Optimization of turning parameters based on tool wear and machining cost for various parts

Chung, Chunhui; Wang, Po-Chieh; Chinomona, Benvolence

doi:10.1007/s00170-022-09037-y

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…An inaccurate selection of cutting parameters could cause damage to the cutting tool, resulting in premature tool failure, increased tooling costs, and part damage. Poor control of tool wear in machining leads to out-of-tolerance parts and increased machine down-time, which, indirectly, may account for 30% of the total machining cost [131]. The cutting speed is the most crucial parameter affecting the tool life and surface quality in machining hard-tocut materials [132].…”

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

View full text Add to dashboard Cite

show abstract

“…Inaccurate selection of cutting parameters could cause damage to the cutting tool, resulting in premature tool failure, increased tooling costs and part damage. Poor control of tool wear in machining leads to out-of-tolerance parts and increased machine down-time, which indirectly may account for 30% of the total machining cost [85]. Cutting speed is the most crucial parameter affecting tool life and surface quality in machining hard-to-cut materials [86,87].…”

Section: Tool Wear: Models and Implementationsmentioning

confidence: 99%

High-Performance Machining System: Process Optimization for a Cyber-Physical System – a Review

Gohari,

Hassan,

Shi

et al. 2024

Preprint

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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 the 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 the uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems with a focus on the 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 the offline and online process optimization approaches have been presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, and reduced energy consumption and carbon emissions are independently investigated for the offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, the paper explores the challenges and opportunities inherent in developing a robust cyber-physical optimization system

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“…The authors relate the correct strategy selection to a reduction in tool wear, machining forces, and overall machining cost. Machining parameters have a great influence on the process [21], affecting factors such as tool wear [22], surface quality [23], and material removal rate [24], which affect the total operation time and, thus, the cost of the machining operations. Zhang et al [25], study the reduction of energy consumption for micro-milling processes, by proposing an energy model.…”

Section: Introductionmentioning

confidence: 99%

Build-Up an Economical Tool for Machining Operations Cost Estimation

Silva

Sousa

Pinto

et al. 2022

Metals

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Currently, there is a lack of affordable and simple tools for the estimation of these costs, especially for machining operations. This is particularly true for manufacturing SMEs, in which the cost estimation of machined parts is usually performed based only on required material for part production, or involves a time-consuming, non-standardized technical analysis. Therefore, a cost estimation tool was developed, based on the calculated machining times and amount of required material, based on the final drawing of the requested workpiece. The tool was developed primarily for milling machines, considering milling, drilling, and boring/threading operations. Regarding the considered materials, these were primarily aluminum alloys. However, some polymer materials were also considered. The tool first estimates the required time for total part production and then calculates the total cost. The total production time is estimated based on the required machining operations, as well as drawing, programming, and machine setup time. A part complexity level was also introduced, based on the number of details and operations required for each workpiece, which will inflate the estimated times. The estimation tool was tested in a company setting, comparing the estimated operation time values with the real ones, for a wide variety of parts of differing complexity. An average error of 14% for machining operation times was registered, which is quite satisfactory, as this time is the most impactful in terms of machining cost. However, there are still some problems regarding the accuracy in estimating finishing operation times.

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Optimization of turning parameters based on tool wear and machining cost for various parts

Cited by 13 publications

References 23 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

High-Performance Machining System: Process Optimization for a Cyber-Physical System – a Review

Build-Up an Economical Tool for Machining Operations Cost Estimation

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