Hybrid Taguchi-Gray Relation Analysis Method for Design of Metal Powder Injection-Molded Artificial Knee Joints with Optimal Powder Concentration and Volume Shrinkage

Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools

Ultra-precision turning is a crucial process in the manufacturing industry as it helps to produce parts with high dimensional accuracy, surface finish, and tolerance. The process is similar to traditional turning but is carried out under special circumstances to achieve greater precision and surface finish. The process can be applied to conventional structural materials, but the demand for machining hardened steels is increasing. The optimization of ultra-precision turning of AISI D2 using cubic boron nitride (CBN) tools is a crucial aspect in the field of high-quality machining. This study aims to evaluate the performance of the process and identify the optimal parameters that result in the best quality components while using a CBN tool's ultra-precision turning of AISI D2. Ultra-precision turning process factors such as cutting speed, feed, and depth of cut were experimentally investigated to enhance the response output, such as surface roughness and cutting force components. The full factorial experimental design was used for determining the process characteristics under different conditions, and experimental results were applied to search for the optimum response of machining performance. The optimization process was done by combining the hybrid genetic algorithm-response surface methodology (GA-RSM) and the Taguchi-grey relational analysis (GRA) statistical tools. These methods are useful in situations where the relationship between the input variables and the output responses is complex and non-linear. The results showed that a hybrid GA-RSM approach, combined with Taguchi-GRA statistical analysis, can effectively find optimal process parameters, leading to the best combination of surface roughness and cutting force. In hybrid Taguchi - GRA, the optimal cutting conditions were found to be a cutting speed of 175 m/min, a feed of 0.025 mm, and a depth of cut of 0.06 mm. The findings of this study provide valuable insights for the optimization of ultra-precision CBN turning operations, contribute to the development of precision manufacturing technology, and can be used as a reference for similar machining processes.

Moldflow Simulation and Characterization of Pure Copper Fabricated via Metal Injection Molding

Metal injection molding (MIM) is a representative near-net-shape manufacturing process that fabricates advanced geometrical components for automobile and device industries. As the mechanical performance of an MIM product is affected by green-part characteristics, this work investigated the green part of pure copper processed with MIM using the injection temperature of ~180 °C and injection pressure of ~5 MPa. A computational analysis based on the Moldflow program was proposed to simulate the effectivity of the process by evaluating the confidence of fill, quality prediction, and pressure drop of three distinctive regions in the green part. The results showed that the ring and edge regions of the green parts showed localized behavior, which was related to processing parameters including the position of the gate. A microstructural observation using scanning electron microscopy and a 3D X-ray revealed that both the surface and body matrix consisted of pores with some agglomeration of micro-pores on the edges and ring part, while any critical defects, such as a crack, were not found. A microhardness analysis showed that the three regions exhibited a reasonable uniformity with a slight difference in one specific part mainly due to the localized pore agglomeration. The simulation results showed a good agreement with the microstructures and microhardness data. Thus, the present results are useful for providing guidelines for the sound condition of MIM-treated pure copper with a complex shape.

Optimization of Ultrasonic Welding Process Parameters to Enhance Weld Strength of 3C Power Cases Using a Design of Experiments Approach

Ultrasonic welding (UW) is a joining of plastics through the use of heat generated from high-frequency mechanical motion, which is known as an efficient process in many applications, such as textile, packaging, or automotive. UW of thermoplastics has been widely employed in industry since no polymer degradations are found after UW. However, the trial-and-error approach is frequently used to study optimum UW process parameters for new 3C plastic power cases in current industry, resulting in random efforts, wasted time, or energy consumption. In this study, Taguchi methods are used to study optimum UW process parameters for obtaining high weld strength of a plastic power case. The most important control factor influencing the weld strength is amplitude, followed by weld pressure, hold time, and trigger position. The optimum UW process parameters are amplitude of 43.4 µm, weld pressure of 115 kPa, hold time of 0.4 s, and trigger position of 69.95 mm. Finally, the confirmation experiments are performed to verify the optimum process parameters obtained in this study.

Integrating Taguchi Method and Gray Relational Analysis for Auto Locks by Using Multiobjective Design in Computer-Aided Engineering

In automobiles, lock parts are matched with inserts, and this is a crucial quality standard for the dimensional accuracy of the molding. This study employed moldflow analysis to explore the influence of various injection molding process parameters on the warpage deformation. Deformation of the plastic part is caused by the nonuniform product temperature distribution in the manufacturing process. Furthermore, improper parameter design leads to substantial warpage and deformation. The Taguchi robust design method and gray correlation analysis were used to optimize the process parameters. Multiobjective quality analysis was performed for achieving a uniform temperature distribution and reducing the warpage deformation to obtain the optimal injection molding process parameters. Subsequently, three water cooling system designs—original cooling, U-shaped cooling, and conformal cooling—were tested to modify the temperature distribution and reduce the warpage. Taguchi gray correlation analysis revealed that the main influencing parameter was the mold temperature followed by the holding pressure. Moreover, the results indicated that the conformal cooling system improved the average temperature distribution.

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

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.

Application of Intelligent Modeling Method to Optimize the Multiple Quality Characteristics of the Injection Molding Process of Automobile Lock Parts

This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L₁₈ (2¹ × 3⁷) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.

Improving Cooling Performance of Injection Molding Tool with Conformal Cooling Channel by Adding Hybrid Fillers

Silicone rubber mold (SRM) is capable of reducing the cost and time in a new product development phase and has many applications for the pilot runs. Unfortunately, the SRM after injection molding has a poor cooling efficiency due to its low thermal conductivity. To improve the cooling efficiency, the thermal conductivity of the SRM was improved by adding fillers into the SRM. An optimal recipe for fabricating a high cooling efficiency low-pressure injection mold with conformal cooling channel fabricated by fused deposition modeling technology was proposed and implemented. This study proposes a recipe combining 52.6 wt.% aluminum powder, 5.3 wt.% graphite powder, and 42.1 wt.% liquid silicon rubber can be used to make SRM with excellent cooling efficiency. The price-performance ratio of this SRM made by the proposed recipe is around 55. The thermal conductivity of the SRM made by the proposed recipe can be increased by up to 77.6% compared with convention SRM. In addition, the actual cooling time of the injection molded product can be shortened up to 69.1% compared with the conventional SRM. The actual cooling time obtained by the experiment is in good agreement with the simulation results with the relative error rate about 20%.