Article
Chemistry, Analytical
Sriraamshanjiev Natarajan, Mohanraj Thangamuthu, Sakthivel Gnanasekaran, Jegadeeshwaran Rakkiyannan
Summary: A technique based on Digital Twins is proposed to achieve accurate monitoring and prediction of tool conditions. By collecting vibration and sound signal data from physical systems, such as milling machines, and training the data with machine learning algorithms, the tool condition can be accurately monitored and predicted.
Article
Automation & Control Systems
Renjie Ge, Song Zhang, Renwei Wang, Xiaona Luan, Irfan Ullah
Summary: This research aims to investigate the energy consumption of machine tools and establish a cutting power model for ball-end milling. By conducting experiments, the study revealed and validated mathematical functions for spindle power and feed power. Results showed that the proposed method accurately predicted spindle power, feed power, and cutting power. The study's proposed power investigation method provides technical support for understanding machine tools' energy consumption characteristics.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Industrial
E. Traini, G. Bruno, F. Lombardi
Summary: This paper presents a data-driven framework for estimating tool wear status and predicting its remaining useful life using machine learning techniques. The framework includes data preprocessing, feature engineering, and development of prediction models. A case study in a milling process demonstrates the potential of the framework for tool condition monitoring.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Review
Engineering, Manufacturing
Ni Chen, Hao Nan Li, Jinming Wu, Zhenjun Li, Liang Li, Gongyu Liu, Ning He
Summary: This article provides a comprehensive overview of micro milling cutters, covering their uniqueness, material removal mechanisms, materials, structures, designs, fabrication techniques, and machining performances. It also outlines several possible future research directions to offer potential insights for the micro milling community and future researchers.
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
(2021)
Article
Automation & Control Systems
Joanna Jastrzebska, Arkadiusz Parus, Daniel Jastrzebski, Piotr Pawelko
Summary: This work presents a method for measuring the translational quasi-static stiffness of a machine tool using the Stiffness Workspace System (SWS). The SWS is modified and the changes in measurement procedure, data analysis, and technical solutions are described in detail. A methodology for determining generalised translational static stiffness is introduced. The paper aims to provide information about the quasi-static stiffness values of a typical machine tool. A case study on a 5-axis machining centre is conducted to implement the measurement procedure. The measurement results are used to determine the generalised translational stiffness indicators and estimate the static stiffness distribution on the XY plane.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Engineering, Mechanical
Huihui Miao, Chenyu Wang, Changyou Li, Wenjun Song, Xiulu Zhang, Mengtao Xu
Summary: This research focuses on developing a dynamic model of the whole machine tool considering the milling process to predict vibration response and dynamic characteristics. The proposed model is evaluated by measuring and comparing frequency response functions, cutting forces, and vibration responses under different cutting conditions. The experimentally confirmed model is used for parameter analysis, revealing the influence of preload and cutting conditions on the dynamic characteristics of the machine tool during machining.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Engineering, Mechanical
Xing Zhang, Yang Gao, Zhuocheng Guo, Wei Zhang, Jia Yin, Wanhua Zhao
Summary: A physical model-based tool wear and breakage monitoring method is proposed in this paper. The comprehensive feature is extracted from the seven-channel specific cutting force coefficients (SCFCs) through the measurements of milling force, spindle box vibration and driving current for tool wear monitoring. An efficient tool breakage monitoring method is also put forward. The verification results indicate that the proposed method can accurately estimate the cutting status of the tool and provide a research basis for the potential industrial application of tool wear and breakage monitoring technology.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
T. Mohanraj, Jayanthi Yerchuru, H. Krishnan, R. S. Nithin Aravind, R. Yameni
Summary: This study attempted to predict tool wear using wavelet analysis and machine learning algorithms, with Hoelder's exponent and wavelet coefficients performing better in forecasting flank wear. Decision trees and support vector machines demonstrated the highest accuracy in the selected machine learning models.
Article
Engineering, Chemical
Purvam Mehulkumar Gandhi, Siva Kumar Valluri, Mirko Schoenitz, Edward Dreizin
Summary: This study investigated the effects of liquid process control agents (PCA) on the properties of milled powders during high-energy ball milling. Correlations between different PCA properties and milling outcomes were identified. It was found that PCA density had an impact on particle and crystallite sizes, but its effect was less significant compared to other parameters. Particle size was strongly correlated with PCA proton affinity and surface tension for Bi2O3 and CuO, respectively, while crystallite size was most strongly correlated with PCA dynamic viscosity and density for Bi2O3 and CuO, respectively.
ADVANCED POWDER TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Pengfei Ding, Xianzhen Huang, Chengying Zhao, Huizhen Liu, Xuewei Zhang
Summary: In modern manufacturing, micro-milling technology is crucial for producing high-precision and complex micro-size parts. Understanding the changing rule of time-varying cutting is significant for comprehending the micro-milling mechanism and improving machining efficiency. Additionally, identifying and updating tool wear in advance can enhance the accuracy and sustainability of micromachining. This study proposes a tool wear prediction framework for micro-milling using a temporal convolution network, bi-directional long short-term memory, and a multi-objective arithmetic optimization algorithm. A new integrated model for real-time micro-milling cutting force monitoring is then developed, considering factors such as tool deformation, tool runout, time-varying cutting coefficient, chip separation state, and tool wear estimation results. The accuracy of the proposed tool wear prediction and cutting force model is verified through micro-milling experiments with Al6061 workpiece material. The developed model provides theoretical guidance for statics and dynamics analysis in micro-milling.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Haijun Zhang, Shijin Lu, Chunyu Zhang, Guo Li, Fei Teng, Junjie Zhang, Tao Sun
Summary: The paper investigates the tool chatter behavior and its correlation with the machined surface morphology in the ultra-precision diamond micro-milling of a copper workpiece. Through a combination of finite element simulations and experimental validations, the research provides a theoretical basis for understanding the origination of tool chatter and the rational selection of machining parameters.
APPLIED SCIENCES-BASEL
(2022)
Article
Mathematics, Applied
Siamak Pedrammehr, Mahsa Hejazian, Mohammad Reza Chalak Qazani, Hadi Parvaz, Sajjad Pakzad, Mir Mohammad Ettefagh, Adeel H. Suhail
Summary: The research aims to optimize different milling parameters to achieve the maximum material removal rate while minimizing tool wear and surface roughness. Experimental data is used to develop mathematical models, and genetic algorithms are applied to determine the optimal cutting parameters.
Article
Engineering, Multidisciplinary
Pawel Twardowski, Maciej Tabaszewski, Martyna Wiciak-Pikula, Agata Felusiak-Czyryca
Summary: The study focuses on monitoring tool wear based on acoustic emission signals, using machine learning methods to accurately predict tool condition class with an error value below 6% through the decision tree approach. It also compares the results with other machine learning methods.
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Miroslav Janota, Petr Kolar, Jiri Falta, Tomas Kozlok
Summary: This paper presents an in-process identification method of the tangential cutting force coefficient using the spindle power signal. The calibration of the spindle drive system was successfully implemented and the results are consistent with the available literature. The method ensures accurate measurement of the tangential component and improves the accuracy and effectiveness of cutting force modeling.
Article
Engineering, Mechanical
Dongqian Wang, Lars Penter, Albrecht Haenel, Yang Yang, Steffen Ihlenfeldt
Summary: This paper proposes a complete and practical model for micro-milling process, including dynamic tool deflection and a stability lobe diagram with runoutdependent effect. The study identifies the frequency response function at the tool point using a multi-sections discretization method combined with dynamic tests. The dynamic tool deflection is then developed based on force decomposition and the frequency response function. The interaction between feed per tooth, runout, and dynamic tool deflection is examined, resulting in the proposed stability lobe diagram.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Filipe Figueiredo dos Santos, Sandro da Costa Silva, Alexandre Mendes Abrao, Berend Denkena, Bernd Breidenstein, Kolja Meyer
Summary: Surface integrity is crucial for the functional performance of mechanical components, and deep rolling is an important surface treatment method. The study found that deep rolling significantly improves surface finish, but excessive pressure and feed can increase roughness. Increasing the number of rolling passes, however, helps improve surface finish.
JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME
(2021)
Article
Engineering, Manufacturing
Berend Denkena, Marc-Andre Dittrich, Siebo Stamm, Marcel Wichmann, Soren Wilmsmeier
Summary: This passage discusses the challenges faced by production systems, bio-inspired production systems, and the concepts of "Gentelligence" and "process-DNA," as well as their performance in different applications.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Manufacturing
Berend Denkena, Alexander Kroedel, Sascha Beblein
Summary: The current state of the art shows that the friction behavior during machining is not yet fully understood, requiring further research. A novel method for analyzing friction mechanisms in machining was proposed in this study, revealing a significant influence of the sliding velocity of the chip on the coefficient of friction across a wide range of coating properties and cutting velocities.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Manufacturing
P. Kuhlemann, B. Denkena, T. Grove
Summary: Current research demonstrates that the mechanical surface-strengthening process of deeprolling is effective in improving the strength and life span of highly stressed components. Adjusting the process temperature during deep-rolling can further increase the mechanical life span, especially in soft machining operations where higher degrees of plastic deformation are induced in the subsurface.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Industrial
Berend Denkena, Alexander Kroedel, Arne Muecke, Lars Ellersiek
Summary: Surface quality is crucial in 5-axis ball end milling for determining component performance. This paper introduces a new method to predict surface defects, allowing for the selection of suitable process parameters without extensive experimental efforts.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2021)
Article
Engineering, Manufacturing
Berend Denkena, Fritz Schinkel, Jonathan Pirnay, Soren Wilmsmeier
Summary: This paper introduces a new method for process-parallel Flexible Job Shop Scheduling based on quantum computing optimization, showcasing its good performance and practicality through a scientific benchmark and application to a real use-case. A managerial insight demonstrates how this approach can be integrated into existing production planning and control IT infrastructure.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Review
Engineering, Manufacturing
Berend Denkena, Benjamin Bergmann, Alexander Schmidt
Summary: This study investigates the capability of sensor fusion based on principal component analysis to monitor preload loss of single nut ball screws, by studying features of different preload levels of the ball screw through selecting different ball diameters. The results show that this method can reliably detect preload levels.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Industrial
Berend Denkena, Patrick Ahlborn
Summary: This paper presents a novel linear-rotary direct drive for machine tools, which combines linear and rotary movement in one drive to enhance the overall dynamics of the machine and reduce installation space.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Industrial
Carolin Kellenbrink, Nicolas Nuebel, Andre Schnabel, Philipp Gilge, Joerg R. Seume, Berend Denkena, Stefan Helber
Summary: This paper presents a cyber-physical system demonstrator for the maintenance, repair, and overhaul (MRO) of high-pressure turbine blades of aircraft engines. By using a virtual layer and a virtual twin, the system handles the variability in damage patterns and achieves individual, flexible, and economically optimized MRO actions. It showcases the combination and innovation of research results from multiple disciplines.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Lars Schoenemann, Oltmann Riemer, Bernhard Karpuschewski, Per Schreiber, Heinrich Klemme, Berend Denkena
Summary: The research has found that ultra-precision cutting based on digital surface twins can accurately predict surface features and characteristics, thus supporting the development of tool offset compensation methods and improving productivity.
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
(2022)
Article
Engineering, Manufacturing
B. Denkena, A. Kroedel, M. Wilckens
Summary: The use of larger CBN grains in grinding can improve material removal rates for hardened steel components and achieve higher depths of cut. Grinding with coarse grains results in lower process forces, higher residual stress, and rougher surfaces, with minimal wear observed. In some cases, using larger grains can enhance tool performance and allow for higher feed rates.
PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT
(2021)
Article
Engineering, Electrical & Electronic
Berend Denkena, Benjamin Bergmann, Matthias Witt
Summary: To achieve increased automation and flexibility in production, it is necessary to monitor component-specific characteristics and evaluate signals highly correlated with process quality. The use of material-specific cutting force improves the sensitivity of confidence limits to process errors, allowing the force-sensitive machine to substitute the dynamometer for process monitoring.
Article
Engineering, Manufacturing
Berend Denkena, Marc-Andre Dittrich, Hai Nam Nguyen, Konrad Bild
Summary: Self-optimizing process planning uses machine learning models to correlate process parameters with surface quality, automatically adjusting optimal parameters to achieve target roughness.
PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT
(2021)
Article
Engineering, Manufacturing
Berend Denkena, Bernd-Arno Behrens, Benjamin Bergmann, Malte Stonis, Jens Kruse, Matthias Witt
Summary: This study predicts variations in dimension and cavities during cross-wedge rolling of shafts based on measured tool pressure. Multi-linear regression models are developed to determine the resulting diameters of the shaft shoulder, showing better prediction accuracy than models based on meta-data. The sensor concept for a new cross-wedge rolling machine and the approach for monitoring machining processes of workpieces with dimensional variations are presented for upcoming studies.
PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT
(2021)
Article
Engineering, Manufacturing
Berend Denkena, Alexander Kroedel, Steffen Heikebruegge, Kolja Meyer, Philipp Pillkahn
Summary: This study investigates the impact of machining parameters on surface topography after deep rolling, and introduces a novel tool concept to explore the predictability of surface topography for milled specimens. By adjusting parameters gradually, the minimum pressure and lateral displacement conditions were identified to ensure accuracy in predicting surface topography.
PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT
(2021)
