Data-Driven Thermal Deviation Prediction in Turning Machine-Tool - A Comparative Analysis of Machine Learning Algorithms

Ouerhani, Nabil; Loehr, Bernard; Rizzotti-Kaddouri, Aïcha; Pinho, Dylan Santo De; Limat, Adrien; Schinderholz, Philippe

doi:10.1016/j.procs.2022.01.217

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…Intelligent data-driven manufacturing optimizes the efficiency and connectivity of intelligent machine tools using advanced data analysis [48], with a strong focus on ensuring data security and privacy [49]. Intelligent precision machining is centered around enhancing machining process efficiency, surface quality, and energy efficiency via the utilization of advanced manufacturing and intelligent control for high precision and optimization [50]. Structural optimization and performance enhancement both focus on improving the structure and components of CNC machine tools to achieve higher precision and speed [51], thus effectively advancing the intelligence of machine tools.…”

Section: Emerging Technology Forecasts and Analysismentioning

confidence: 99%

Forecasting Emerging Technologies in Intelligent Machine Tools: A Novel Framework Based on Community Analysis

He,

Liu,

Liu

2024

Machines

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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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Section: Emerging Technology Forecasts and Analysismentioning

confidence: 99%

Forecasting Emerging Technologies in Intelligent Machine Tools: A Novel Framework Based on Community Analysis

He,

Liu,

Liu

2024

Machines

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“…The other is the DT-based thermal error model [2]. However, the large heat sources and rapid speed changes of the CNCMT spindle system lead to significant time-varying thermal errors [3]. Based on a single thermal error modeling method, it is difficult to achieve a real-time and accurate reflection of the physical spindle system, which seriously hinders the efficient operation of CNCMT.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

Lu,

Zhu,

Wang

et al. 2023

Lubricants

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Traditional methods for predicting thermal error ignore the correlation between physical world data and virtual world data, leading to the low prediction accuracy of thermal errors and affecting the normal processing of the CNC machine tool (CNCMT) spindle. To solve the above problem, we propose a thermal error prediction approach based on digital twins and long short-term memory (DT-LSTM). DT-LSTM combines the high simulation capabilities of DT and the strong data processing capabilities of LSTM. Firstly, we develop a DT system for the thermal characteristics analysis of a spindle. When the DT system is implemented, we can obtain the theoretical value of thermal error. Then, the experimental data is used to train LSTM. The output of LSTM is the actual value of thermal error. Finally, the particle swarm optimization (PSO) algorithm fuses the theoretical values of DT with the actual values of LSTM. The case study demonstrates that DT-LSTM has a higher accuracy than the single method by nearly 11%, which improves the prediction performance and robustness of thermal error.

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Thermal Compensation of Sudden Working Space Condition Changes in Swiss-Type Lathe Machining

Kaftan

Mayr

Wegener

2023

Lecture Notes in Production Engineering

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The Swiss-type lathe is a specialized turning machine of Swiss-origin with a wide range of applications across the precision machining industry. Its unique features enable it to mass produce parts at high speeds and with high precision. However, the complex non-symmetric structure of the machine tool makes it particularly susceptible to the adverse effects of thermal influences. These internal and external thermal influences cause an offset at the tool center point and degrade the accuracy of the produced part. It is a common practice in Swiss-type lathe machining for an operator to open the machine door during a production run to exchange tools or inspect the produced part. Consequently, thermal boundary conditions change rapidly when cooler ambient air enters the working space of the machine tool and when the air heats up after the machine tool is restarted. The machine tool exhibits short cool-down and warm-up cycles during which the thermal errors change abruptly and can be challenging to compensate, as it is well known in the machine tool industry. This work develops a novel methodology based on artificial intelligence that compensates thermal errors associated with sudden boundary condition changes. The results show that thermal error residual peaks associated with a machine tool door opening are significantly attenuated and the peak-to-peak thermal error of the Swiss-type lathe is reduced.

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Data-Driven Thermal Deviation Prediction in Turning Machine-Tool - A Comparative Analysis of Machine Learning Algorithms

Cited by 10 publications

References 9 publications

Forecasting Emerging Technologies in Intelligent Machine Tools: A Novel Framework Based on Community Analysis

Forecasting Emerging Technologies in Intelligent Machine Tools: A Novel Framework Based on Community Analysis

Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

Thermal Compensation of Sudden Working Space Condition Changes in Swiss-Type Lathe Machining

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