Condition Monitoring of CNC Machining Using Adaptive Control

Prasad, Balla Srinivasa; Dora, Siva Prasad; Sandeep, Adem; Veeraiah, G.

doi:10.1007/s11633-013-0713-1

Cited by 19 publications

(11 citation statements)

References 8 publications

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“…A decision-making module in an intelligent manufacturing system estimates the proper time for the tool change that maximizes tool utilization and avoids any possible damage to the workpiece [142]. Prasad et al [143] developed an adaptive control machining system for a numerical turning operation, in which the process is constrained based on a set of predefined thresholds. The developed self-tuning system adjusts the cutting parameters (cutting speed, feedrate, and depth of cut) to maintain the flank wear and tool deflection under specific limits that are defined for a certain workpiece.…”

Section: Tool Wear Monitoring and Controlmentioning

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: Tool Wear Monitoring and Controlmentioning

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 current trend of setting and programming algorithms for CNC machines is for the machine to be able to evaluate the critical values of the given parameter and accordingly adjust the production cycle by adjusting the operating conditions. These conditions are met using the process of adaptive control [26,27]. The first step for the implementation and creation of an adaptive control system is the interpretation of the measured critical parameters into the creation of a structural model [28].…”

Section: Introductionmentioning

confidence: 99%

Advisability-Selected Parameters of Woodworking with a CNC Machine as a Tool for Adaptive Control of the Cutting Process

Kminiak

Němec

Igaz

et al. 2023

Forests

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The operation of CNC machining centers, despite their technological progress, can still be affected by undesirable events associated with the technological parameters of their operation. The minimization of these risks can be achieved via their adaptive control in the process of operation. Several input parameters for adaptive control are still the subject of research. The work aimed to find out the influence of the change in feed speed, revolutions, and radial depth of cut on the noise and temperature of the tool during the milling of wood-based composite material particleboard. At the same time, it was evaluated whether it is possible to use the measured values of these parameters in the future in the process of an adaptive control of the CNC machine with the minimization of their negative influence. The methods of measuring these parameters were chosen based on valid legislation and previous research. The results of the research show that all parameters influence both the noise and temperature of the tool, while the rate of the radial depth of cut has the greatest influence on the increase in temperature, and the noise is most affected by the revolutions. The effect of temperature during woodworking can also be characterized in terms of the potential long-term wear of the cutting tool. The setting of optimization algorithms of monitored parameters in the adaptive control of the CNC machining center will be the subject of further research.

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“…Adaptive control (AC) of machine tools introduces a promising technique to optimize, monitor, and control machining process to achieve the fully automated system attaining maximum productivity. In contrast to NC part program operation, adaptive control techniques [6] feed and/or speed to their optimal levels during each operation so as to achieve some objectives under the imposed system constraints. Measurable responses include cutting forces [6], [7], [9] and [11], and vibration [6].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to NC part program operation, adaptive control techniques [6] feed and/or speed to their optimal levels during each operation so as to achieve some objectives under the imposed system constraints. Measurable responses include cutting forces [6], [7], [9] and [11], and vibration [6]. Based on this approach, AC system improves metal removal rate MRR through reducing cycle time [7], maintains surface roughness for the machined surface [6] and [9] or prevents damage to tools, parts, and machine, and minimizing production disruptions [10] and [11].…”

Section: Introductionmentioning

confidence: 99%

“…Measurable responses include cutting forces [6], [7], [9] and [11], and vibration [6]. Based on this approach, AC system improves metal removal rate MRR through reducing cycle time [7], maintains surface roughness for the machined surface [6] and [9] or prevents damage to tools, parts, and machine, and minimizing production disruptions [10] and [11]. However, many control systems and instrumentation problems are still the main obstacles delaying the establishment of a universally feasible commercial system.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Control Program for Rough Turning Machining Processes

2016

6th International Conference on Advances in Engineering Sciences and Applied Mathematics (ICAESAM-2016) Dec. 21-22, 2016 Kuala

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Although much research have been carried out to study and evaluate theory of metal cutting and machining, unsatisfactory repetitive outcome was obtained with a wide domain of variability. Although numerical control (NC) technology of machine tools has contributed to the machining topic in terms of more flexibility, better surface quality and dimensional accuracy, and higher productivity, it still incapable to adapt to the dynamic conditions that result from continuous variations during cutting. Current CNC machines follow preprogrammed fixed feeds and speeds during each cutting segment. In contrast to NC procedures, adaptive control (AC) technique measures the process output (responses) in real time, and automatically adjusts and continuously tunes cutting feed and/or speed to the optimal levels during each operation so as to achieve some objectives under the imposed system constraints. In the current work, an adaptive control simulation strategy is proposed in which the core of the optimization routine is based on some mathematical empirical models that define the interrelationship between the system responses (output) and the operating conditions (speed and feed). These models independently define the targeted primary objective; the metal removal rate (MRR), the secondary objectives; wear or its rate, and the system constraints; cutting forces and machining power. Optimization strategy involves the search for the best operational speed and/or feed combination that maximizes the MRR while attaining the lowest possible edge wear and/or its rate (tool life) under system constraints of tolerable force level and available spindle power. While all previously developed AC approaches addressed only the cutting feed and its relevant force level in milling, the proposed mathematical-model based adaptive control system deals with the turning operation where the cutting speed, as the main controlling parameter of the edge wear, along with the feed are considered.

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Condition Monitoring of CNC Machining Using Adaptive Control

Cited by 19 publications

References 8 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

Advisability-Selected Parameters of Woodworking with a CNC Machine as a Tool for Adaptive Control of the Cutting Process

Adaptive Control Program for Rough Turning Machining Processes

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