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This research aimed at lowering the non-volatile matter (NVM) of long oil alkyd (LOA) resin to meet the resin requirements in producing Hi-gloss paints. The research also aimed to assist Hi-gloss paint manufacturers who require solid resin content below 70%.

The research utilised both the document review and experimental methods. The experimental method, which involved the blending process, was used in lowering the NVM of LOA resin, whereby eight experiments were conducted as reputations to obtain accurate data and results. A factorial design was used in the design of the experiment (DOE), where three factors were required to be evaluated: specific gravity, viscosity and NVM and two levels were required: weight of LOA and white spirit.

The NVM of LOA resin was successfully lowered, and different shades of paint were produced for reliability testing. The quality parameters of Hi-gloss paints that used the modified LOA were all found within the company's specifications compared to those with unmodified LOA, where some parameters were either above or below the specifications. However, all samples with the NVM of LOA used below 50% were found to be poor in terms of quality during the reliability testing. So, if the company had used the modified LOA resin in the production of Hi-gloss paints, it would have potentially saved 18,925.2 kg and 9,446.4 kg, respectively, of LOA just in producing Hi-gloss black and white paints only. Moreover, the cost of 1 kg of LOA is $2.3, which is equal to Tanzanian Shillings (Tshs) 5,290. The company would have potentially saved Tshs 100.114 million Tshs and Tshs 49.97 million Tshs in producing Hi-gloss black and white paints, respectively, making a total of Tshs 150.08 million per year. This was counted as a loss during that year because they did not use LOA resin with 50% NVM in production.

This study focused on LOA resin used in producing Hi-gloss paints. The study also did not consider other categories of alkyd resins used in producing paints and varnishes e.g. epoxy resin and short and medium oil alkyd resins.

This is probably the first alkyd resin improvement study conducted using white spirit as Tanzania's main agent or material. The study utilised vegetable oils such as coconut, sunflower and tobacco seeds.

The purposes of this paper are to review the progress of and conclude the trend for paint thickness simulation for painting robot trajectory planning.

This paper compares the explicit function-based method and computational fluid dynamics (CFD)-based method used for paint thickness simulation. Previous research is considered, and conclusions with the outlook are drawn.

The CFD-based paint deposition simulation is the trend for paint thickness simulation for painting robot trajectory planning. However, the calculation of paint thickness resulting from dynamically painting complex surface remains to be researched, which needs to build an appropriate CFD model, study approaches to dynamic painting simulation and investigate the simulation with continuously changing painting parameters.

This paper illustrates that the CFD-based method is the trend for the paint thickness simulation for painting robot trajectory planning. Current studies have been analyzed, and techniques of CFD modeling have also been summarized, which is vital for future study.

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.

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.

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.

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.

This paper aims to present a new trajectory optimization approach targeting spray painting applications that satisfies the paint thickness requirements of complex-free surfaces.

In this paper, a new trajectory generation approach is developed to optimize the transitional segments at the junction of adjacent patches for straight line, convex arc and concave arc combinations based on different angles between normal vectors of patches. In addition, the paint parameters including the paint gun velocity, spray height and the distance between adjacent trajectories have been determined in the generation approach. Then a thickness distribution model is established to simulate the effectiveness of trajectory planning.

The developed approach was applied to a complex-free surface of various curvatures, and the analysis results of the trajectory optimization show that adopting different transitional segment according to the angle between normal vectors can obtain the optimal trajectory. Based on the simulation and experimental validation results, the proposed approach is effective at improving paint thickness uniformity, and the obtained results are consistent with the simulation results, meaning that the simulation model can be used to predict the actual paint performance.

This paper discusses a new trajectory generation approach to decrease the thickness error values to satisfy spray paint requirements. According to the successfully performed simulation and experimental results, the approach is useful and practical in overcoming the challenge of improving the paint thickness quality on complex-free surface.

Both paint manufacturers and paint users are continually striving to improve both paint materials and the application of those materials. A tool employed by these companies to achieve the best quality in paint finish is the painting laboratory. It is a facility which enables the companies to test materials, establish optimum spray parameters, evaluate different physical arrangements in spray booths, and so on.

The purpose of this study is to determine required paint and silicone proportions, their mixing rate and duration to eliminate the pattern problems caused by the misuse of silicone polymers in hammertone paints. Results of the findings aim at preventing raw material, manufacturing time and labour losses in hammertone paint production.

A paint formulation produced commercially was used. The effects of silicone proportion and mixing rate and paint and pigment density on the pattern formation within the paints, to which the spray painting method was applied, were investigated. Brightness controls of the produced paints were done, and the effect of the additive polymer on brightness was investigated. Photographs of paints for each practice were taken.

The most suitable silicone proportion and mixing conditions for the silver grey paints, in which Al non-leafing pigments were used, were calculated as 0.034-0.036 g/kg-paint and 15 s at 200 rpm, respectively. It was observed that the increase in pigment density increased required silicone proportion for the desired pattern. It was determined that mixing rate and duration were very significant in pattern formation, and that silicone decomposed and the pattern was disrupted in long-continued mixings. In addition, excess silicone use disrupted the pattern and decreased the paint brightness.

This research was conducted for hammertone paints, which are industrial and self-patterned aluminium silvered decorative paints. The results can be used for hammertone paint production.

The results will enable manufacturers to produce hammertone paints more economically. Results can be used in the paint industry to produce such paint.

It is crucial to produce high-quality products using less raw materials in today’s industry, where the amount of raw materials is decreasing, and economical and reliable production is becoming increasingly important. When environmental impacts in the solvent-based paint industry are taken into consideration, paint production in shorter times is important for both workers’ and environmental health. As a result of this study, hammertone paints will be produced more economically by appropriate production conditions yet without any energy, raw material and time loss.

Paint path planning for industrial robots is critical for uniform paint distribution, process cycle time and material waste, etc. However, paint path planning is still a costly and time‐consuming process. Currently paint path planning has always caused a bottle‐neck for manufacturing automation because typical manual teaching methods are tedious, error‐prone and skill‐dependent. Hence, it is essential to develop automated tool path‐planning methods to replace manual paint path planning. The purpose of this paper is to review the existing automated tool path‐planning methods, and investigate their advantages and disadvantages.

The approach takes the form of a review of automated tool path‐planning methods, to investigate the advantages and disadvantages of the current technologies.

Paint path planning is a very complicated task considering complex parts, paint process requirements and complicated spraying tools. There are some research and development efforts in this area. Based on the review of the methods used for paint path planning and simulation, the paper concludes that: the tessellated CAD model formats have many advantages in paint path planning and paint deposition simulation. However, the tessellated CAD model formats lack edge and connection information. Hence, it may not be suitable for some applications requiring edge following, such as welding. For the spray gun model, more complicated models, such as 2D models, should be used for both path planning and paint distribution simulation. Paint path generation methods should be able to generate a paint path for complex automotive parts without assumptions, such as presupposing a part with a continuous surface.

The paper makes possible automated path generation for spray‐painting process using industrial robots such that the path‐planning time can be reduced, the product quality improved, etc.

The paper provides a useful review of current paint path‐planning methodologies based on the CAD models of parts.

This study aims to develop a manufacturing process management system which aims to benefit the excavation, collection and search of the mentors’ experience and knowledge. The coating painting industry is a typical small and medium-sized traditional industry and usually depends on master's experience to solve the manufacturing problem.

Based on the characteristics and manufacturing process of the architectural coating industry, this study develops a practical knowledge management system (KMS) with two stages. The first stage collects and analyzes manufacturing process data, and the second stage constructs the KMS of the manufacturing process.

This manufacturing process KMS can accumulate and share the operators’ experience and knowledge systematically; this KMS not only improves the apprentices’ skill and problem-solving abilities but also enhances the enterprise’s overall product quality, undoubtedly.

This manufacturing process KMS was designed to be implemented in architectural coating industry which is defined as a “3K” industry. Such a specific manufacturing process KMS is not a generalized principles system which is hard to apply to other industries because the different industries own different explicit and tacit knowledge which cannot be captured in this study simultaneously. Further research will try to design a more complete and integrated KMS for industry and combine it with other approaches such as TRIZ (Russian acronym for the theory of inventive problem solving), patent database and problem-solving tools.

The developments of this manufacturing process KMS software was based on systematic analysis and design. Using the architectural coating industry as an example, this system externalized the cognitive apprenticeship and developed a KMS based on the characteristics of the manufacturing process to preserve and share the valuable manufacturing process knowledge.

The paper extends knowledge management’s contribution to externalize the cognitive apprenticeship and develop a KMS. The adoption of this manufacturing process KMS enables employees to improve their techniques, skills and problem-solving abilities and enables companies to enhance the overall product quality.

This paper aims to achieve automatically surface segmentation for painting different kinds of aircraft efficiently considering the demands of painting robot.

This project creatively proposed one method that accepts point cloud, outputs several blocks, each of which can be handled by ABB IRB 5500 in one station. Parallel PointNet (PPN) is proposed in this paper for better handling six dimensional aircraft data including every point normal. Through semantic segmentation of PPN, each surface has its own identity information indicating which part this surface belongs to. Then clustering considering constraints is applied to complete surface segmentation with identity information. To guarantee segmentation paintable and improve painting efficiency, different dexterous workspaces of IRB 5500 corresponding to different postures have been analyzed carefully.

The experiments confirm the effectiveness of the proposed surface segmentation method for painting different types of aircraft by IRB 5500. For semantic segmentation on aircraft data with point normal, PPN has higher precision than PointNet. In addition, the whole algorithm can efficiently segment one complex aircraft into qualified blocks, each of which has its own identity information, can be painted by IRB 5500 in one station and has fewer edges with other blocks.

As the provided experiments indicate, the proposed method can segment one aircraft into qualified blocks automatically, which highly improves the efficiency in aircraft painting compared with traditional approaches. Moreover, the proposed method is able to provide identity information of each block, which is necessary for application of different paint parameters and different paint materials. In addition, final segmentation results by the proposed method behaves better than k-means cluster on variance of normal vector distance.

Inspired by semantic segmentation of 3 D point cloud, some improvements based on PointNet have been proposed for better handling segmentation of 6 D point cloud. By introducing normal vectors, semantic segmentation could be accomplished precisely for close points with opposite normal, such as wing upper and lower surfaces. Combining deep learning skills with traditional methods, the proposed method is proved to behave much better for surface segmentation task in aircraft painting.

This work aims to establish the relationship between painting art and sustainability, which allows for highlighting implications likely to improve sustainability for humanity's welfare.

To achieve this objective, painting art is measured by a composite index aggregating the quantity and quality represented by the market value. As for sustainable development, it is represented by a composite index comprising three variables: the climate change performance index (ecological dimension), the wage index reflecting distributive justice (social dimension) and the gross domestic product (economic dimension). The composite indices were determined through adjusted data envelopment analysis. In addition, two other methods are used in this work: correlation analysis and a neural network method. These methods are applied to data from 2007 to 2021 across the world.

The correlation method highlighted a perfect positive correlation between painting art and sustainability. As for the neural network method, it revealed that the quality of painting has the greatest impact on sustainability. The neural network method also showed that the most positively impacted variable of sustainability by painting art is the social variable, with a pseudo-probability of 0.90.

The relationship between painting art and sustainability is underexplored, in particular in terms of statistical analysis. Therefore, this research intends to fill this gap. Moreover, analysis of the relationship between both using composite indices computed via an original method (adjusted data envelopment analysis) and a neural network method is nonexistent, which constitutes the novelty of this work.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0006