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Machining Dynamics and Parameters Process Optimization

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (10 October 2020) | Viewed by 37145

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Dr. Gorka Urbicain

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Guest Editor

Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Plaza Europa 1, 20018 San Sebastián, Spain
Interests: modeling of metal removal processes; machining dynamics; chatter vibrations
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Dr. Daniel Olvera Trejo

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Guest Editor

Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico
Interests: machining dynamics; modal analysis; nonlinear vibrations
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Special Issue Information

Dear Colleagues,

At the start of the 21st century, despite new technologies having emerged, machining still remains the key operation to achieve high productivity and precision for high-added value parts in several sectors, but recent advances in computer applications should close the gap between simulations and industrial practices. This Special Issue on the topic of “Machining, Cutting Dynamics, and Parameters Process Optimization” is oriented toward (but not limited to) the different strategies and paths when it comes increasing productivity and reliability in metal removal processes.

Dynamic characterization and modeling of machine tools;
Experimental Techniques for chatter avoidance: online chatter detection and monitoring, passive and active vibration suppression;
Metal cutting mechanics: cutting force models, surface topography models, thermal models, and so on;
Artificially intelligent models and optimization techniques to improve process reliability;
Characterization and machinability of new emerging materials, difficult to cut alloys, etc.;
Cutting tools: design, behavior and study of grades, substrates, coatings, and wear;
Sensor-assisted machining and in-process data analysis.

Dr. Gorka Urbikain Pelayo
Dr. Daniel Olvera Trejo
Guest Editors

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Keywords

metal cutting
numerical models
high-performance computing
machine tool dynamics
cutting tools

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Published Papers (11 papers)

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Editorial

Jump to: Research

3 pages, 170 KiB

Open AccessEditorial

Special Issue on “Machining Dynamics and Parameters Process Optimization”

by Gorka Urbikain and Daniel Olvera-Trejo

Appl. Sci. 2020, 10(24), 8908; https://doi.org/10.3390/app10248908 - 14 Dec 2020

Cited by 1 | Viewed by 1528

Abstract

In 1907, F.W Taylor—the father of production engineering—exposed the fundamentals of modern machining and defined chatter as the most obscure and delicate of all problems facing the machinist [...] Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

Research

Jump to: Editorial

13 pages, 5103 KiB

Open AccessArticle

Implicit Subspace Iteration to Improve the Stability Analysis in Grinding Processes

by Jorge Alvarez, Mikel Zatarain, David Barrenetxea, Jose Ignacio Marquinez and Borja Izquierdo

Appl. Sci. 2020, 10(22), 8203; https://doi.org/10.3390/app10228203 - 19 Nov 2020

Cited by 6 | Viewed by 1891

Abstract

An alternative method is devised for calculating dynamic stability maps in cylindrical and centerless infeed grinding processes. The method is based on the application of the Floquet theorem by repeated time integrations. Without the need of building the transition matrix, this is the most efficient calculation in terms of computation effort compared to previously presented time-domain stability analysis methods (semi-discretization or time-domain simulations). In the analyzed cases, subspace iteration has been up to 130 times faster. One of the advantages of these time-domain methods to the detriment of frequency domain ones is that they can analyze the stability of regenerative chatter with the application of variable workpiece speed, a well-known technique to avoid chatter vibrations in grinding processes so the optimal combination of amplitude and frequency can be selected. Subspace iteration methods also deal with this analysis, providing an efficient solution between 27 and 47 times faster than the abovementioned methods. Validation of this method has been carried out by comparing its accuracy with previous published methods such as semi-discretization, frequency and time-domain simulations, obtaining good correlation in the results of the dynamic stability maps and the instability reduction ratio maps due to the application of variable speed. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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14 pages, 5012 KiB

Open AccessArticle

Natural Frequency Prediction Method for 6R Machining Industrial Robot

by Jiabin Sun, Weimin Zhang and Xinfeng Dong

Appl. Sci. 2020, 10(22), 8138; https://doi.org/10.3390/app10228138 - 17 Nov 2020

Cited by 13 | Viewed by 2718

Abstract

The industrial robot machining performance is highly dependent on dynamic behavior of the robot, especially the natural frequency. This paper aims at introducing a method to predict the natural frequency of a 6R industrial robot at random configuration, for improving dynamic performance during robot machining. A prediction model of natural frequency which expresses the mathematical relation between natural frequency and configuration is constructed for a 6R robot. Joint angles are used as input variables to represent the configurations in the model. The quantity and range of variables are limited for efficiency and practicability. Then sample configurations are selected by central composite design method due to its capacity of disposing nonlinear effects, and natural frequency data is acquired through experimental modal test. The model, which is in form of regression equation, is fitted and optimized with sample data through partial least square (PLS) method. The proposed model is verified with random configurations and compared with the original model and a model fitted by least square method. Prediction results indicate that the model fitted and optimized by PLS method has the best prediction ability. The universality of the proposed method is validated through implementation onto a similar 6R robot. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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22 pages, 5832 KiB

Open AccessArticle

Uncharted Stable Peninsula for Multivariable Milling Tools by High-Order Homotopy Perturbation Method

by Jose de la Luz Sosa, Daniel Olvera-Trejo, Gorka Urbikain, Oscar Martinez-Romero, Alex Elías-Zúñiga and Luis Norberto López de Lacalle

Appl. Sci. 2020, 10(21), 7869; https://doi.org/10.3390/app10217869 - 6 Nov 2020

Cited by 10 | Viewed by 2797

Abstract

In this work, a new method for solving a delay differential equation (DDE) with multiple delays is presented by using second- and third-order polynomials to approximate the delayed terms using the enhanced homotopy perturbation method (EMHPM). To study the proposed method performance in terms of convergency and computational cost in comparison with the first-order EMHPM, semi-discretization and full-discretization methods, a delay differential equation that model the cutting milling operation process was used. To further assess the accuracy of the proposed method, a milling process with a multivariable cutter is examined in order to find the stability boundaries. Then, theoretical predictions are computed from the corresponding DDE finding uncharted stable zones at high axial depths of cut. Time-domain simulations based on continuous wavelet transform (CWT) scalograms, power spectral density (PSD) charts and Poincaré maps (PM) were employed to validate the stability lobes found by using the third-order EMHPM for the multivariable tool. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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13 pages, 7969 KiB

Open AccessArticle

Process Parameters Optimization of Thin-Wall Machining for Wire Arc Additive Manufactured Parts

by Niccolò Grossi, Antonio Scippa, Giuseppe Venturini and Gianni Campatelli

Appl. Sci. 2020, 10(21), 7575; https://doi.org/10.3390/app10217575 - 27 Oct 2020

Cited by 16 | Viewed by 3305

Abstract

Additive manufacturing (AM) is an arising production process due to the possibility to produce monolithic components with complex shapes with one single process and without the need for special tooling. AM-produced parts still often require a machining phase, since their surface finish is not compliant with the strict requirements of the most advanced markets, such as aerospace, energy, and defense. Since reduced weight is a key requirement for these parts, they feature thin walls and webs, usually characterized by low stiffness, requiring the usage of low productivity machining parameters. The idea of this paper is to set up an approach which is able to predict the dynamics of a thin-walled part produced using AM. The knowledge of the workpiece dynamics evolution throughout the machining process can be used to carry out cutting parameter optimization with different objectives (e.g., chatter avoidance, force vibrations reduction). The developed approach exploits finite element (FE) analysis to predict the workpiece dynamics during the machining process, updating its changing geometry. The developed solution can automatically optimize the toolpath for the machining operation, generated by any Computer Aided Manufacturing (CAM) software updating spindle speed in accordance with the selected optimization strategies. The developed approach was tested using as a test case an airfoil. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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17 pages, 12217 KiB

Open AccessArticle

A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning

by Harshavardhan Mamledesai, Mario A. Soriano and Rafiq Ahmad

Appl. Sci. 2020, 10(20), 7298; https://doi.org/10.3390/app10207298 - 19 Oct 2020

Cited by 24 | Viewed by 3121

Abstract

Tool condition monitoring is one of the classical problems of manufacturing that is yet to see a solution that can be implementable in machine shops around the world. In tool condition monitoring, we are mostly trying to define a tool change policy. This tool change policy would identify a tool that produces a non-conforming part. When the non-conforming part producing tool is identified, it could be changed, and a proactive approach to machining quality that saves resources invested in non-conforming parts would be possible. The existing studies highlight three barriers that need to be addressed before a tool condition monitoring solution can be implemented to carry out tool change decision-making autonomously and independently in machine shops around the world. First, these systems are not flexible enough to include different quality requirements of the machine shops. The existing studies only consider one quality aspect (for example, surface finish), which is difficult to generalize across the different quality requirements like concentricity or burrs on edges commonly seen in machine shops. Second, the studies try to quantify the tool condition, while the question that matters is whether the tool produces a conforming or a non-conforming part. Third, the qualitative answer to whether the tool produces a conforming or a non-conforming part requires a large amount of data to train the predictive models. The proposed model addresses these three barriers using the concepts of computer vision, a convolution neural network (CNN), and transfer learning (TL) to teach the machines how a conforming component-producing tool looks and how a non-conforming component-producing tool looks. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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20 pages, 5039 KiB

Open AccessArticle

Tool Wear Monitoring for Complex Part Milling Based on Deep Learning

by Xiaodong Zhang, Ce Han, Ming Luo and Dinghua Zhang

Appl. Sci. 2020, 10(19), 6916; https://doi.org/10.3390/app10196916 - 2 Oct 2020

Cited by 42 | Viewed by 4769

Abstract

Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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18 pages, 4440 KiB

Open AccessArticle

Semi-Active Magnetorheological Damper Device for Chatter Mitigation during Milling of Thin-Floor Components

by Santiago Daniel Puma-Araujo, Daniel Olvera-Trejo, Oscar Martínez-Romero, Gorka Urbikain, Alex Elías-Zúñiga and Luis Norberto López de Lacalle

Appl. Sci. 2020, 10(15), 5313; https://doi.org/10.3390/app10155313 - 31 Jul 2020

Cited by 28 | Viewed by 3831

Abstract

The productivity during the machining of thin-floor components is limited due to unstable vibrations, which lead to poor surface quality and part rejection at the last stage of the manufacturing process. In this article, a semi-active magnetorheological damper device is designed in order to suppress chatter conditions during the milling operations of thin-floor components. To validate the performance of the magnetorheological (MR) damper device, a 1 degree of freedom experimental setup was designed to mimic the machining of thin-floor components and then, the stability boundaries were computed using the Enhance Multistage Homotopy Perturbation Method (EMHPM) together with a novel cutting force model in which the bull-nose end mill is discretized in disks. It was found that the predicted EMHPM stability lobes of the cantilever beam closely follow experimental data. The end of the paper shows that the usage of the MR damper device modifies the stability boundaries with a productivity increase by a factor of at least 3. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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16 pages, 4359 KiB

Open AccessArticle

An Accurate and Efficient Approach to Calculating the Wheel Location and Orientation for CNC Flute-Grinding

by Yang Fang, Liming Wang, Jianping Yang and Jianfeng Li

Appl. Sci. 2020, 10(12), 4223; https://doi.org/10.3390/app10124223 - 19 Jun 2020

Cited by 10 | Viewed by 3848

Abstract

The profile of flutes has a great influence on the stiffness and chip-removal capacity of end-mills. Generally, the accuracy of flute parameters is determined by the computer numerical control (CNC) grinding machine through setting the wheel’s location and orientation. In this work, a novel algorithm was proposed to optimize the wheel’s location and orientation for the flute-grinding to achieve higher accuracy and efficiency. Based on the geometrical constraint that the grinding wheel should always intersect with the bar-stock while grinding the flutes, the grinding wheel and bar-stock were simplified as an ellipse and circle via projecting in the cross-section. In light of this, we re-formulated the wheel’s determination model and analyzed the geometrical constraints for interference, over-cut and undercut in a unified framework. Then, the projection model and geometrical constraints were integrated with the evolution algorithm (i.e., particle swarm optimization (PSO), genetic algorithm (GA) for the population initialization and local search operator so as to optimize the wheel’s location and orientation. Numerical examples were given to confirm the validity and efficiency of the proposed approach. Compared with the existing approaches, the present approach improves the flute-grinding accuracy and robustness with a wide range of applications for various flute sizes. The proposed algorithm could be used to facilitate the general flute-grinding operations. In the future, this method could be extended to more complex grinding operations with the requirement of high accuracy, such as various-section cutting-edge resharpening. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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20 pages, 2804 KiB

Open AccessArticle

Double B-Spline Curve-Fitting and Synchronization-Integrated Feedrate Scheduling Method for Five-Axis Linear-Segment Toolpath

by Xiangyu Gao, Shuyou Zhang, Lemiao Qiu, Xiaojian Liu, Zili Wang and Yang Wang

Appl. Sci. 2020, 10(9), 3158; https://doi.org/10.3390/app10093158 - 1 May 2020

Cited by 18 | Viewed by 4027

Abstract

The discontinuities of a five-axis linear-segment toolpath result in fluctuation in the feedrate, acceleration and jerk commands that lead to machine tool vibration and poor surface finish. For path smoothing, with the global curve-fitting method it is difficult to control fitting error and the local corner-smoothing method has large curvature extreme. For path synchronization, the parameter synchronization method cannot ensure smooth rotary motion. Aiming at these problems, this paper proposes a double B-spline curve-fitting and synchronization-integrated feedrate scheduling method. Two C2-continuous and error-bounded B-spline curves are produced to fit tool-tip position and tool orientation, respectively. The fitting error is controlled by locally refining the curve segments that exceed the fitting tolerance. The tool-tip position trajectory is firstly planned to address axial kinematic constraints in the feedrate scheduling process. Then the feedrate is deformed for the tool orientation to guarantee smooth rotary motion as well as to share the same motion time with the tool-tip position segment by segment. The feasibility and effectiveness of the proposed method have been validated by simulations and experiments on the S-shape test piece. Simulations show that the proposed curve-fitting method can generate smooth toolpath and constrain fitting error. The proposed feedrate scheduling method can guarantee smooth rotary motion and keep axial motions under kinematic limits, compared with the method that does not consider axial kinematic constraints and the parameter synchronization method. Experimental results verify that the proposed curve-fitting method can generate smooth tool path under fitting tolerance, and the proposed feedrate scheduling method can produce smooth and restricted axial motions. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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21 pages, 8300 KiB

Open AccessArticle

Development of an Analyzing and Tuning Methodology for the CNC Parameters Based on Machining Performance

by Ben-Fong Yu and Jenq-Shyong Chen

Appl. Sci. 2020, 10(8), 2702; https://doi.org/10.3390/app10082702 - 14 Apr 2020

Cited by 9 | Viewed by 4066

Abstract

This paper proposes the development of a tuning methodology which can set the proper values of the Computer Numerical Control (CNC) parameters to achieve the required machining performance. For the conventional operators of machine tools, the CNC parameters were hard to be adjusted to optimal settings, which was a complicated and time-consuming task. To save time in finding optimal CNC parameters, the objective of this research was to develop a practical methodology to tune the CNC parameters effectively for easy implementation in the commercial CNC controller. Firstly, the effect of the CNC parameters in the CNC controller on the tool-path planning was analyzed via experiments. The machining performance was defined in the high-speed (HS) mode, the high-accuracy (HP) mode, and the high-surface-quality (HQ) mode, according to the dynamic errors of several specified paths. Due to the CNC parameters that have a particularly critical effect on the dynamic errors, the relationship between the CNC parameters and the dynamic errors was validated by the measured data. Finally, the tuning procedure defined the anticipated dynamic errors for the three machining modes with the actual machine. The CNC parameters will correspond with anticipated dynamics errors based on several specified paths. The experimental results showed that the HS mode was the fastest to complete the path, and the completion time of the HP and HQ modes were increased by 37% and 6%, respectively. The HP mode had the smallest dynamic errors than other modes, and the dynamic errors of the HS and HQ modes are increased by 66% and 16%. In the HQ mode, the motion oscillation was reduce significantly, and the tracking error of the HS and HP modes were increased by 85% and 28%. The advantage of the methodology is that it simplifies set-up steps of the CNC parameters, making it suitable for practical machine applications. Full article

(This article belongs to the Special Issue Machining Dynamics and Parameters Process Optimization)

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