Parameter optimization of automated fiber placement based on CCD response surface method

Abstract

Defects in the automatic fiber placement (AFP) directly affect the prepreg bonding strength, resulting in a less strong product than expected. Improper process parameter selection is one of the major factors contributing to layup defects. However, there is a significant degree of uncertainty associated with the parameters that affect the lay-up quality and currently few studies that use response surfaces to consider the interaction of these parameters. In general, it is necessary to obtain a more accurate combination of process parameters through experiment, which is time-consuming and consumable. Moreover, it is difficult to investigate the interrelation between process parameters. Therefore, this paper presents a method of placement parameter optimization based on lap shear strength (LSS) to achieve high-quality and high-strength AFP process control. Firstly, this article establishes a comprehensive quality evaluation system by analyzing the causes of defects in AFP process. Secondly, the central composite design of experiments (CCD) response surface methodology was used to effectively optimize the process parameters and to understand the interactions between the various factors. A prediction model with quadratic polynomial regression equations for four process parameters and laydown quality was developed. Based on the regression equations, the process parameters with optimal lay-up quality were obtained. Then, using 30 sets of process parameters from the response surface experiment, lap samples were made for tensile testing. The lap shear strength of tows under various process conditions was studied, and the experimental results were consistent with the response surface analysis results. Finally, by analyzing the quality evaluation curve and load-time curve, it was found that the trend of AFP quality evaluation score and LSS was practically the same. Data analysis shows that placement using optimized parameters corresponding to higher scoring samples has higher lap shear strength and fewer defects than placement of samples with low quality scores. Therefore, the quality evaluation system of lay-up quality proposed in this paper is an effective and reliable method for optimizing the parameters of AFP process.

Graphical Abstract

Keywords

Automated fiber placement central composite design response surface method layup quality process parameters optimization

Get full access to this article

View all access options for this article.

References

Mathieu

Charfi

Chatelain

, et al. Effect of graphene additive on cutting forces and temperature during the trimming process of CFRP. Int J Adv Manuf Technol 2022; 122: 3969–3981. DOI: 10.1007/s00170-022-10150-1.

Brasington

Sacco

Halbritter

, et al. Automated fiber placement: a review of history, current technologies, and future paths forward. Composites Part C 2021; 6: 100182. DOI: 10.1016/j.jcomc.2021.100182.

Rudberg

Cemenska

Sherrard

. A process for delivering extreme AFP head reliability. SAE Int J Adv Curr Pract Mobil 2019; 1(2): 333–342. DOI: 10.4271/2019-01-1349.

Labeas

Watiti

Katsiropoulos

. Thermomechanical simulation of infrared heating diaphragm forming process for thermoplastic parts. J Thermoplast Compos Mater 2008; 21: 353–370. DOI: 10.1177/0892705708089480.

Qureshi

Swait

Scaife

, et al. In situ consolidation of thermoplastic prepreg tape using automated tape placement technology: potential and possibilities. Composites Part B 2014; 66: 255–267. DOI: 10.1016/j.compositesb.2014.05.025.

Wang

Mahapatra

Belnoue

, et al. Understanding tack behaviour during prepreg-based composites’ processing. Composites Part A 2023; 164: 107284. DOI: 10.1016/j.compositesa.2022.107284.

Khan

Mitschang

Schledjewski

. Parametric study on processing parameters and resulting part quality through thermoplastic tape placement process. J Compos Mater 2013; 47: 485–499. DOI: 10.1177/0021998312441810.

Pan

Yang

, et al. Process-dependent wrinkle formation for steered tow during automated fiber placement: modeling and experimental verification. Thin-Walled Struct 2022; 180: 109928. DOI: 10.1016/j.tws.2022.109928.

Mattia

Laura

Giuseppe

, et al. Heater power control for multi-material, variable speed Automated Fibre Placement. Composites Part A 2017; 101: 408–421. DOI: 10.1016/j.compositesa.2017.06.015.

10.

Venkatesan

Velu

Vaheed

, et al. Effect of process parameters on polyamide-6 carbon fibre prepreg laminated by IR-assisted automated fibre placement. Int J Adv Manuf Technol 2020; 108: 1275–1284. DOI: 10.1007/s00170-020-05230-z.

11.

Venkatesan

Zulkifli

Silva

. Effects of processing parameters of infrared-based automated fiber placement on mechanical performance of carbon fiber-reinforced thermoplastic composite. Compos Struct 2023; 309: 116725. DOI: 10.1016/j.compstruct.2023.116725.

12.

Miao

Dai

, et al. Effect of consolidation force on interlaminar shear strength of CF/PEEK laminates manufactured by laser-assisted forming. Compos Struct 2021; 266: 113779. DOI: 10.1016/j.compstruct.2021.113779.

13.

Oromiehie

Gain

Prusty

. Processing parameter optimisation for automated fibre placement (AFP) manufactured thermoplastic composites. Compos Struct 2021; 272: 114223. DOI: 10.1016/j.compstruct.2021.114223.

14.

Nima

Mehdi

. Effect of compaction roller on layup quality and defects formation in automated fiber placement. J Reinforc Plast Compos 2019; 39: 0731684419868845. DOI: 10.1177/0731684419868845.

15.

Huang

Sun

Zhang

, et al. Processing parameters analysis and optimization for automated tape laying using response surface methodology. Compos Sci and Eng 2013; 5: 37–44 in chinese.

16.

Huang

Xiao

Zhao

, et al. Process parameter optimization for automatic fiber placement. Fib Glass 2015; 6: 20–26 in chinese.

17.

Nguyen

Sun

Tretiak

, et al. Automatic process control of an automated fibre placement machine. Compos Part A 2023; 168: 107465. DOI: 10.1016/j.compositesa.2023.107465.

18.

Beakou

Cano

Cam

JBL

, et al. Modelling slit tape buckling during automated prepreg manufacturing: a local approach. Compos Struct 2011; 93: 10. DOI: 10.1016/j.compstruct.2011.04.030.

19.

Nima

Mehdi

. An experimental and simulative study on the defects appeared during tow steering in automated fiber placement. Composites Part A 2018; 113: 122–131. DOI: 10.1016/j.compositesa.2018.07.031.

20.

Forghani

Hickmott

Bedayat

, et al. Simulating prepreg tack in AFP process. In: American society for composites, West Lafayette, IN, 23–25 October 2017.

21.

Aized

Shirinzadeh

. Robotic fiber placement process analysis and optimization using response surface method. Int J Adv Manuf Technol 2011; 55: 393–404. DOI: 10.1007/s00170-010-3028-1.

22.

David

. Progress in automated ply inspection of AFP layups. Reinforc Plast 2015; 59(5): 242–245. DOI: 10.1016/j.repl.2015.05.002.

23.

Liu

Lei

. Experimental study on the cytocompatibility of milling surface of poly-ether-ether-ketone (PEEK). Int J Adv Manuf Technol 2023; 126: 1747–1757. DOI: 10.1007/s00170-023-11223-5.

24.

Arao

Koyanagi

Utsunomiya

, et al. Effect of ply angle misalignment on out-of-plane deformation of symmetrical cross-ply CFRP laminates: accuracy of the ply angle alignment. Compos Struct 2011; 93: 1225–1230. DOI: 10.1016/j.compstruct.2010.10.019.