Journal Description
Machines
Machines
is an international, peer-reviewed, open access journal on machinery and engineering published monthly online by MDPI. The IFToMM is affiliated with Machines and its members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Mechanical)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.6 (2022);
5-Year Impact Factor:
2.8 (2022)
Latest Articles
Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments
Machines 2024, 12(3), 200; https://doi.org/10.3390/machines12030200 - 18 Mar 2024
Abstract
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the
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This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.
Full article
(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)
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Axiomatic Design of a Test Artifact for PBF-LM Machine Capability Monitoring
by
Alessandro Giorgetti, Filippo Ceccanti, Niccolò Baldi, Simon Kemble, Gabriele Arcidiacono and Paolo Citti
Machines 2024, 12(3), 199; https://doi.org/10.3390/machines12030199 - 18 Mar 2024
Abstract
Powder Bed Fusion Laser Melting (PBF-LM) additive manufacturing technology is expected to have a remarkable impact on the industrial setting, making possible the realization of a metallic component with very complex designs to enhance product performance. However, the industrial use of the PBF-LM
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Powder Bed Fusion Laser Melting (PBF-LM) additive manufacturing technology is expected to have a remarkable impact on the industrial setting, making possible the realization of a metallic component with very complex designs to enhance product performance. However, the industrial use of the PBF-LM system needs a capability monitoring system to ensure product quality. Among the various studies developed, the investigation of methodology for the actual machine capability determination has been faced and still represents an open point. There are multiple authors and institutes proposing different investigation methods, ranging from the realization of samples (ex situ analysis) to installing monitoring devices on the machine (in situ analysis). Compared to other approaches, sample realization allows for assessing how the machine works through specimen analysis, but it is sensitive to the sample design. In this article, we first present an analysis of a well-known test artifact from an Axiomatic Design perspective. Second, based on the customer needs analysis and adjustments with respect to the use of hypothetical additive production lines, a new test artifact with an uncoupled design matrix is introduced. The proposed design has been experimentally tested and characterized using artifact made of Inconel 718 superalloy to evaluate its performance and representativeness in machine capability assessment. The results show an accurate identification of beam offset and scaling factor considering all the building platform positions. In addition, the artifact is characterized by a reduced building time (more than 90% with respect to the reference NIST artifact) and a halved inspection time (from 16 h to 8 h).
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(This article belongs to the Special Issue Design Methods for Mechanical and Industrial Innovation)
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Open AccessArticle
Research on Running Performance Optimization of Four-Wheel-Driving Ackerman Chassis by the Combining Method of Quantitative Experiment with Dynamic Simulation
by
Xiangyu Zhang, Bowen Xie, Yang Yang, Yongbin Liu and Pan Jiang
Machines 2024, 12(3), 198; https://doi.org/10.3390/machines12030198 - 17 Mar 2024
Abstract
The wheeled chassis, which is the carrying device of the existing handling robot, is mostly only suitable for flat indoor environments and does not have the ability to work on outdoor rugged terrain, greatly limiting the development of chassis driven handling robots. On
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The wheeled chassis, which is the carrying device of the existing handling robot, is mostly only suitable for flat indoor environments and does not have the ability to work on outdoor rugged terrain, greatly limiting the development of chassis driven handling robots. On this basis, this paper innovatively designs a four-wheel-driving Ackerman chassis with strong vibration absorption and obstacle surmounting capabilities and conducts performance research and optimization on it through quantitative experiments and dynamic simulation. Firstly, based on the introduction of the working principle and structure of the four-wheel-driving Ackerman carrier chassis, a multi-sensor distributed dynamic performance test system is constructed through the analysis of the chassis performance evaluation index. Then, according to the quantitative operation experiment of the chassis, the vibration and acceleration characteristics of the chassis at different positions of the chassis, the amount of slip and straightness of the chassis under different running distance, and the operating characteristics of the chassis under different road conditions and different damping springs conditions were analyzed respectively, which verified the rationality of the chassis design. Finally, by constructing the chassis dynamics simulation model; the influence law of chassis structure; and performance parameters such as chassis wheelbase, guide rod structure, and parameters, wheel friction coefficient and assembly error on the dynamic characteristics of the chassis is studied, and the optimal structure of the four-wheel-driving Ackerman chassis is determined while it is verified based on the simulation results. The research shows that the four-wheel-driving Ackerman chassis has good vibration performance and stability and has strong adaptability to different roads. After optimization, the vibration performance, stability, amount of slip, and straightness of the chassis structure are significantly improved, and the straightness is reduced to 0.399%, which is suitable for precise carriage applications on the chassis. The research has important guiding significance for promoting the development and application of wheeled chassis.
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(This article belongs to the Section Vehicle Engineering)
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Forecasting Emerging Technologies in Intelligent Machine Tools: A Novel Framework Based on Community Analysis
by
Cunxiang He, Yufei Liu and Yuhan Liu
Machines 2024, 12(3), 197; https://doi.org/10.3390/machines12030197 - 17 Mar 2024
Abstract
Having emerged as strategic focal points in industrial transformation and technological innovation, intelligent machine tools are pivotal in the field of intelligent manufacturing. Accurately forecasting emerging technologies within this domain is crucial for guiding intelligent manufacturing’s evolution and fostering rapid innovation. However, prevailing
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Having emerged as strategic focal points in industrial transformation and technological innovation, intelligent machine tools are pivotal in the field of intelligent manufacturing. Accurately forecasting emerging technologies within this domain is crucial for guiding intelligent manufacturing’s evolution and fostering rapid innovation. However, prevailing research methodologies exhibit limitations, often concentrating on popular topics at the expense of lesser-known yet significant areas, thereby impacting the accurate identification of research priorities. The complex, systemic, and interdisciplinary nature of intelligent machine tool technology challenges traditional research approaches, particularly in assessing technological maturity and intricate interactions. To overcome these challenges, we propose a novel framework that leverages technological communities for a comprehensive analysis. This approach clusters data into specific topics which are reflective of the technology system, facilitating detailed investigations within each area. By refining community analysis methods and integrating structural and interactive community features, our framework significantly improves the precision of emerging technology predictions. Our research not only validates the framework but also projects key emerging technologies in intelligent machine tools, offering valuable insights for business leaders and scholars alike.
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(This article belongs to the Section Advanced Manufacturing)
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Open AccessArticle
Introduction of Collaborative Robotics in the Production of Automotive Parts: A Case Study
by
Mirco Polonara, Alessandra Romagnoli, Gianfranco Biancini and Luca Carbonari
Machines 2024, 12(3), 196; https://doi.org/10.3390/machines12030196 - 16 Mar 2024
Abstract
Incorporating collaborative applications constitutes a challenging and increasingly intricate objective within the context of small and medium-sized enterprises (SMEs). This challenge stems from the shortage of highly specialized personnel in these types of companies when it comes to adopting cutting-edge technologies. The lack
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Incorporating collaborative applications constitutes a challenging and increasingly intricate objective within the context of small and medium-sized enterprises (SMEs). This challenge stems from the shortage of highly specialized personnel in these types of companies when it comes to adopting cutting-edge technologies. The lack of innovation in production processes, however, increases the risk that SMEs will not be able to adapt to rapid changes in the market and the growing needs of consumers, who today are evolving at an unprecedented pace. The importance of adopting collaborative applications can be found in their capacity to harmonize human adaptability with the precision of robotic technology. This synergy contributes to the establishment of a safer work environment while guaranteeing effective and efficient performance. These features not only lead to improved production line performance compared to traditional manual or stationary operations, but also highlight new perspectives in the design, production, and customization of new products. This, in turn, helps companies strengthen their competitiveness in the global market. In this scenario, the primary challenge centers around effectively putting these solutions into practice. Our research aims to highlight how significant benefits can be achieved, both in terms of performance improvements and economically, through the analysis of a simple yet illuminating case study.
Full article
(This article belongs to the Special Issue Intelligent Factory 4.0: Advanced Production and Automation Systems, Volume II)
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Making Cognitive Ergonomics in the Human–Computer Interaction of Manufacturing Execution Systems Assessable: Experimental and Validation Approaches to Closing Research Gaps
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Andreas Dörner, Marek Bures, Michal Simon and Gerald Pirkl
Machines 2024, 12(3), 195; https://doi.org/10.3390/machines12030195 - 16 Mar 2024
Abstract
Cognitive ergonomics and the mental health of production workers have attracted increasing interest in industrial companies. However, there is still not much research available as it is regarding physical ergonomics and muscular load. This paper designs an experiment to analyze the cognitive ergonomics
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Cognitive ergonomics and the mental health of production workers have attracted increasing interest in industrial companies. However, there is still not much research available as it is regarding physical ergonomics and muscular load. This paper designs an experiment to analyze the cognitive ergonomics and mental stress of shop floor production workers interacting with different user interfaces of a Manufacturing Execution System (MES) that is adjustable for analyzing the influence of other assistive systems, too. This approach is going to be designed with the Design of Experiments (DoE) method. Therefore, the respective goals and factors are going to be determined. The environment will be the laboratories of the University of Applied Sciences Amberg-Weiden and its Campus for Digitalization in Amberg. In detail, there will be a sample assembly process from the automotive supplier industry for demonstration purposes. At this laboratory, the MES software from the European benchmark SAP is installed, and the respective standard Production Operator Desk is going to be used with slight adaptions. In order to make the cognitive ergonomics measurable, different approaches are going to be used. For instance, body temperature, heart rate and skin conductance as well as subjective methods of self-assessment are planned. The result of this paper is a ready-to-run experiment with sample data for each classification of participants. Further, possible limitations and adjustments are going to be discussed. Finally, an approach to validating the expected results is going to be shown and future intentions are going to be discussed.
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(This article belongs to the Section Automation and Control Systems)
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Applying the MIMO BP Neural Network and Cloud-Based Monitoring of Thermal Behavior for High-Speed Motorized Spindle Units
by
Milos Knezev, Robert Cep, Luka Mejic, Branislav Popovic, Aco Antic, Branko Strbac and Aleksandar Zivkovic
Machines 2024, 12(3), 194; https://doi.org/10.3390/machines12030194 - 15 Mar 2024
Abstract
Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This
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Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools.
Full article
(This article belongs to the Special Issue Advanced Signal Processing Methods and Deep Neural Networks for Machine Fault Diagnosis)
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Vibration Characteristics of Asymmetric Flexible Cantilever Beams Connected to a Central Rigid Body
by
Dehuang Gong, Xueqian Wei, Hongli Liu and Fengming Li
Machines 2024, 12(3), 193; https://doi.org/10.3390/machines12030193 - 15 Mar 2024
Abstract
A satellite with two solar wings can be modeled using a pair of symmetric flexible cantilever beams connected to a central rigid body. Due to certain reasons, the symmetric flexible cantilever beams may be turned into asymmetric ones, which will inevitably influence the
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A satellite with two solar wings can be modeled using a pair of symmetric flexible cantilever beams connected to a central rigid body. Due to certain reasons, the symmetric flexible cantilever beams may be turned into asymmetric ones, which will inevitably influence the vibration properties of the structural system. By changing the structural sizes and adding local mass on one side of the two beams, a structural system with asymmetric mass distribution is obtained and its vibration characteristics are investigated. Hamilton’s principle with the assumed mode method is employed to establish the equation of motion of the asymmetric structural system. The natural frequencies, mode shapes, frequency response curves and displacement time histories of the system are calculated, and they are compared with those of the structural system with a symmetric mass distribution. The correctness and feasibility of the present analytical method are verified by means of the finite element method (FEM) and a vibration experiment. The analytical results show that the mass asymmetry of the two beams leads to the mode localization phenomenon, and the coupling effect between the two beams and the central rigid body is enhanced. The larger the mass asymmetry is and the closer the position of the added local mass to the end of the cantilever beam is, the more obvious of the mode localization phenomenon is and the more obvious of the coupling effect between the two beams and the central rigid body is. The present investigation results are helpful for the dynamic analysis and design of spacecraft structures composed of flexible solar wings and a central rigid body.
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(This article belongs to the Section Machine Design and Theory)
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An Internet-of-Things-Based Dynamic Scheduling Optimization Method for Unreliable Flexible Manufacturing Systems under Complex Operational Conditions
by
Abdulmajeed Dabwan, Husam Kaid, Abdulrahman Al-Ahmari, Khaled N. Alqahtani and Wadea Ameen
Machines 2024, 12(3), 192; https://doi.org/10.3390/machines12030192 - 15 Mar 2024
Abstract
The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment
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The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.
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(This article belongs to the Special Issue Recent Advances in Smart Design and Manufacturing Technology)
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Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile
by
Mingjie Feng, Jianbo Dai, Wenbo Zhou, Haozhi Xu and Zhongbin Wang
Machines 2024, 12(3), 191; https://doi.org/10.3390/machines12030191 - 15 Mar 2024
Abstract
Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each
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Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.
Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Modeling for Hysteresis Contact Behavior of Bolted Joint Interfaces with Memory Effect Penalty Constitution
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Di Yuan, Dong Wang and Qiang Wan
Machines 2024, 12(3), 190; https://doi.org/10.3390/machines12030190 - 14 Mar 2024
Abstract
A novel penalty contact constitution was developed to replicate the hysteresis memory effect observed in contact interfaces. On this basis, a refined finite element analysis (FEA) was performed to study the stick–slip friction contact behavior of bolted joint interfaces. The analysis was validated
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A novel penalty contact constitution was developed to replicate the hysteresis memory effect observed in contact interfaces. On this basis, a refined finite element analysis (FEA) was performed to study the stick–slip friction contact behavior of bolted joint interfaces. The analysis was validated by comparing it with the experimental hysteresis loops in the literature. The simulated hysteresis loops were subsequently used to identify four parameters of the Iwan model. Additionally, the effects of bolt clamping, friction coefficient, and excitation amplitude were individually examined. It was found that the deterioration in bolt clamping performance resulted in a decrease in both the equivalent joint stiffness and energy dissipation. Similarly, the reduction in the friction coefficient yielded a comparable impact. Furthermore, the identified model parameters of critical stick–slip force and displacement exhibited a quasi-linear relationship to the bolt preload and friction coefficient.
Full article
(This article belongs to the Special Issue Cutting-Edge Research in Tribology and Its Applications to Rolling Element Bearings)
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Design and Optimization of Production Line Layout Using Material Flows
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Michal Bučko, Lucie Krejčí, Ivo Hlavatý and Jiří Lorenčík
Machines 2024, 12(3), 189; https://doi.org/10.3390/machines12030189 - 14 Mar 2024
Abstract
Businesses are constantly trying to improve their production by looking for bottlenecks to improve their market position. The introduction and innovation of automated production lines is necessary for both labor shortages and productivity and quality reasons. A combination of precision, fluidity, and speed,
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Businesses are constantly trying to improve their production by looking for bottlenecks to improve their market position. The introduction and innovation of automated production lines is necessary for both labor shortages and productivity and quality reasons. A combination of precision, fluidity, and speed, that is the basic definition of a production line. With the advent of new technologies, production lines have also begun to continuously speed up and innovate. Innovation is the subject of this paper, where the problem of designing a completely new layout for a new production line in the food industry has been addressed. The aim of this paper was to create a design for the optimal layout of the production line in preselected production areas. Optimal use of the space allocated for production is very important for every company today.
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(This article belongs to the Special Issue Precision Manufacturing and Machine Tools)
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Assessment of the Correlation between the Monitored Operating Parameters and Bearing Life in the Milling Head of a CNC Computer Numerical Control Machining Center
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Petr Baron, Oleksandr Pivtorak, Ján Ivan and Marek Kočiško
Machines 2024, 12(3), 188; https://doi.org/10.3390/machines12030188 - 13 Mar 2024
Abstract
The present paper describes a study conducted at the request of the operator of machining center equipment. The operator observed undesirable indicators in terms of increased backlash and vibration of the milling head and poor quality of the machined surfaces. Vibration measurements and
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The present paper describes a study conducted at the request of the operator of machining center equipment. The operator observed undesirable indicators in terms of increased backlash and vibration of the milling head and poor quality of the machined surfaces. Vibration measurements and vibrodiagnostics were carried out before disassembling the milling head in the idle state. The bearings, lubricant, and friction regime were analyzed in the next step. The vibrodiagnostic methods used included VEL, ACC, EN2, EN3, and EN4, with recommended limits conforming to STN ISO 10816-3. The vibration values obtained indicated a problem with the bearings, exceeding the limit values. After disassembly of the bearings, abrasive wear, corrosion, and improper lubricant conditions were detected. Lubricant analysis showed the presence of abrasive and corrosive particles, indicating an unsatisfactory friction regime. Determining the optimum lubricant temperature and the effect on friction torque constituted other aspects of the study. Inspection of the bearing microgeometry confirmed unsatisfactory roundness. Furthermore, the assembly of tapered roller bearings with axial preload was analyzed with a focus on bearing stiffness, accuracy, and life. The results showed that preload improves shaft guidance accuracy and load distribution, promoting reliable operation and extending bearing life.
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(This article belongs to the Section Advanced Manufacturing)
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Optimal Design of Quadcopter Chassis Using Generative Design and Lightweight Materials to Advance Precision Agriculture
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Anurag Balayan, Rajnish Mallick, Stuti Dwivedi, Sahaj Saxena, Bisheshwar Haorongbam and Anshul Sharma
Machines 2024, 12(3), 187; https://doi.org/10.3390/machines12030187 - 13 Mar 2024
Abstract
This research addresses the imperative challenge of a lightweight design for an Unmanned Aerial Vehicle (UAV) chassis to enhance the thrust-to-weight and power-to-weight ratios, crucial for optimal flight performance, focused on developing an intriguing lightweight yet robust quadcopter chassis. Advanced generative design techniques,
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This research addresses the imperative challenge of a lightweight design for an Unmanned Aerial Vehicle (UAV) chassis to enhance the thrust-to-weight and power-to-weight ratios, crucial for optimal flight performance, focused on developing an intriguing lightweight yet robust quadcopter chassis. Advanced generative design techniques, integrated with topology optimization, using Autodesk Fusion 360 software (v. 16.5. 0.2083), 3D-printing methods and lightweight materials like Polylactic Acid (P.L.A.), Acrylonitrile Butadiene Styrene (A.B.S.), and Nylon 6/6 play a significant role in achieving the desired balance between structural integrity and weight reduction. The study showcases successful outcomes, presenting quadcopter chassis designs that significantly improve structural efficiency and overall performance metrics. The findings contribute to aerial robotics and hold promise for precision agriculture applications with relevant performed simulations, emphasizing the importance of tailored design methodologies for other engineering domains. In conclusion, this research provides a foundational step toward advancing drone technology, with weight reductions of almost 50%, P/W and T/W ratios increment of 6.08% and 6.75%, respectively, at least an 11.8% increment in Factor of Safety, at least a 70% reduction in stress values and reduced manufacturing time from its comparative DJI F450 drone, demonstrating the critical role of innovative design approaches in optimizing operational efficiency for targeted applications.
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(This article belongs to the Section Machine Design and Theory)
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An Adapted NURBS Interpolator with a Switched Optimized Method of Feed-Rate Scheduling
by
Xiaoyang Zhou
Machines 2024, 12(3), 186; https://doi.org/10.3390/machines12030186 - 13 Mar 2024
Abstract
With the increasing demand for processing precision in the manufacturing industry, feed-rate scheduling is a crucial component in achieving the processing quality of complex surfaces. A smooth feed-rate profile not only guarantees machining quality but also improves machining efficiency. Although the typical offline
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With the increasing demand for processing precision in the manufacturing industry, feed-rate scheduling is a crucial component in achieving the processing quality of complex surfaces. A smooth feed-rate profile not only guarantees machining quality but also improves machining efficiency. Although the typical offline feed-rate scheduling method possesses good processing efficiency, it may not provide an optimal solution due to the NP-hard problem caused by the feed-rate scheduling of continuous curve segments, which easily results in excess kinetic limitations and feed-rate fluctuations in a real-time interpolation. Instead, the FIR (Finite Impulse Response) method is widely used to realize interpolation in real-time processing. However, the FIR method will filter out a large number of high-frequency signals, leading to a low-processing efficiency. Further, greater acceleration or deceleration is required to ensure the interpolation passes through the segment end at a predefined feed rate and the deceleration in the feed rate profile appears earlier, which allows the interpolation to easily exceed the kinetic limitation. At present, a simple offline or online method cannot realize the global optimization of the feed-rate profile and guarantee the machining efficiency. Moreover, the current feed-rate scheduling that considers both offline and online methods does not consider the situation that the call of offline data and online prediction data will lead to a decrease in the real-time performance of the CNC system. Further, real-time feed-rate scheduling data tend to dominate the whole interpolation process, thus reducing the effect of the offline feed-rate scheduling data. Hence, based on the tool path with C3 continuity (Cubic Continuously Differentiable), this paper first presents a basic interpolation unit relevant to the S-type interpolation feed-rate profile. Then, an offline local smooth strategy is proposed to smooth the feed-rate profile and reduce the exceeding of kinetic limitations and feed-rate fluctuations caused by frequent acceleration and deceleration. Further, a global online smoothing strategy based on the data generated by offline pre-interpolation is presented. What is more, FIR login and logout conditions are proposed to further smooth the feed-rate profile and improve the real-time performance and machining efficiency. The case study validates that the proposed method performs better in kinetic results compared with the typical offline and FIR methods in both the simulation experiment and actual machining experiments. Especially, in actual processing experiments, the proposed method obtains a 28% reduction in contour errors. Further, the proposed method compared with the FIR method obtains a 15% increase in machining efficiency but only a 4% decrease compared with the typical offline method.
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(This article belongs to the Section Advanced Manufacturing)
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An Improved Fault Localization Method for Direct Current Filters in HVDC Systems: Development and Application of the DRNCNN Model
by
Xiaohui Liu, Haofeng Liu, Hui Qiao, Sihan Zhou and Liang Qin
Machines 2024, 12(3), 185; https://doi.org/10.3390/machines12030185 - 13 Mar 2024
Abstract
This paper focus on direct current (DC) filter grounding faults to propose a novel dilated normalized residual convolutional neural network (DRNCNN) fault diagnosis model for high-voltage direct current (HVDC) transmission systems. To address the insufficiency of the traditional model’s receptive field in dealing
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This paper focus on direct current (DC) filter grounding faults to propose a novel dilated normalized residual convolutional neural network (DRNCNN) fault diagnosis model for high-voltage direct current (HVDC) transmission systems. To address the insufficiency of the traditional model’s receptive field in dealing with high-dimensional and nonlinear data, this paper incorporates dilated convolution and batch normalization (BN), significantly enhancing the CNN’s capability to capture complex spatial features. Furthermore, this paper integrates residual connections and parameter rectified linear units (PReLU) to optimize gradient propagation and mitigate the issue of gradient vanishing during training. These innovative improvements, embodied in the DRNCNN model, substantially increase the accuracy of fault detection, achieving a diagnostic accuracy rate of 99.28%.
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(This article belongs to the Section Electromechanical Energy Conversion Systems)
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Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method
by
Xing Shui, Zhijun Rong, Binbin Dan, Qiangjian He and Xin Yang
Machines 2024, 12(3), 184; https://doi.org/10.3390/machines12030184 - 12 Mar 2024
Abstract
Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making
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Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches.
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(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)
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Open AccessArticle
Studying the Performance of Reinforced Polymer Gear Wheels: Development of an Advanced Test Bench for Wear Analysis
by
Luca Landi, Giulia Morettini, Massimiliano Palmieri, Stefano Benicchi, Filippo Cianetti and Claudio Braccesi
Machines 2024, 12(3), 183; https://doi.org/10.3390/machines12030183 - 11 Mar 2024
Abstract
In recent years, polymeric materials have gained prominence as a competitive option for gear manufacturing. Nevertheless, the absence of comprehensive literature addressing the wear due to the coupling of these materials presents a real challenge in response to this innovative trend. Wear of
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In recent years, polymeric materials have gained prominence as a competitive option for gear manufacturing. Nevertheless, the absence of comprehensive literature addressing the wear due to the coupling of these materials presents a real challenge in response to this innovative trend. Wear of plastic gearwheels represents, in fact, a key issue, traditionally assessed using standard formulations under optimal dry operating conditions. These calculations often rely on coefficients derived from specialized gear tests, but their applicability is constrained to specific polymer–metal combinations. This research was dedicated to the development of a test bench tailored to evaluate the wear of glass fiber-reinforced self-lubricating polymer gearwheels under different operating conditions. This study commenced with a comprehensive exploration of wear phenomena in thermoplastic gearwheels and the inherent challenges associated with utilizing existing standards and the scientific literature for wear analysis. This was followed by a careful evaluation of the operational needs of the test bench, which, starting from a basic solution already implemented, improved its use in various aspects. Finally, this study introduced an optical-based methodology for average linear wear control. This research strived to establish a testing approach that minimizes uncertainties when assessing the wear of thermoplastic gears.
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(This article belongs to the Section Machines Testing and Maintenance)
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Open AccessArticle
Synthesis of Geared Planar Linkage Mechanisms through the Segmentation of Multiloop Mechanisms into Discrete Chains
by
Sean Mather and Arthur Erdman
Machines 2024, 12(3), 182; https://doi.org/10.3390/machines12030182 - 11 Mar 2024
Abstract
Gears are foundational tools used to transmit or modify mechanical energy or motion. Implementing gears into planar linkage mechanisms is less common but can be a similarly valuable technique that takes advantage of the high efficiency of gears while producing complex and precise
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Gears are foundational tools used to transmit or modify mechanical energy or motion. Implementing gears into planar linkage mechanisms is less common but can be a similarly valuable technique that takes advantage of the high efficiency of gears while producing complex and precise motions. While recent numerical methods for designing these geared planar linkage mechanisms (GPLMs) have proliferated in the literature, analytical approaches have their merits and have received less attention. Here, an analytical alternative is presented as a modification of the complex-number loop-based synthesis method for designing multiloop mechanisms. Some of the base topologies for geared dyad, triad, and quadriad chains are presented, along with a numerical example demonstrating the solution procedure’s effectiveness.
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(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Open AccessArticle
Imperative Formal Knowledge Representation for Control Engineering: Examples from Lyapunov Theory
by
Carsten Knoll, Julius Fiedler and Stefan Ecklebe
Machines 2024, 12(3), 181; https://doi.org/10.3390/machines12030181 - 08 Mar 2024
Abstract
In this paper, we introduce a novel method to formally represent elements of control engineering knowledge in a suitable data structure. To this end, we first briefly review existing representation methods (RDF, OWL, Wikidata, ORKG). Based on this, we introduce our own approach:
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In this paper, we introduce a novel method to formally represent elements of control engineering knowledge in a suitable data structure. To this end, we first briefly review existing representation methods (RDF, OWL, Wikidata, ORKG). Based on this, we introduce our own approach: The Python-based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected theorems from Lyapunov’s theory. While the approach is still experimental, the current result already allows the application of methods of automated quality assurance and a SPARQL-based semantic search mechanism. The feature set of the framework is demonstrated by various examples. The paper concludes with a discussion of the limitations and directions for further development.
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(This article belongs to the Special Issue Nonlinear Control Applications and New Perspectives)
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