Process optimization of high machining efficiency and low surface defects for HSD milling UD-CF/PEEK with limited thermal effect

Liu, Lei; Qu, Da; Cao, Huajun; Huang, Xinyan; Song, Yang; Kang, Xinzhen

doi:10.1016/j.jmapro.2022.02.040

Cited by 17 publications

(11 citation statements)

References 41 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%

“…As presented in [203], an optimization procedure based on the combination of a data-driven model, Support Vector Regression (SVR), and improved PSO was developed to determine the optimal process parameters and ensure that the tensile residual stress on the product surfaces complied with the design requirements. Process optimization can also be developed for specific material and cutting conditions such as machining carbon fiber reinforced composites (CF/PEEK) under dry cutting conditions, in which controlling the surface defects is highly crucial [188]. To address this problem, a process optimization method to improve the machining efficiency and reduce the surface defects was developed for the high-speed dry milling of CF/PEEK material, based on an analysis of the thermal impact of the cutting process on the machined surfaces [188].…”

Section: Surface Integritymentioning

confidence: 99%

See 1 more Smart Citation

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%

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

“…Cao et al [93] measured the CF/PEEK milling temperature using IR thermal camera and the optimal cutting speed window was obtained through effective temperature control. Liu et al [94] optimized the high speed milling parameter of CF/PEEK through a hybrid method integrating Artificial neural network (ANN), None-dominated Sorting Genetic Algorithm -II (NSGA-II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The microstructural matrix smearing damage has been effectively eliminated using optimal milling parameters, owing to effective prediction and control of machining temperature.…”

Section: Millingmentioning

confidence: 99%

Process characteristics, damage mechanisms and challenges in machining of fibre reinforced thermoplastic polymer (FRTP) composites: A review

Luo

Catalanotti

et al. 2023

Preprint

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Fibre reinforced thermoplastic polymer (FRTP) composites have been used for a wide range of engineering applications (e.g. in transport, construction, energy, etc) due to their excellent mechanical properties and ease of repair and recycling. In recent years, FRTP is increasingly deployed as an alternative to conventional thermoset carbon fibre reinforced epoxy (CF/epoxy) composites, for the purpose of reducing the carbon footprint and contributing to a sustainable manufacturing agenda. Machining of FRTP remains an indispensable process to achieve rapid parts assembly whilst meeting stringent geometric tolerances. However, due to the heterogeneous structure and high thermal sensitivity of FRTP, a range of machining-induced damages (such as matrix smearing, thermal degradation, delamination, burr and surface cavity) often occur, leading to concerns on machined parts quality and reliability. To date, composite machining studies have been mostly focused on conventional thermoset CF/epoxy and there is a lack of an up-to-date, in-depth review of the latest advancement concerning the machining of FRTP. This paper provides a state-of-the-art overview on the recent developments in FRTP machining over the past decade, with a particular emphasis on machining characteristics, damage mechanisms as well as the challenges facing such manufacturing process. The purpose is to present the composite manufacturing community with a timely update, which may guide and inspire further research and development for future FRTP manufacturing.

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“…Here, the thrust force, hole-exit delamination damage factor Fda and MRR are the three optimization objectives considered in this study and multi-objective optimization is conducted to identify a set of solutions to achieve the global optimization (trade off) between hole quality and the machining efficiency. Additionally, Fda and Tmax should be constrained to ensure the delamination and thermal damage are within acceptable range [26,31]. The objective functions, constraints and design spaces for the composite drilling optimization can be expressed as follows:…”

Section: Delamination Factor Analysismentioning

confidence: 99%

“…The optimal solutions have effectively improved the closeness coefficient and the prediction accuracy was verified by experiment. Liu et al [31] deployed a hybrid approach combining GA-BP neural network, NSGA-II and TOPSIS for multi-objective optimization of high speed CF/PEEK milling. The prediction accuracy was over 90% and the optimal milling parameters selected from the Pareto front have effectively eliminated the surface defects such as fibre fracture and matrix smearing.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of thermoplastic CF/PEKK drilling through a hybrid method integrating NSGA-II and TOPSIS: an approach towards sustainable manufacturing

Zhang

Luo

et al. 2022

Preprint

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Carbon-fibre-reinforced-polyetherketonketone (CF/PEKK) has attracted increasing interest in the aviation industry due to its self-healing/recycling properties. However, its machining performance is not well understood and there is a lack of optimization study for minimizing its hole damage and improving the production efficiency. Here, we report the first multi-objective optimization study for CF/PEKK drilling. A hybrid optimization algorithm integrating NSGA-II and TOPSIS is deployed to obtain the Pareto solutions and rank the multiple solutions based on closeness to ideal solutions. To highlight the impact of different matrix properties on the optimization outcome, comparative study with conventional thermoset CF/epoxy was carried out for the first time. Experimental validation shows the proposed method can achieve 91.5-95.7% prediction accuracy and the Pareto solutions effectively controlled the delamination and thermal damage within permissible tolerance. The vastly different optimal drilling parameters identified for CF/PEKK and CF/epoxy is attributed to the thermoplastic nature of CF/PEKK with unique thermal/mechanical interaction characteristics.

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Process optimization of high machining efficiency and low surface defects for HSD milling UD-CF/PEEK with limited thermal effect

Cited by 17 publications

References 41 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

Process characteristics, damage mechanisms and challenges in machining of fibre reinforced thermoplastic polymer (FRTP) composites: A review

Multi-objective optimization of thermoplastic CF/PEKK drilling through a hybrid method integrating NSGA-II and TOPSIS: an approach towards sustainable manufacturing

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