Research

16 pages, 15017 KiB

Open AccessArticle

Correction of Thermal Errors in Machine Tools by a Hybrid Model Approach

by Christian Friedrich, Alexander Geist, Muhammad Faisal Yaqoob, Arvid Hellmich and Steffen Ihlenfeldt

Appl. Sci. 2024, 14(2), 671; https://doi.org/10.3390/app14020671 - 12 Jan 2024

Viewed by 623

Abstract

Thermally induced position errors are one of the main error sources on the workpiece caused by the behavior of the machine tool. In today’s industrial environment, the correction of thermal errors is usually based on simple regression approaches, where the characteristic diagrams for [...] Read more.

Thermally induced position errors are one of the main error sources on the workpiece caused by the behavior of the machine tool. In today’s industrial environment, the correction of thermal errors is usually based on simple regression approaches, where the characteristic diagrams for correction are generated experimentally. The performance of these approaches is only valid for the corresponding load regimes, which often results in insufficient correction quality in practical applications. Consequently, there is only a limited benefit or even a deterioration in machine behavior if the correction characteristic is based on an inapplicable load case compared to the initial experiment. Simulation-generated characteristic diagrams using finite element models solve this disadvantage, but do not answer the question about the choice of the right characteristic matching the current load situation, and, in addition, calculate very slowly. Structural model-based correction using reduced models, on the other hand, calculates quickly, but requires a high modeling effort for accurate correction. The approach, presented in this contribution, combines simulation-generated characteristic diagrams and a structural model-based decision algorithm for a new hybrid model in order to select the appropriate characteristic diagram for the present load situation in the control system. This paper presents the simulative characteristic diagram generation by a finite element model validated by experiments in a climate chamber and a validated structural model including the concept for the decision algorithm. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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16 pages, 6099 KiB

Open AccessArticle

Examination of a Human Heart Fabricating Its 3D-Printed Cardiovascular Model and Employing Computational Technologies

by Paschalis Charalampous, Nikolaos Kladovasilakis, Maria Zoumaki, Ioannis Kostavelis, Konstantinos Votis, Konstantinos Petsios, Dimitrios Tzetzis and Dimitrios Tzovaras

Appl. Sci. 2023, 13(18), 10362; https://doi.org/10.3390/app131810362 - 16 Sep 2023

Cited by 1 | Viewed by 1796

Abstract

In this paper, an innovative approach concerning the investigation of the human heart is introduced, employing state-of-the-art technologies. In particular, sophisticated algorithms were developed to automatically reconstruct a 3D model of a human heart based on DICOM data and to segment the main [...] Read more.

In this paper, an innovative approach concerning the investigation of the human heart is introduced, employing state-of-the-art technologies. In particular, sophisticated algorithms were developed to automatically reconstruct a 3D model of a human heart based on DICOM data and to segment the main parts that constitute it. Regarding the reconstructed 3D model, a diagnosis of the examined patient can be derived, whereas in the present study, a clinical case involving the coarctation of the aorta was inspected. Moreover, numerical approaches that are able to simulate flows on complex shapes were considered. Thereupon, the outcomes of the computation analysis coupled with the segmented patient-specific 3D model were inserted in a virtual reality environment, where the clinicians can visualize the blood flow at the vessel walls and train on real-life medical scenarios, enhancing their procedural understanding prior to the actual operation. The physical model was 3D-printed via the MultiJet 3D printing process utilizing materials possessing an adequate mechanical response replicating the mechanical properties and the geometrical characteristics of the human heart. The presented tools aim at the creation of an innovative digital environment, where gaining surgical experience and developing pre-operative strategies could be achieved without the risk and anxiety of actual surgery. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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15 pages, 14371 KiB

Open AccessArticle

Analysis of Kinematic Constraints in the Linkage Model of a Mecanum-Wheeled Robot and a Trailer with Conventional Wheels

by Igor Zeidis, Klaus Zimmermann, Steffen Greiser and Julia Marx

Appl. Sci. 2023, 13(13), 7449; https://doi.org/10.3390/app13137449 - 23 Jun 2023

Cited by 1 | Viewed by 950

Abstract

Mechanical systems that consist of a four-wheeled or two-wheeled robot with Mecanum wheels and a two-wheeled trailer with conventional wheels are considered. The kinematic characteristics of the mechanical systems under consideration of holonomic and non-holonomic constraints are presented and compared. From this, it [...] Read more.

Mechanical systems that consist of a four-wheeled or two-wheeled robot with Mecanum wheels and a two-wheeled trailer with conventional wheels are considered. The kinematic characteristics of the mechanical systems under consideration of holonomic and non-holonomic constraints are presented and compared. From this, it is shown that the structure of the kinematic constraint equations for mobile systems with a trailer does not apply to Chaplygin’s dynamic equations. If the mechanical system is not Chaplygin’s system, then the dynamic equations cannot be integrated separately from the equations of kinematic constraints. This is the difference between the kinematic constraint equations for the robot-trailer system and the constraint equations for a single robot with Mecanum wheels. Examples of numerical calculations using the equations of kinematic constraints are given. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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15 pages, 17786 KiB

Open AccessArticle

A Dynamic Multiple-Query RRT Planning Algorithm for Manipulator Obstacle Avoidance

by Chengren Yuan, Changgeng Shuai and Wenqun Zhang

Appl. Sci. 2023, 13(6), 3394; https://doi.org/10.3390/app13063394 - 07 Mar 2023

Cited by 2 | Viewed by 1417

Abstract

Manipulator motion planning for real-time obstacle avoidance in a dynamic environment is explored in this article. To address obstacle avoidance problems, a multiple-query and sampling-based motion replanning algorithm with the dynamic bias-goal factor, rapidly exploring random tree (DBG-RRT), is proposed to achieve a [...] Read more.

Manipulator motion planning for real-time obstacle avoidance in a dynamic environment is explored in this article. To address obstacle avoidance problems, a multiple-query and sampling-based motion replanning algorithm with the dynamic bias-goal factor, rapidly exploring random tree (DBG-RRT), is proposed to achieve a rapid response and a high success rate. Differently from other studies on path planning, a relay-node method is adopted on the basis of motion planning to generate a new collision-free trajectory. Subsequently, an un-interrupt strategy is embraced to judge whether the generated trajectory would be interfered with by dynamic obstacles. In the end, the DBG-RRT algorithm is applied, and the results demonstrate its effectiveness for manipulator motion planning in a dynamic environment. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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15 pages, 13980 KiB

Open AccessCommunication

Novel Real-Time Compensation Method for Machine Tool’s Ball Screw Thermal Error

by Ren Rong, Huicheng Zhou, Yubin Huang, Jianzhong Yang and Hua Xiang

Appl. Sci. 2023, 13(5), 2833; https://doi.org/10.3390/app13052833 - 22 Feb 2023

Viewed by 1035

Abstract

The real-time compensation of thermal error in ball screws is an effective means to improve the accuracy of machining tools. However, the trade-off between robustness and computational efficiency of existing ball screw thermal error models is complicated and not conducive to practical, high-precision, [...] Read more.

The real-time compensation of thermal error in ball screws is an effective means to improve the accuracy of machining tools. However, the trade-off between robustness and computational efficiency of existing ball screw thermal error models is complicated and not conducive to practical, high-precision, real-time error compensation. Focusing on this problem, we propose an iterative prediction model of screw thermal error based on a finite difference equation. By assuming an approximately linear relationship between heat generation and the ball screw’s convection power and feed speed, a simplified and more efficient identification of physical parameters needed for the iterative model is achieved. The proposed method is integrated with a three-axis drilling and tapping machine powered by an HNC–848D controller. A test piece machine using the proposed real-time thermal error compensation method exhibited a maximum machining error of 13 µm, compared to the 71 µm of an uncompensated specimen. The proposed method is demonstrated to improve machining accuracy, especially in the X- and Y- axes, and overcome the limitations of traditional thermal error prediction models. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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21 pages, 11754 KiB

Open AccessArticle

Development of a Collaborative Robotic Platform for Autonomous Auscultation

by Daniel Lopes, Luís Coelho and Manuel F. Silva

Appl. Sci. 2023, 13(3), 1604; https://doi.org/10.3390/app13031604 - 27 Jan 2023

Cited by 4 | Viewed by 1794

Abstract

Listening to internal body sounds, or auscultation, is one of the most popular diagnostic techniques in medicine. In addition to being simple, non-invasive, and low-cost, the information it offers, in real time, is essential for clinical decision-making. This process, usually done by a [...] Read more.

Listening to internal body sounds, or auscultation, is one of the most popular diagnostic techniques in medicine. In addition to being simple, non-invasive, and low-cost, the information it offers, in real time, is essential for clinical decision-making. This process, usually done by a doctor in the presence of the patient, currently presents three challenges: procedure duration, participants’ safety, and the patient’s privacy. In this article we tackle these by proposing a new autonomous robotic auscultation system. With the patient prepared for the examination, a 3D computer vision sub-system is able to identify the auscultation points and translate them into spatial coordinates. The robotic arm is then responsible for taking the stethoscope surface into contact with the patient’s skin surface at the various auscultation points. The proposed solution was evaluated to perform a simulated pulmonary auscultation in six patients (with distinct height, weight, and skin color). The obtained results showed that the vision subsystem was able to correctly identify

100 %

of the auscultation points, with uncontrolled lighting conditions, and the positioning subsystem was able to accurately position the gripper on the corresponding positions on the human body. Patients reported no discomfort during auscultation using the described automated procedure. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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13 pages, 11239 KiB

Open AccessFeature PaperArticle

A Non-Uniform Offset Algorithm for Milling Toolpath Generation Based on Boolean Operations

by Giuseppe Venturini, Niccolò Grossi, Lorenzo Morelli and Antonio Scippa

Appl. Sci. 2023, 13(1), 208; https://doi.org/10.3390/app13010208 - 24 Dec 2022

Cited by 2 | Viewed by 1752

Abstract

In milling, the advancement of CAM strategies has increased the need for tailored algorithms for semi-finished phase computation. In some cases (e.g., thin-wall milling), variable radial engagement of the tool during the toolpath is desired, leading to the need of non-uniform machining allowance [...] Read more.

In milling, the advancement of CAM strategies has increased the need for tailored algorithms for semi-finished phase computation. In some cases (e.g., thin-wall milling), variable radial engagement of the tool during the toolpath is desired, leading to the need of non-uniform machining allowance on the component that could be achieved only with a non-uniform offset algorithm, i.e., offset where the distance to the initial contour varies along that input. This work presents a general algorithm for non-uniform offset of polyline curves. The approach is based on 2D polygons and Boolean union operation, following these steps: (i) projection segments are generated, (ii) polygons (trapezoids and circular sectors) are created, (iii) Boolean union of all the polygons is performed, (iv) boundary of interest is extracted. The proposed algorithm is able to handle both internal and external offset and is robust for complexity of both the polyline and variable offset magnitude along that line, as proven by several examples and two applications to thin-wall milling provided. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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17 pages, 8100 KiB

Open AccessArticle

Bearing Fault Diagnosis Based on Small Sample Learning of Maml–Triplet

by Qiang Cheng, Zhaoheng He, Tao Zhang, Ying Li, Zhifeng Liu and Ziling Zhang

Appl. Sci. 2022, 12(21), 10723; https://doi.org/10.3390/app122110723 - 23 Oct 2022

Cited by 2 | Viewed by 1309

Abstract

Since the emergence of artificial intelligence and deep learning methods, the fault diagnosis of bearings in rotating machinery has gradually been realized, reducing the high costs of bearing faults. However, in the actual work of the equipment, faults rarely occur, resulting in less [...] Read more.

Since the emergence of artificial intelligence and deep learning methods, the fault diagnosis of bearings in rotating machinery has gradually been realized, reducing the high costs of bearing faults. However, in the actual work of the equipment, faults rarely occur, resulting in less fault data. Therefore, it is necessary to study small sample fault data. For the case of less fault data, the Maml–Triplet fault classification learning framework based on the combination of maml and the triplet neural network is proposed. In the framework of Maml-Triplet fault classification, firstly, an initial signal feature extractor is obtained using the Maml training method. Secondly, the feature vectors corresponding to signal data are obtained using depth distance measurement learning in the triplet neural network, and the fault type is judged based on the feature vectors of unknown signal. The results show that the accuracy of the Maml–Triplet model is 2% higher than that of the triplet model alone and 5% higher than that of the Maml–CNN meta learning method. When there are fewer data samples, the accuracy gap is more obvious. Therefore, in the case of less data, the Maml–Triplet model has an excellent fault identification ability. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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16 pages, 5202 KiB

Open AccessArticle

Dynamic Scheduling Optimization of Production Workshops Based on Digital Twin

by Guozhi Ding, Shiyao Guo and Xiaohui Wu

Appl. Sci. 2022, 12(20), 10451; https://doi.org/10.3390/app122010451 - 17 Oct 2022

Cited by 3 | Viewed by 2177

Abstract

Production scheduling is the key to manufacturing process decision support, which directly affects the efficiency and competitiveness of enterprises. The production process of discrete workshops is complex and changeable, and it is usually difficult to make adjustments quickly and accurately in response to [...] Read more.

Production scheduling is the key to manufacturing process decision support, which directly affects the efficiency and competitiveness of enterprises. The production process of discrete workshops is complex and changeable, and it is usually difficult to make adjustments quickly and accurately in response to disturbance events. In this paper, a workshop production scheduling method based on digital twin is proposed and applied to the manufacturing workshop of an aerospace factory. Combined with the advantages of real-time virtual real interaction fusion of digital twin technology, the dynamic scheduling problem under fault disturbance factors is studied. A high-fidelity digital twin workshop is established to realize the mapping and interaction between the real production and the virtual factory. Based on the vibration data of machine tool spindle, a fault prediction method of learning vector quantization neural network is proposed. The dynamic scheduling strategy of workshop production based on digital twin is constructed and compared with the scheduling results without digital twin under fault disturbance. The results show that the scheduling method based on digital twin can effectively deal with disturbances and improve workshop productivity. This study can be used for the application of digital twin and production scheduling in practical factories. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems)

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