Optimal Machining Research Articles

PMP-SVM: A Hybrid Approach for effective Cancer Diagnosis using Feature Selection and Optimization

Cancer disease is becoming a prominent factor in increasing the death ration over the world due to the late diagnosis. Machine Learning (ML) is playing a vital role in providing computer aided diagnosis models for early diagnosis of cancer. For the diagnosis process the microarray data has its own place. Microarray data contain the genetic information of a patient with a large number of dimensions such as genes with a small sample such as patient details. If the microarray is directly taken without reducing the dimension as the input to any ML model for classification, then Small Sample Size is the resulting issue. So, size of the microarray data needs to be reduces by using either of dimensionality reduction technique or the feature selection technique to increase the model’s performance. In this work, proposed a hybrid model using Principal Component Analysis (PCA), Maximum Relevance Minimum Redundancy (MRMR), Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) for cancer diagnosis. PCA and MRMR is used for feature selection and PSO is applied to get the optimized feature set. Finally, SVM is applied as the classification model. The proposed model is evaluated against multiple cancer microarray datasets to measure the performance in terms of accuracy, precision, recall, and F1 score. Result shows that proposed model performs better than existing state of art model.

Parametric Analysis of Tool Wear, Surface Roughness and Energy Consumption during Turning of Inconel 718 under Dry, Wet and MQL Conditions

Economy and productivity are the two most important elements of modern manufacturing systems. Economy is associated with energy-efficient operations, which results in an overall high input-to-output ratio, while productivity is related to quality and quantity. This specific work presents experimental investigations of the use of cooling conditions (dry, MQL and wet) as input variables alongside other input parameters, including depth of cut, feed and cutting speed. This research aimed to investigate the variation in output responses including tool wear, specific cutting energy, and surface roughness while machining Inconel 718, a nickel-based super alloy. For experimentation, three levels of depth of cut, feed, and cutting speed were chosen. The Taguchi method was used for the experimental design. The contribution ratio of each input parameter was ascertained through analysis of variance (ANOVA). Use of coolant showed a positive effect on process parameters, particularly MQL. By adapting the optimum machining conditions, specific cutting energy was improved by 27%, whereas surface roughness and tool wear were improved by 15% and 30%, respectively.

Efficient Feature Selection in High Dimensional Data Based on Enhanced Binary Chimp Optimization Algorithms and Machine Learning

Feature selection with the highest performance accuracy is the biggest win for multidimensional data. The Chimpanzee Optimization Algorithm (ChOA) serves as a crucial technique for dealing with multidimensional global optimization issues. However, ChOA often lacks fast convergence and good selection of sensitive attributes leading to poor performance. To address these issues, most significant features were selected using two variants of ChOA called BChimp1 and BChimp2 (BChimp1 and BChimp are available at : https://www.mathworks.com/matlabcentral/fileexchange/133267-binary-chimpoptimization-algorithm-forfeatures-selection. September 22, 202). BChimp1 selects the optimal solution from the four best possible solutions and it applies a stochastic crossover on four moving solutions to deeply speed-up convergence level. BChimp2 uses the sigmoid function to select the significant features. Then, these features were trained using six-well known classifiers. The proposed techniques tend to select the most significant features, speed up the convergence rate and decrease training time for high-dimensional data. 23 standard datasets with six well-known classifiers were employed to assess the performance of BChimp1 and BChimp2. Experimental results validate the efficiency of BChimp1 and BChimp2 in enhancing accuracy by 83.83% and 82.02%, and reducing dimensionality by 42.77% and 72.54%, respectively. However, time-evaluation results of BChimp1 and BChimp2 in all datasets showed fast convergence and surpassed current optimization algorithms such as PSO, GWA, GOA, and GA.

A Built-In Integrated Magnetorheological Fluid Brake Permanent Magnet Synchronous Motor

In this paper, a built-in integrated magnetorheological (MR) fluid brake permanent magnet synchronous motor is presented, which can switch between rotation and brake operation states in a relatively simple structure by integrating the MR fluid brake inside the rotor of the PMSM. Benefiting from the different flux paths design corresponding to the armature winding in the outer stator and the braking winding in the inner stator, the proposed machine can provide adequate brake torque and improve space utilization. This paper illustrates machine topology, operation principles, and design considerations. After proceeding flux leakage reduction, magnetic pole layout optimization of the braking winding, and balance optimization between brake torque and electromagnetic torque, successively, the optimal machine is obtained. Finally, the finite element analysis is devoted to the electromagnetic performance analysis of the proposed machine, and torque capabilities between rotation and brake states are compared, verifying the feasibility of the integration design.

Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic Algorithm

The gravity-aided inertial navigation system (GAINS) can achieve precise positioning of underwater vehicles in a gravity-suitable area. However, there are generally shortcomings in the existing intelligent suitable area selection methods in terms of selecting gravity feature parameters and learning parameters. In this paper, an intelligent suitable area selection method is proposed based on an improved support vector machine by the genetic algorithm (GA-SVM) to address the aforementioned problems. Firstly, the genetic algorithm (GA) is utilized to independently pick out the optimal feature subset of 15 existing gravity feature parameters and obtain the optimal support vector machine (SVM) learning parameters while eliminating irrelevant redundant features, thus improving the classifier’s performance and generalization ability. Then, the SVM classifier is trained according to the optimal information output by GA, and the accuracy of the test set is 95.5%. Finally, the classifier is utilized to distinguish suitable and unsuitable areas in the application region to evaluate the proposed method’s performance. The TERCOM experiment in the suitable area resulted in an average positioning error of less than 170 m.

Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study

BackgroundIdentifying patients with hepatocellular carcinoma (HCC) at high risk of recurrence after hepatectomy can help to implement timely interventional treatment. This study aimed to develop a machine learning (ML) model to predict the recurrence risk of HCC patients after hepatectomy. MethodsWe retrospectively collected 315 HCC patients who underwent radical hepatectomy at the Third Affiliated Hospital of Sun Yat-sen University from April 2013 to October 2017, and randomly divided them into the training and validation sets at a ratio of 7:3. According to the postoperative recurrence of HCC patients, the patients were divided into recurrence group and non-recurrence group, and univariate and multivariate logistic regression were performed for the two groups. We applied six machine learning algorithms to construct the prediction models and performed internal validation by 10-fold cross-validation. Shapley additive explanations (SHAP) method was applied to interpret the machine learning model. We also built a web calculator based on the best machine learning model to personalize the assessment of the recurrence risk of HCC patients after hepatectomy. ResultsA total of 13 variables were included in the machine learning models. The multilayer perceptron (MLP) machine learning model was proved to achieve optimal predictive value in test set (AUC = 0.680). The SHAP method displayed that γ-glutamyl transpeptidase (GGT), fibrinogen, neutrophil, aspartate aminotransferase (AST) and total bilirubin (TB) were the top 5 important factors for recurrence risk of HCC patients after hepatectomy. In addition, we further demonstrated the reliability of the model by analyzing two patients. Finally, we successfully constructed an online web prediction calculator based on the MLP machine learning model. ConclusionMLP was an optimal machine learning model for predicting the recurrence risk of HCC patients after hepatectomy. This predictive model can help identify HCC patients at high recurrence risk after hepatectomy to provide early and personalized treatment.

Predicting seismic collapse probability of the building isolated with triple friction pendulums using machine learning

Seismic collapse probability is an important engineering demand for seismic collapse assessment of structures, including isolated buildings using triple friction pendulum bearings (TFPBs). Generally obtaining collapse probability needs an extensive analytical analysis which makes it time-consuming and requires skillful analysts. To remedy the cumbersomeness, an optimal machine learning (ML) method based on genetic algorithm is proposed in this study and the corresponding ML model is further developed to predict the important demand using limited number of parameters defining TFPB isolated system. For comparison, four conventional ML methods namely random forest, k-nearest neighbours, extreme gradient boosting and artificial neural networks are employed to develop the ML models. The data for constructing each ML model is obtained from time-history analyses of the isolated building subjected to earthquake ground motions. The results show that all ML methods can confidently predict the collapse probability. Among them, the optimal ML method possesses the best performance, the prediction accuracy is 0.9999 and the root mean square error value is approximately close to 0.

Development of an algorithm for the reasoned selection of machines for leather garments manufacturing

The technology of processing clothing elements is very mobile and changes with the appearance of new materials and equipment. The selected design and technological solutions that ensure the conformity of aesthetic properties and requirements can be satisfied through continuous improvement of sewing production technologies. This study aims to develop an algorithm for choosing the optimal equipment, taking into account the material parameters and the permissible requirements for the technological process of manufacturing sewing products from artificial leather. As a result of the analysis of the range of sewing machines and manufacturing companies, Juki sewing machines were chosen to build a complex matrix of machine equipment. A morphological scheme of the selection process and a mathematical description of the two-dimensional matrix of elements have been developed. That has made it possible to reflect the simultaneous consideration of all process components and their influence on the technological parameters of the machine. The database of the parameters of the equipment selection system was built in the form of matrix elements: a matrix of the type of operations, a matrix of material coatings, a matrix of the base of materials, a matrix of machine assignments, and a matrix of qualifications. Each matrix is a production rule for making a decision at a separate selection stage. The operation matrix was constructed using the TechLab mobile application, which includes 50 processing schemes for artificial leather products. The analysis of the schemes has made it possible to determine the frequency of occurrence of types of seams for processing artificial leather products: 1.01.01 (28.75 %), 2.01.01 (16.75 %), 1.06.02 (11.00 %), 2.02.01 (15.50 %), 5.01.01 (24.25 %), 6.02.01 (3.75 %). The mathematical notation of the algorithm for choosing the optimal machine equipment has made it possible to visualize the structure of the process using a graph. The graph was built using Gephi. Such a notation takes into account the qualification of the worker, the type of technological operation, and the material’s properties, including the material’s thickness, coating, and base.

Data mining and library generation to search electron-rich and electron-deficient building blocks for the designing of polymers for photoacoustic imaging

Photoacoustic imaging is a good method for biological imaging, for this purpose, materials with strong near infrared (NIR) absorbance are required. In the present study, machine learning models are used to predict the light absorption behavior of polymers. Molecular descriptors are utilized to train a variety of machine learning models. Building blocks are searched from chemical databases, as well as new building blocks are designed using chemical library enumeration method. The Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method is employed for the creation of 10,000 novel polymers. These polymers are designed based on the input of searched and selected building blocks. To enhance the process, the optimal machine learning model is utilized to predict the UV/visible absorption maxima of the newly designed polymers. Concurrently, chemical similarity analysis is also performed on the selected polymers, and synthetic accessibility of selected polymers is calculated. In summary, the polymers are all easy to synthesize, increasing their potential for practical applications.

Impurity-Based Important Features for feature selection in Recursive Feature Elimination for Stock Price Forecasting

Stock investors perform stock price forecasting based on technical indicators and historical stock prices. The large number of technical indicators and historical data often leads to overfitting and ambiguity in forecasting using machine learning. In this paper, we proposed a feature selection approach using impurity-based important features in recursive feature elimination for stock price forecasting. The data utilized includes historical data and various moving averages. Feature selection is employed to reduce the number of features and obtain important and relevant features. The recursive feature elimination with impurity-based important features is utilized as the feature selection method. The machine learning methods employed are linear regression, support vector regression, multi-layer perceptron regression, and random forest regression. The measurement results of mean squared error (mse), root mean squared error (rmse), mean absolute error (mae), and mean absolute percentage error (mape) show that the optimal feature selection and machine learning method is achieved with six features and linear regression. The average mse, rmse, mae, and mape values are 0.000279, 0.016577, 0.012843, and 1.42236%, respectively. These results validate the effectiveness of impurity-based important features for feature selection in recursive feature elimination using historical data and various moving averages in stock price forecasting.

Macbeth ve Gri İlişkisel Analiz Yöntemleri İle En Uygun Süt Sağım Makinesinin Belirlenmesi

Milking machines are an important element of the livestock sector, which is one of the main activities of the countries. Milking machines have become a part of the life of livestock keepers. Such equipment can be considered as applications of mechanical engineering on the livestock sector. Especially livestock enterprises are going through a difficult process in supplying such machines with optimum features and maximum benefit. In terms of productivity, competitiveness and sustainability of livestock sector enterprises, decision-making processes should be scientific. With this perspective, in this study, the problem of determining the optimum milking machine was evaluated with Multi-Criteria Decision Making (MCDM) methods. In the study, six different milking machines were analyzed with two different MCDM methods according to eight criteria. In this frame, the criterion weights of the related decision problem were calculated by the MACBETH method. Moreover, MACBETH and Gray Relational Analysis (GIA) methods were used separately to determine the most suitable milking machine. Furthermore, rankings obtained+- by different methods were tested with Spearman Rank Correlation Analysis and the result was found to be highly positive. The results of the study were shared with the decision makers. Besides, academic, and sectoral suggestions were made for future studies on similar topics.

A new intelligence model for evaluating clay compressibility in soft ground improvement: a combined approach of bees optimization and extreme learning machine

A new intelligence model for evaluating clay compressibility in soft ground improvement: a combined approach of bees optimization and extreme learning machine

An Improved Mayfly Optimization Algorithm for Type-2 Multi-Objective Integrated Process Planning and Scheduling

The type-2 multi-objective integrated process planning and scheduling problem, as an NP-hard problem, is required to deal with both process planning and job shop scheduling, and to generate optimal schedules while planning optimal machining paths for the workpieces. For the type-2 multi-objective integrated process planning and scheduling problem, a mathematical model with the minimization objectives of makespan, total machine load, and critical machine load is developed. A multi-objective mayfly optimization algorithm with decomposition and adaptive neighborhood search is designed to solve this problem. The algorithm uses two forms of encoding, a transformation scheme designed to allow the two codes to switch between each other during evolution, and a hybrid population initialization strategy designed to improve the quality of the initial solution while taking into account diversity. In addition, an adaptive neighborhood search cycle based on the average distance of the Pareto optimal set to the ideal point is designed to improve the algorithm’s merit-seeking ability while maintaining the diversity of the population. The proposed encoding and decoding scheme can better transform the continuous optimization algorithm to apply to the combinatorial optimization problem. Finally, it is experimentally verified that the proposed algorithm achieves better experimental results and can effectively deal with type-2 MOIPPS.

Genomic prediction of pig growth traits based on machine learning.

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.

Recent advances in the interface structure prediction for heteromaterial systems

The atomic structures of solid-solid interfaces in materials are of fundamental importance for understanding the physical properties of interfacial materials, which is, however, difficult to determine both in experimental and theoretical approaches. New theoretical methodologies utilizing various global optimization algorithms and machine learning (ML) potentials have emerged in recent years, offering a promising approach to unraveling interfacial structures. In this review, we give a concise overview of state-of-the-art techniques employed in the studies of interfacial structures, e.g., ML-assisted phenomenological theory for the global search of interface structure (ML-interface). We also present a few applications of these methodologies.

Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques.

To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC). This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set. A total of 154 patients (59.2 ± 9.1years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657). The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set. Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance. • Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.

Evolution of the Interelectrode Gap during Co-Rotating Electrochemical Machining

A new co-rotating electrochemical machining method is presented to machine the complex structure inside annular parts such as flame tubes and aero-engine casings. Due to the unique shape and motion of electrodes, it is difficult to accurately compute the electric field intensity in the machining area. In this paper, the complex electric field model is simplified by conformal transformation, and the analytical solution of electric field intensity is exactly calculated. A material removal model is built on the basis of the electric field model, and the dynamic simulation of the material removal process is realized. The effects of the cathode radius, applied voltage, feed rate and initial interelectrode gap on the interelectrode gap (IEG) and material removal rate (MRR) are analyzed. The simulation results indicate that the MRR is always slightly less than the feed rate in a quasi-equilibrium state, resulting in a slow reduction in IEG. In addition, the final machining state is not affected by the initial IEG, and the MRR in a quasi-equilibrium state is determined by the feed rate. Several comparative experiments were carried out using the optimized processing parameters, in which the MRR and IEG were measured. The convex structures were successfully machined inside the annular workpiece with optimum machining parameters. The experimental results are in good agreement with the theoretical results, indicating that the established model can effectively predict the evolution process of MRR and IEG.

Predictive modeling of roughness change in multistep machining

Following sustainability in manufacturing, the machining chain can be optimized by either reducing the time and energy consumption of each operation or eliminating the unnecessary operations subjected to keeping the quality of the final product as consistent. However, the roadblock in designing an optimum machining chain is lack of prediction tool to interact between the included operations. In this paper, an integrated algorithm is developed to simulate the surface roughness generation and following modification caused by milling and burnishing, respectively. Predict the surface roughness generation by milling process and its alternation after burnishing. The algorithm works on the basis of clouds of points which were generated in the engagement region of tool and workpiece and their transformation from tool to workpiece coordinate systems. Moreover, some mechanical attributes of the process regarding effect of surface work hardening and elastic rebound were added to the algorithm to enhance the accuracy of simulation. To verify the results, a series of burnishing experiments with multi-roller rotary tool have been carried out on the surface of the finish-milled samples and the surface roughness change was taken into investigation. The obtained results showed that by applying the work hardening and springback effect to predictive algorithm the prediction accuracy of roughness at submicron level enhances up to 50%. It was also found that the most influential parameters influencing the surface roughness after milling-burnishing sequence are milled surface roughness, burnishing force and pass number. In addition, results showed that applying burnishing after rough machining consumes lots of energy to achieve nanoscale surface finish. Accordingly, the sequence of rough-milling, finish-milling and burnishing results in achieving sound surface finish within significantly shorter period of time and applied force.

Process model design for positive displacement compressors and their experimental validation: Comparison of Optimal Experimental Design and Machine Learning

Valid simulation models play a critical role in enhancing efficiency in development processes and minimizing experimental efforts. As a result, ensuring accurate predictions through model discrimination, Model Calibration, and Validation (MoCaVal) has become increasingly important and is a necessary step to analyze the behavior and evaluate the effectiveness of systems during their initial design stages. Additionally, to achieve the climate objectives, it is imperative that heat pumps predominantly perform the building sector’s heating. The effectiveness of heat pumps hinges on the performance of the compressor. Therefore, there is a requirement for valid compressor models. However, currently, there is no universal compressor model that applies to all refrigerants and designs. Therefore, every refrigerant or design modification requires the development or adaptation of models. Since creating new models is a time-consuming procedure, automated techniques are advantageous.This paper compares two automated model development methods, Optimal Experimental Design (OED) and Machine Learning (ML), to create valid simulation models within the MoCaVal framework. OED is used to calibrate existing simulation models, while ML is employed to develop new models. Our comparison of the two methods focuses on a fixed-speed scroll compressor of 4 kW nominal power, using R410A and featuring 51 measurement points. Using the Full Factorial Plan (FFP) and the D-Optimal Experimental Design (D-OED), predefined models for mass flow rate, electrical power, isentropic and volumetric efficiency were calibrated. Employing the FFP, Machine Learning (ML) was also applied through support vector regression.The OED reduces experimental effort by 75–90 % compared to the FFP while only slightly increasing the average uncertainty by 0.30–0.79 %. For the FFP, MoCaVal achieved uncertainties of 0.52–3.9 % for calibrated models and 0.34–0.56 % with ML models. Therefore, within the design space, ML outperforms calibrated models based on the FFP and OED regarding uncertainty. However, since machine learning models were trained on the FFP, additional research is needed to demonstrate their potential for reducing test time and extrapolating results.

Seagull Optimization-based Feature Selection with Optimal Extreme Learning Machine for Intrusion Detection in Fog Assisted WSN

Seagull Optimization-based Feature Selection with Optimal Extreme Learning Machine for Intrusion Detection in Fog Assisted WSN