Property modification and process parameter optimization design of polylactic acid composite materials Part II: application of response surface methodology and multi-objective particle swarm optimization in the processing of polylactic acid composite fiber. Issue 7 (May 2015)

• Record Type: Journal Article
• Authors: Kuo, Chung-Feng Jeffrey; Lan, Wei Lun; Chen, Shih Hsiung; Chen, Ciou-Yin
• Is Part Of: Textile research journal. Volume 85:Issue 7(2015)
• Page Start: 687
• Page End: 700
• Publication Date: 2015-05
• Subjects: polylactic acid composite -- melt spinning -- response surface method -- particle swarm optimization; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/0040517514555793
• Languages: English
• ISSNs: 0040-5175

Abstract

Two biodegradable polymers (polylactic acid (PLA) and polybutylene succinate (PBS)) were mixed with ultraviolet absorbers and hindered amine light stabilizers to improve the durability of this composite fiber outdoors. Filament was obtained from this composite material under the optimum conditions by using melt spinning to investigate the effects of spinning temperature, spinneret rotation speed and take-up roller speed in the fiber spinning process on the quality characteristics of newly spun threads. The response surface method was used to build a regression model for different quality characteristics. Moreover, multi-objective particle swarm optimization was applied to optimize the model of these quality characteristics and parameter limits. According to experimental validation, the composite material of PLA/PBS was better than the composite material of PLA/polycaprolactone. The denier of the composite fiber obtained under the optimized conditions was 40.07 d, with an increase of 5.3% compared to the denier before optimization. The fiber strength after optimization was 1.69 (g/d), with an increase of 42%. The breaking elongation was 176.04% and increased 27% after optimization. The prediction error rate of an optimum regression model showed that the model has good reproducibility.