This paper emphasizes on the development of a combined study of surface roughness for modeling and optimization of cutting parameters for keyway milling operation of C40 steel under wet condition. Spindle speed, feed, and depth of cut are considered as input parameters and surface roughness (Ra) is selected as output parameter. Surface roughness model is developed by both artificial neural networks (ANN) and response surface methodology (RSM). ANOVA analysis is performed to determine the effect of process parameters on the response. Back-propagation algorithm based on Levenberg-Marquardt (LM) and gradient descent (GDX) methods is used separately to train the neural network and results obtained from the two methods are compared. It is found that network trained by the LM algorithm gives better result. ANN model (trained by the LM algorithm) is coupled with genetic algorithm (GA) and RS model is further interfaced with the GA and particle swarm optimization (PSO) to optimize the cutting conditions that lead to minimum surface roughness. It is found that RSM coupled with PSO gives better result and the result is validated by confirmation test. Good agreement is observed between the predicted Ra value and experimental Ra value for RSM-PSO technique.

Modeling and optimization of surface roughness in keyway milling using ANN, genetic algorithm, and particle swarm optimization

The quality control of plastic products is an essential aspect of the plastic injection molding (PIM) process. However, the warpage and shrinkage deformations continue to exist because the PIM process is easily interfered with by several related or independent process parameters. Thus, great efforts have been devoted to optimizing process parameters to minimize the warpage and shrinkage deformations of products during the last decades. In this review, we begin by introducing the manufacturing process in PIM and the cause of warpage and shrinkage deformations, followed by the mechanism about how process parameters, like mold temperature, melt temperature, injection rate, injection pressure, holding pressure, holding and cooling duration, affect those defects. Then, we summarize the recent progress of the design of experiments and four advanced methods (artificial neural networks, genetic algorithm, response surface methodology, and Kriging model) on optimizing process parameters to minimize the warpage and shrinkage deformations. In the end, future perspectives of quality control in injection molding machines are discussed. 

/pdf/recent-progress-in-minimizing-the-warpage-and-shrinkage-vq3c5fem.pdf

Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

Multi-objective optimization of the fiber-reinforced composite injection molding process using Taguchi method, RSM, and NSGA-II

Optimization of high-strength self-consolidating concrete mix design using an improved Taguchi optimization method

Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

https://downloads.hindawi.com/journals/apt/2020/7023616.pdf

Intelligent Injection Molding on Sensing, Optimization, and Control

This paper proposes a systematic optimization model of process parameters in plastic injection molding (PIM). Firstly, the Taguchi method is employed for experimentation and data analysis, in which the quality characteristics for the plastic injection product are length and warpage. The control factors for the process are melt temperature, injection velocity, packing pressure, packing time, and cooling time. Moreover, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are used to obtain a combination of parameter settings. Experimental data are set for the response surface methodology (RSM) in order to analyze and create two quality predictors and two S/N ratio predictors. The two quality predictors are associated with genetic algorithms (GA) to search for an optimal combination of process parameters that meets multiple-objective quality characteristics. Finally, four predictors are combined with the hybrid GA-PSO to find the final optimal combination of process parameters. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in length and warpage, but also reduces the costs of and time spent in the PIM process.

Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

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Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO