Optimization of the milling process for aluminum honeycomb structures

Zarrouk, Tarik; Nouari, Mohammed; Salhi, Jamal‐Eddine; Makich, Hamid; Salhi, Mourad; Atlati, Samir; Salhi, Najim

doi:10.1007/s00170-021-08495-0

Cited by 17 publications

(4 citation statements)

References 25 publications

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“…Numerous product and process features may require optimization, depending on the manufacturers' production priorities and constraints. The common crucial features of industrial products are the surface roughness [184,185], dimensional accuracy [186,187], cutting temperature [188,189], and machining-induced residual stresses (RS) [190]. Surface roughness is an essential quality indicator, since it influences the mechanical characteristics of the final product, such as wear, corrosion, lubrication, thermal and electrical conductivity, and fatigue behavior [191,192].…”

Section: Surface Integritymentioning

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: Surface Integritymentioning

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

“…Numerous product and process features may require optimization depending on the manufacturers' production priorities and constraints. The common crucial features of industrial products are the surface roughness [146][147][148][149][150][151][152][153], dimensional accuracy [154][155][156], cutting temperature [157,158], and the machining-induced residual stresses (RS) [159][160][161]. Surface roughness is an essential quality indicator since it influences mechanical characteristics of the final product such as wear, corrosion, lubrication, thermal and electrical conductivity, and fatigue behavior [162,163].…”

Section: Special Process Optimization: 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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“…[20] Owing to these challenges, researchers have tried to optimize the machining process of honeycomb cores and composite materials. [12,13,21] Various parameters related to machining were analyzed in the literature. Jaafar [5] has experimented with the machining process and concluded that the feed rate is the most significant factor during machining.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis

Habib,

Khan,

Munir

et al. 2023

Small Methods

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Nomex Honeycomb core is the foundational building block for manufacturing aerospace composite components. Its usage requires machining honeycomb in complex aerodynamic profiles where the quality of the core is governed by accuracy and precision of cut profiles. The assessment of accuracy and precision is directly related to forces induced in the cutting tool and cutting efficiency. These two parameters form the basis of a multi‐objective function that this paper aims to optimize for the milling operation. The parameter of depth of cut considered in this paper has not been analyzed in a multi‐objective optimization study of the Nomex Honeycomb core previously. A Taguchi‐based array of Design of Experiments followed by Analysis of Variance and correlation analysis is utilised. The results indicate that the most significant factor is the feed rate, with a percentage contribution of 72% for the cutting forces and depth of cut, with a percentage contribution of 85% in the case of cutting efficiency. The two parameters are optimized using Desirability Function Analysis and Grey Relational Analysis. The results are validated through experimental runs with an error within 5% of the statistical predictions, with the percentage improvement in cutting forces for optimum runs as compared to the worst experimental run at 47.8%. The percentage improvement in cutting efficiency likewise is 11%.

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Optimization of the milling process for aluminum honeycomb structures

Cited by 17 publications

References 25 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

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis

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