mm diameter was selected as a tool electrode. The experiments were planned using central composite design (CCD) procedure. The output responses measured were material removal rate (MRR) and depth averaged surface roughness (DASR). Mathematical models were derived for the above responses using response surface methodology (RSM). The results revealed that MRR is more influenced by peak current, duty factor and electrode rotation, whereas DASR is strongly influenced by peak current and pulse on-time. Finally, the parameters were optimized for maximum MRR with the desired surface roughness value using desirability function approach.     

Material removal rate and electrode wear ratio study on the powder mixed electrical discharge machining of cobalt-bonded tungsten carbide

In this article, a material removal rate (MRR) and electrode wear ratio (EWR) study on the powder mixed electrical discharge machining (PMEDM) of cobalt-bonded tungsten carbide (WC-Co) has been carried out. This type of cemented tungsten carbide was widely used as moulding material of metal forming, forging, squeeze casting, and high pressure die casting. In the PMEDM process, the aluminum powder particle suspended in the dielectric fluid disperses and makes the discharging energy dispersion uniform; it displays multiple discharging effects within a single input pulse. This study was made only for the finishing stages and has been carried out taking into account the four processing parameters: discharge current, pulse on time, grain size, and concentration of aluminum powder particle for the machinability evaluation of MRR and EWR. The response surface methodology (RSM) has been used to plan and analyze the experiments. The experimental plan adopts the face-centered central composite design (CCD). This study highlights the development of mathematical models for investigating the influence of processing parameters on performance characteristics.     

A study on fiber laser micro-spot welding of thin stainless steel using response surface methodology and simulated annealing approach

This study analyzed variations of shear strength that depend on the fiber laser process during micro-spot welding of AISI 304 stainless thin sheets. A preliminary study used ANSYS results to obtain initial process conditions. The experimental plan was based on a Taguchi orthogonal array table. A hybrid method that includes the response surface methodology (RSM)- and back propagation neural network (BPNN)- integrated simulated annealing algorithm (SAA) is proposed to search for an optimal parameter setting of the micro-spot welding process. In addition, an analysis of variance was implemented to identify significant factors influencing the micro-spot welding process parameters, which was also used to compare the results of BPNN-integrated SAA with the RSM approach. The results show that the RSM and BPNN/SAA methods are both effective tools for the optimization of micro-spot welding process parameters. A confirmation experiment was also conducted in order to validate the optimal welding process parameter values.     

Knowledge-based substep deterministic optimization of large diameter thin-walled Al-alloy tube bending

Regarding increasing applications with mass quantities, diverse specifications, and close quality tolerance, the precision bending of large diameter thin-walled (LDTW) Al-alloy tube should be efficiently achieved. However, bending of LDTW Al-alloy tube is a highly tri-nonlinear process with possible multi-defect, needing strict coordination of various bending tools and processing parameters. Considering the coupling effects of various forming parameters on multiple defects, this study developed a knowledge-based substep methodology to solve the deterministic optimization of LDTW Al-alloy tube bending with multi-objective and multi-variable under multiple factor constraints. Considering narrow forming window under small bending radii (R _b?<?2D, R _b—bending radius, D—initial tube diameter), a finite element (FE)-based stepwise iterative search method is proposed to optimize key forming parameters of LDTW Al-alloy tube under small R _b, and the search direction is based on bending knowledge. While for large R _b bending with wide optional ranges of forming parameters, a hybrid optimization approach is used by combining virtual design of experiment, FE simulation, approximate response surface model, sequential quadratic programming algorithm, or genetic algorithm. Using orthogonal experimental method, three-dimensional (3D)-FE simulation, experiential data, and analytical formulae, knowledge on key forming parameters, coupling effects on multiple defects, effect significance, and design rules are obtained as well as initial values and design ranges. By several practical bending scenarios with D up to 100 mm, the proposed substep deterministic optimization methodology for LDTW Al-alloy tube bending is evaluated.