Machining Simulation Research Articles

Bayesian approach to determine force model parameters for the prediction of cutting forces in turning operations

The prediction of cutting forces is a major topic in predesigning turning operations. An efficient method to predict cutting forces are machining simulations taking mechanistic force models into account. However, their accuracy highly depends on the model parameters, whose calibration requires numerous time- and cost-consuming experiments. In order to calibrate model parameters with low experimental effort, this study presents a Bayesian Makrov Chain Monte Carlo (MCMC) method. Force model parameters are conditioned to only few measurements of turning experiments and lead to accurate results of machining simulations. Due to low experimental effort, this approach is of major interest for industrial applications.

A Novel 2D Micromilling FEM simulation strategy to optimize the flow stress law of IN625

The products miniaturization tendency of the last years led to an acceleration of micromilling process development. Considering the high-quality requirements, a deep knowledge of this operation, concerning ploughing-shearing transition, tool run-out, and tool edge radius effects, is mandatory, especially when machining difficult-to-cut materials. For this reason, this paper introduces a novel 2D micromachining Finite Element Method simulation strategy for micromilling forces evaluation, when cutting IN625. The major output of this technique consists in the computation of an optimized flow stress law, suitable for the simulation of high-speed machining. Particle Swarm Optimization method was employed for optimizing the flow stress parameters by comparing the cutting force predicted by an analytical model previously calibrated on experimental data, providing good agreement. This strategy permits the micromilling process predictive analysis, avoiding costly optimization experimental tests.

Hyper-heuristics applications to manufacturing scheduling: overview and opportunities

modern industrial challenges require solutions that are both efficient and reactive. Heuristic-based approaches allow production systems to react quickly to unexpected events and disturbances. Thus, the term Hyper-Heuristic (HH) covers a wide variety of techniques that allow the selection or generation of heuristics. To conciliate the speed of heuristics with the necessary global performance, a number of mechanisms have been proposed in the literature. This paper first presents the HHs. Afterward, inspired from previous works, a classification is given to categorize them. In addition, an enhancement is proposed. Indeed, a third type of HHs is added (i.e. to classic selection HHs and generation HHs). This new category is named “mixed” HHs. Then, different contributions from the literature to dynamic scheduling are highlighted. Before concluding, a number of trends and future directions such as the use of Machine Learning and simulation are explored.

Tool life prediction in end milling using a combination of machining simulation and tool wear progress data

Tool life prediction in end milling using a combination of machining simulation and tool wear progress data

Towards an accurate estimation of heat flux distribution in metal cutting by machine learning

This study presents a machine learning-based approach for inverse identification of heat flux distribution on the rake face of the cutting tools in machining. This approach includes temperature measurements from thermocouples embedded in the tool and heat transfer finite element (FE) simulations to create the data required to train the ML model. The identified heat flux distribution is compared with the distribution from FE machining simulations for validation. The results show a clear potential to estimate the heat flux distribution in machining more efficiently by using an ML-based inverse approach.

Evolution of microstructures in high and low Ti/Al ratio ferritic stainless steels after hot rolling

The evolution of microstructures after hot rolling was studied on high and low Ti/Al ratio ferritic stainless steels. The steels were hot rolled using the Gleeble 1500D® thermomechanical simulation machine. The evolution of the microstructures was compared using the electron backscattered diffraction (EBSD) mode of scanning electron microscope (SEM) and optical microscopy (OM) methods by studying the grain sizes and recrystallization after hot rolling. The results showed that after hot rolling, the grain structure of the high Ti/Al ratio steel was more pancake like and smaller with high volume fraction of recrystallized grains. These recrystallised grains were mostly localised around TiNb(C,N) particles, which suggested particle stimulated nucleation (PSN) of grains. It was therefore seen that PSN occurred in both steels and therefore there was supposed fine grains and improved texture in both steels but the initial grain size prior to deformation overrides the effect of occurrence of PSN. Starting deformation with fine grained material yields higher recrystallization microstructures and textures.

Multiscale simulation approach to predict the penetration depth of oil between chip and tool during orthogonal cutting of AISI 4140

Cooling lubricants in machining perform important tasks, from cooling and lubrication of the friction partners in contact to the removal of the separated chips. An essential, determining and largely unresolved question in relation to cooling lubricants in machining is to what extent the coolant can get into the cutting zone. The aim of this paper is to address this question by using a multiscale approach to determine the penetration of the cooling lubricant gap. This is achieved by multiscale simulations by means of coupling the results of flow, structural and continuum mechanical simulations. Comparatively, the results of the simulated machining operation are compared with experimental orthogonal cutting tests of AISI 4140.

Design of Micro Five-axis CNC Machine Tool and Research on Virtual Simulation Machining

By studying the structure and functional characteristics of five-axis linkage CNC machine tool, design five-axis CNC machine tool with X, Y, Z linear motion axes, B and C rotation axes. The virtual simulation system of miniature five-axis machine tool is built based on the VERICT, and an impeller part was simulated and processed as an example. Check whether the state of each moving axis in the machine tool movement is correct, and judge the rationality of the structure design of the machine tool based on the error information such as interference and collision in the processing. It is proved by simulation that the machine can realize five-axis linkage machining within the design range and meet the design requirements.

Statistical Tests for Comparing Machine Learning Algorithms

Abstract: The average result of machine learning models is determined using naive 10-fold cross-validation, paired Student's ttest, McNemar's test. The algorithm with the best average performance should outperform those with the worst. But what if the difference in average results is due to a statistical anomaly? A statistical hypothesis test is used to determine whether the mean results of the differences between the three algorithms are real. Using statistical hypothesis testing, this study will show how to compare machine learning algorithms. When choosing a model, the output of several machine learning algorithms or simulation channels is compared. The model that performs best based on your performance measure becomes the final model that can be used to predict new data. With classification and regression prediction models, this can be done using traditional machine learning and deep learning methods. It is difficult to determine whether the difference between the two models is accurate or not.

Research on Intelligent Control System of Hydraulic Support Based on Position and Posture Detection

In the study of coal mining, the position and posture, the working status, and the straightness of the hydraulic support are critical to the safety of the coal mine. To detect and control the position of a hydraulic support, a model for solving the self-position and posture and a model for solving the relative position and pose of a hydraulic support based on the principle of three points determining a plane in space were established. Then, with the help of particle swarm optimization algorithm and Bang-Bang control algorithm, the hydraulic support position and the posture detection and control system are constructed. Finally, according to the requirements of sensor data collection and online monitoring of the position and posture of hydraulic support, a hydraulic support posture detection and control system is developed, and the simulation experiments of hydraulic support cylinder movement sample collecting, hydraulic support following motion and hydraulic support independent following machine are carried out on the self-developed three-machine intelligent control simulation experiment platform of the working face. However, the test under this simulation platform did not add the load of the top beam. The results show that the self-pose solution model and the relative-pose solution model of the hydraulic support can be used to quickly monitor the position and posture of the hydraulic support group. In the hydraulic support cylinder following motion movement, the largest following error of each cylinder is the balance cylinder, and its maximum error is 2.6 mm. In the hydraulic support independent following machine simulation, the maximum motion error of the support base is 2.2 mm. The position and posture detection and control system developed for hydraulic support can realize accurate detecting and control the position and posture of hydraulic support, as well as meet the requirements for industrial applications.

Spiral tool path optimization method for fast/slow tool servo-assisted diamond turning of freeform surfaces with highly form accuracy

Spiral tool path generation methods for ultra-precision diamond turning of freeform surfaces can significantly affect form accuracy and machining efficiency. However, there is no previous research treating the tool path generation as a multi-objective optimization problem. In this study, a multi-objective optimization method for tool path generation based on a novel surface analysis model was proposed for the first time. In this method, spiral tool path generation methods were uniformly described on the basis of the newly established surface analysis model. The relation between cutting point sampling details and the resulting form error of freeform surfaces was analyzed and simulated. Then, corresponding fitness function and constraint function models were established, and the non-dominated sorting genetic algorithm (NSGA-II) was used to solve the path optimization problem to obtain the pareto optimal solution of the cutting point sampling parameters. Next, based on the optimization results of tool path, machining simulations with experimental parameters were performed to evaluate the quality of optimized tool path. To demonstrate the effectiveness of the proposed method, turning tests of near-rotational freeform surfaces were attempted on single-crystal germanium using the optimization method and the measurement results demonstrated that the optimized tool path can achieve sub-micron form accuracy, which further verified the effectiveness of the proposed optimization method. The proposed optimization method has been further applied in the machining of larger diameter freeform optics.

Numerical Modeling of Titanium Alloy Ti10V2Fe3Al Milling Process

The simulation of material machining using finite element models is a powerful tool for the optimization of simulated processes and tools, as well as for the determination of cutting process characteristics that are difficult or practically impossible to determine by experiment. The paper presents results of the numerical simulation of the titanium alloy Ti10V2Fe3Al (Ti-1023). The behavior of the machined material was modeled with the Johnson–Cook constitutive equation, and its damage mechanism was modeled using the Cockcroft and Latham model. The parameters of the constitutive equation for machined material behavior and damage were determined using a DOE sensitivity analysis during orthogonal cutting. The values of the cutting force components, as well as the minimum and maximum chip thicknesses, were used as target functions for the DOE analysis. The generalized values of the constitutive equation parameters and the fracture stress values determined by the DOE analysis were calculated as the set intersection of individual multitude values of these parameters. The simulation results of the studied cutting processes showed an acceptable agreement with the experimental data when the cutting speed and tool feed changed significantly. The deviation in the simulated values of the cutting forces from their measured values ranged from about 10% to about 20%.

Multiphysics Coupled Simulation of Electrolytic Machining of Toothed Grooves of Contrate Gear

In the field of aviation, the contrate gear is an important component, the material of the contrate gear is the basis for ensuring the reliability and high performance of the aircraft engine, the commonly used material for contrate gear is 20Cr2Ni4A. In order to fully understand the electrolytic machining of the toothed groove of the contrate gear, a multiphysics coupling model of the electric field, the gas-liquid two-phase flow field and the temperature field was established to study the distribution of bubble rate, temperature, conductivity and current density in the processing gap. Simulation results show that the temperature and bubble rate gradually increase along the process direction in the processing gap, the conductivity and current density of the electrolyte gradually decrease.

Probing the epoch of reionization using synergies of line intensity mapping

The epoch of reionization (EoR) remains a poorly understood cosmic era for the most part. Yet, efforts are still going on to probe and understand this epoch. We present a review of the latest developments in the techniques (especially line-intensity mapping) to study the EoR and try to highlight the contribution of the Indian community in this field. Line-emissions like [Hi]\(_{\mathrm{21~cm}}\), Lyman-\(\alpha \), [Cii]\(_{\text {158}~\mu \text {m}}\) and their role as tracers in probing the EoR are discussed. While the [Hi]\(_{\mathrm{21~cm}}\) is an excellent probe of the early inter-galactic medium (IGM), the others are mainly targeted to do an unresolved and large-scale survey of the reionizing sources. Techniques to model these signals include simulations and machine learning approaches along with the challenge to tackle foregrounds or interlopers. We also discuss synergy opportunities among various tracers that we mention. Synergy addresses different aspects of the problem, which is otherwise difficult or impossible to tackle. They include statistics like cross-power spectrum, cross-bispectrum and other techniques, such as follow-up studies. We present updates on the relevant experiments; these include upper limits on the [Hi]\(_{\mathrm{21~cm}}\) power spectrum along with some highlights on high-redshift galaxy surveys. Finally, we highlight on what can be improved further within the community: applying machine learning and simulations based on hydrodynamic and radiative-transfer techniques. Next-generation experiments also need to be conceived to address issues, which are currently beyond our reach.

Gaussian process regressions on hot deformation behaviors of FGH98 nickel-based powder superalloy

The hot deformation behaviors of FGH98 nickel-based powder superalloy were experimentally investigated and theoretically analyzed by Arrhenius models and machine learning (ML). Hot compression tests were conducted with a Gleeble-3800 thermo-mechanical simulation machine on the FGH98 superalloy at strain rates of 0.001–1 s–1 and temperatures of 1025–1175 °C. The peak stresses under different deformation conditions were analyzed via the Sellars model and an ML-inspired Gaussian process regression (GPR) model. The prediction of the GPR model outperformed that from the Sellars model. In addition, the stress-strain responses were predicted by the GPR model and tested by experimentally measured stress-strain curves. The results indicate that the developed GPR model has great power with wide generalization capability in the prediction of hot deformation behaviors of FGH98 superalloy, as evidenced by the R2 value higher than 0.99 on the test dataset.

Numerical simulation of herringbone gear abrasive flow machining

The Eulerian-Eulerian method is used to numerically simulate herringbone teeth using the precision machining technique of abrasive flow. The effects of inlet velocity and abrasive concentration factors on abrasive flow machining are investigated separately for numerical analysis to reveal the effects of dynamic pressure and wall shear on abrasive flow machining under different machining parameters. The simulation results show that increasing the inlet velocity can improve the processing efficiency and the processing effect of abrasive flow processing. Increasing the abrasive concentration increases the processing cost and predicts a weakening of the abrasive flow, allowing for the use of lower concentrations of abrasive flow for actual processing.

Faculty Development in Proficiency and Application of Point-Of-Care Ultrasound

Introduction: Point-of-care ultrasound (POCUS) continues to be integrated into medical school and residency curriculums with objectives to increase proficiency and utilization in practice. Implementation of a targeted curriculum with faculty can increase proficiency of ultrasound. Methods: We created a POCUS pilot program at a small academic center to assess faculty members’ knowledges and perception about ultrasound in surgical settings. We conducted an observational cross-sectional study to test the hypothesis and hypothesized members would have increased confidence in their proficiency. Participants attended a 2-hour session with expert faculty. Our session consisted of a pre-test assessment survey, one-hour of didactic lecture, hands-on workshop with an ultrasound simulation machine and live standardized patient, and a post-test assessment survey. Faculty were given the option to return for further self-directed learning with ultrasound simulation machine after the pilot program if desired. Results: There was a statistically significant difference in pre- and post-survey questions that addressed comfort using ultrasound in clinical setting and critical care setting, comfort in using ultrasound at bedside rounds, comfort teaching medical student and residents, comfort performing FAST (Focused Abdominal Sonogram for Trauma) exam and comfort in doing basic bedside echocardiography. Faculty indicated time, availability of ultrasound equipment to faculty and house staff, and cost of equipment as important barriers to utilization of ultrasound in the clinical setting. Discussion: Our pilot POCUS course showed improved confidence in ultrasound skills among faculty members. It is important to address barriers such as limited availability of equipment and adequate time preparation for future POCUS curriculums.

FPGA Prototyping of Web Service Using REST and SOAP Packages

This Communication reports on FPGA prototyping of an embedded web service that sends XML messages under two different packages, namely Simple Object Access Protocol (SOAP) and Representational State Transfer (REST). The request and response messages are communicated through a 100 Mbps local area network between a Spartan-3E FPGA board and washing machine simulator. The performances of REST-based and SOAP-based web services implemented on reconfigurable hardware are then compared. In general, the former performs better than the latter in terms of FPGA resource utilization (~12% less), message length (~57% shorter), and processing time (~4.5 μs faster). This work confirms the superiority of REST over SOAP for data transmission using reconfigurable computing, which paves the way for adoption of these low-cost systems for web services of consumer electronics such as home appliances.

First Approach on a Mixed Domain Decomposition Method for 2-D Magnetostatic Simulation of Electrical Machines

A mixed domain decomposition method (DDM) is presented in this article. This method falls within the needs of advanced numerical optimization of electrical machines. Thus, we aim to adapt the LATIN method to the magnetostatic equations, as it has been so far mainly used for mechanical studies. The method provides a powerful iterative scheme that relies on mixed writing of the equations on the domain interfaces, including both primal and dual fields. The method is presented in detail in this article, and its performances are evaluated on a first implementation for the simulation of Team Problem No. 24.

가상공작기계를 이용한 공정진단 및 가공 프로그램 최적화

A virtual machine tool, a computer simulation model of the machine motion and cutting process with a level of accuracy and consistency that can replace an accurate machine tool, is one of the critical digital transformation technologies in the manufacturing industry. During the machine development phase, cost and time can be reduced by evaluating machining efficiency and quality through virtual prototyping. In the machine application phase, virtual machine tools can be used to accurately assess the condition of equipment and processes by analyzing actual data combined with simulated data. This paper introduces a virtual machine tool system that can analyze the behavior of an accurate machine tool by integrating physical models of structure, numerical controller, and cutting process. The key features of the virtual machine tool, synchronous machining simulation, machining stability detection, machining error estimation, and part program optimization, were evaluated through various machining tests with a vertical 3-axis milling machine.