Manual spray painting process optimization using Taguchi robust design

Abstract

Purpose

Researchers heavily investigated the use of industrial robots to enhance the quality of spray-painted surfaces. Despite its advantages, automating process is not always economically feasible. The manual process, on the other hand, is cheaper, but its quality is prone to the mental and physical conditions of the worker making it difficult to operate consistently. This research proposes a mathematical cost model that integrates human factors in determining optimal process settings.

Design/methodology/approach

Taguchi's robust design is used to investigate the effect of fatigue, stability of worker's hand and speed on paint consumption, surface quality, and processing time. A crossed array experimental design is deployed. Regression analysis is then used to model response variables and formulate cost model, followed by a multi-response optimization.

Findings

Results reveal that noise factors have a significant influence on painting quality, time, and cost of the painted surface. As a result, a noise management strategy should be implemented to reduce their impact and obtain better quality and productivity results. The cost model can be used to determine optimal setting for different applications by product and by industry.

Originality/value

Hardly any research considered the influence of human factors. Most focused on robot trajectory and its effect on paint uniformity. In proposed research, both cost and quality are integrated into a single objective. Quality is measured in terms of uniformity, smoothness, and surface defects. The interaction between trajectory and flow rate is investigated here for the first time. A unique approach integrating quality management, statistical analysis, and optimization is used.

Citation

Almansoori, N., Aldulaijan, S., Althani, S., Hassan, N.M., Ndiaye, M. and Awad, M. (2021), "Manual spray painting process optimization using Taguchi robust design", International Journal of Quality & Reliability Management, Vol. 38 No. 1, pp. 46-67. https://doi.org/10.1108/IJQRM-07-2019-0248