Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
103,383
result(s) for
"Machine Tools"
Sort by:
Precision machining technology
\"Introduces students, both at the secondary and postsecondary levels, to the exciting world of precision machining technology as it is practiced in the 21st century.\"
Book
Machine Tool 4.0 for the new era of manufacturing
The widespread use and continuous improvements of machine tools have had a significant impact on productivity in manufacturing industry ever since the Industrial Revolution. At the dawn of the new era of industrialization, the need to advance machine tools to a new level that accords to the concept of Industrie 4.0 has to be recognised and addressed. Muck like the different stages of industrialisation, machine tools have also gone through different stages of technological advancements, i.e., Machine Tool 1.0, Machine Tool 2.0 and Machine Tool 3.0. Industrie 4.0 pleads for a new generation of machines—Machine Tool 4.0. This paper describes some of the key and desired characteristics of Machine Tool 4.0 such as Cyber-physical Machine Tools, vertically and horizontally integrated machine tools and more intelligent, autonomous and safer machine tools.
Journal Article
Traditional toolmaking : the classic treatise on lapping, threading, precision measurements, and general toolmaking
A reference for toolmaking skills and techniques, originally published in 1915.
Book
Robust modeling method for thermal error of CNC machine tools based on random forest algorithm
Thermal error of machine tools has a huge influence on the accuracy of the workpiece. However, the nonlinearity of the thermal error limits the accuracy and robustness of the prediction model. With the rapid advancement in artificial intelligence, this paper presents a novel thermal error modeling method based on random forest. The model’s hyper-parameters are easy to be optimized by grid searching method integrating with cross validation. The temperature features are measured as the model input. Based on the out-of-bag data generated during modeling process, the proposed model itself can simultaneously evaluate the temperature feature importance through comparing the decrease in model’s the prediction accuracy after randomly shuffling the value of the target feature. Moreover, to enhance the model performance and reduce the measurement and computational cost, the method of selecting key temperature points are presented to exclude the redundant features through iteratively eliminating the least important feature and comparing the prediction accuracy under different feature combinations. Furthermore, the hysteresis effect between temperature and deformation is also considered. The method of determining the time lag is proposed through permuting the original time series of the target feature while keeping the remainder constant and comparing the resultant relative importance. A thermal error experiment validates the accuracy and robustness of the proposed model which can continuously maintain the prediction accuracy of over 90% in spite of varying operation conditions. Compared to conventional machine learning methods, the proposed model requires less training data, enables faster and more intuitive parameter tuning, achieves higher prediction accuracy, and has stronger robustness.
Journal Article
A Review of Machine Learning-Based Thermal Error Modeling Methods for CNC Machine Tools
Heat source-induced thermal error is a primary element influencing the precision of CNC machine tools. A practical and economical approach to mitigating thermal errors is through thermal error compensation. To provide a comprehensive understanding of thermal error modeling and its advancements, this paper systematically reviews machine learning-based methods for thermal error compensation. Thermal error modeling is the most critical step in thermal error compensation, as it directly influences the effectiveness of the compensation due to its accuracy and robustness. With the rapid development of big data and artificial intelligence, machine learning has emerged as a powerful tool in thermal error modeling, leading to significant research progress in recent years. In this paper, an overview of the thermal error modeling methods based on deep learning that have been researched and applied in recent years is presented. Specifically, two methods for reducing thermal errors, namely, thermal error suppression and thermal error compensation, are introduced and analyzed. Second, machine learning-based thermal error modeling methods are categorized into traditional machine learning-driven and deep learning-driven approaches. The application of these two methods in thermal error modeling and compensation is reviewed and summarized in detail. By synthesizing these studies, this paper identifies key challenges and trends in machine learning-based thermal error modeling. Finally, the thermal error modeling methods discussed in this paper are summarized, and future research directions are proposed to further enhance modeling accuracy and robustness.
Journal Article
Machining for dummies
\"This hands-on guide begins with basic topics like tools, work holding, and ancillary equipment, then goes into drilling, milling, turning, and other necessary metalworking processes. You'll also learn about robotics and new developments in machining technology that are driving the future of manufacturing and the machining market\"--Amazon.com.
Book
Real-time machining data application and service based on IMT digital twin
With the development of manufacturing, machining data applications are becoming a key technological component of enhancing the intelligence of manufacturing. The new generation of machine tools should be digitalized, highly efficient, network-accessible and intelligent. An intelligent machine tool (IMT) driven by the digital twin provides a superior solution for the development of intelligent manufacturing. In this paper, a real-time machining data application and service based on IMT digital twin is presented. Multisensor fusion technology is adopted for real-time data acquisition and processing. Data transmission and storage are completed using the MTConnect protocol and components. Multiple forms of HMIs and applications are developed for data visualization and analysis in digital twin, including the machining trajectory, machining status and energy consumption. An IMT digital twin model is established with the aim of further data analysis and optimization, such as the machine tool dynamics, contour error estimation and compensation. Examples of the IMT digital twin application are presented to prove that the development method of the IMT digital twin is effective and feasible. The perspective development of machining data analysis and service is also discussed.
Journal Article
Energy Efficiency in Turning: A Comparative Analysis of Screw Drive and Linear Drive CNC Machine Tools
This paper presents a comparative analysis of the energy efficiency of screw drive and linear drive CNC machine tools in turning operations. Two CNC lathes were investigated, one equipped with screw drives and the other with linear drives, during the turning of specially prepared parts. The research examines active and reactive energy consumption, offering insights into the energy efficiency of different drive technologies. The analysis indicates that lathes with linear drives exhibited a higher reactive power consumption (8 kVar) during idle operation in comparison to those with screw drives (1.2 kVar). However, both drive systems demonstrated comparable potential for reducing reactive power consumption through implementing compensation techniques, with a reduction in reactive power consumption of nearly 70%. For both drive systems, the reduction in power use with compensation was at the level of 23–30% for screw drives and 36–47% for linear drives. The study highlights the importance of considering both active and reactive energy in evaluating the energy efficiency of machine tools. The findings contribute to a deeper understanding of energy consumption in turning processes, aiding in the selection and optimization of drive systems for improved sustainability in manufacturing. Future research should explore tool wear impacts, machine-specific energy optimization, and AI-driven solutions for real-time energy management.
Journal Article