Tool deflection compensation by drive signal-based force reconstruction and process control

Denkena, Berend; Bergmann, Benjamin; Stoppel, Dennis

doi:10.1016/j.procir.2021.11.096

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Tool deflection [219] Finite element modeling of the cutting tool and workpiece based on a mechanistic approach to determine cutting forces -Milling Online Tool deflection compensation [220] A method is developed that utilizes the drive signals to compensate for tool deflections. Based on the evaluated forces from the machine tool's drive signals, the tool path is compensated orthogonal to the feed direction Drive signal Milling…”

Section: Offlinementioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

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Section: Offlinementioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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show abstract

“…Networked production landscape and machine tools also contribute to the development and exploitation of AI techniques by providing novel approaches for building process variables and quality indicators as inherent parts in DT for manufacturing processes. Denkena et al [34] applied a long short-term memory (LSTM) NN taking drive current signals as inputs to reconstruct the process cutting forces. On this basis, the tool deflection was compensated, thus optimizing the resulting workpiece shape deviation.…”

Section: Related Workmentioning

confidence: 99%

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Huang

Fey

Liu

et al. 2023

Robotics and Computer-Integrated Manufacturing

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“…In particular, the cutting condition optimization is important because it directly affects machining time and accuracy. The methods to optimize the cutting conditions can be classified into two types: online and offline (Denkena and et al, 2021). As per the online methods, for instance, the feed rate is optimized in-process by referring to the motor current of the machine tools or the force sensor signals so that the cutting force or energy match with the target values (Zuperl, 2005;Ridwan and Xu, 2013).…”

Section: Introductionmentioning

confidence: 99%

Autonomous optimization of cutting conditions in end milling operation based on deep reinforcement learning (Offline training in simulation environment for feed rate optimization)

KANEKO,

KOMATSU,

ZHOU

et al. 2023

JAMDSM

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Full automation of manufacturing is strongly desired to improve the productivity. Autonomous optimization of the cutting conditions in the end milling operation is one of the challenges in achieving this goal. This paper proposes a system for optimization of the cutting conditions based on Deep Q-Network (DQN), which is a kind of deep reinforcement learning. An end mill is used as an agent and the end milling simulation is employed to provide the environment in the proposed system. Geometric information of interference state between tool and workpiece in the simulation is considered as the state of the environment and acceleration of feed rate is the action for the agent to take. The action is optimized by DQN to maximize the accumulated reward given from the environment, which evaluates how good the scenario of action is. Therefore, the cutting conditions can be optimized according to the defined reward function. We performed three case studies to verify our proposed method, in which the cutting torque is controlled to be a specified value.The objective was successfully achieved regardless of differences in the end milling scenario. The obtained results strongly suggested a fact that the reinforcement learning is a promising solution to autonomous optimization of the cutting conditions.

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Tool deflection compensation by drive signal-based force reconstruction and process control

Cited by 8 publications

References 12 publications

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Autonomous optimization of cutting conditions in end milling operation based on deep reinforcement learning (Offline training in simulation environment for feed rate optimization)

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