A dual response surface optimization methodology for achieving uniform coating thickness in powder coating process

Abstract

The powder coating is an economic, technologically superior and environment friendly painting technique compared with other conventional painting methods. However large variation in coating thickness can reduce the attractiveness of powder coated products. The coating thickness variation can also adversely affect the surface appearance and corrosion resistivity of the product. This can eventually lead to customer dissatisfaction and loss of market share. In this paper, the author discusses a dual response surface optimization methodology to minimize the thickness variation around the target value of powder coated industrial enclosures. The industrial enclosures are cabinets used for mounting the electrical and electronic equipment. The proposed methodology consists of establishing the relationship between the coating thickness & the powder coating process parameters and developing models for the mean and variance of coating thickness. Then the powder coating process is optimized by minimizing the standard deviation of coating thickness subject to the constraint that the thickness mean would be very close to the target. The study resulted in achieving a coating thickness mean of 80.0199 microns for industrial enclosures, which is very close to the target value of 80 microns. A comparison of the results of the proposed approach with that of existing methodologies showed that the suggested method is equally good or even better than the existing methodologies. The result of the study is also validated with a new batch of industrial enclosures.

Multi-performance optimization for AWJ drilling process in cutting of ceramic tile: BBD with EOBL-GOA algorithm

“…A Box–Behnken design, the most prevalent RSM design, was employed to assess their influence on the two dependent variables or responses (Berkani et al. , 2015; John, 2015). Low, middle and high levels of each variable are labelled 1, 0 and +1, respectively, in Table 1.…”

“…The main reason to select these parameters was that the majority of manufacturing industries place an emphasis on product quality. A Box-Behnken design, the most prevalent RSM design, was employed to assess their influence on the two dependent variables or responses (Berkani et al, 2015;John, 2015). Low, middle and high levels of each variable are labelled 1, 0 and þ1, respectively, in Table 1.…”

“…Results showed that the KHA metaheuristic approach required less time and space and produced a smaller inverted generational distance. In order to maximise material removal rate (MRR) and hardness while minimizing surface roughness, John (2015) employs a multi-objective teaching–learning-based optimization (TLBO) of AWJ cutting brass (C360).…”

Sequence of Selected Quality Management Tools to Analyze Qualitive Problems of Products

“…In powder coating, the basic types of coating materials are thermoplastic and thermoset coatings. Thermoplastic coatings fuse together with the supply of the right amount of heat and their chemical components remain unchanged after cooling and merging (John, 2015). In the case of thermosetting coatings after melting, chemical reactions occur, in which the cured thermosetting coating has a chemical structure than before the melting process (Naderi, Attar, Moayed, 2004).…”

“…oven temperature, curing time, and conductivity, as well as powder yield, have a significant impact on the quality of the paint coating. After selecting the optimal values for the given parameters, it is possible to obtain an average coating thickness (about 80 microns) (Adams, 1989;Boer, Petruta, 2014;John, 2015;Kramer, 1993). The coatings, in particular thermally cured, phenolic or epoxy phenolic, provide corrosion resistance, and the heat-curing coating to protect against corrosion must be stable at elevated temperatures of up to 220°C.…”