Topology Optimization for the Design of Conformal Cooling System in Thin-wall Injection Molding Based on BEM

Li, Zheng; Wang, Xinyu; Gu, Junfeng; Ruan, Shilun; Shen, Changyu; Yan, Lyu; Zhao, Yang

doi:10.1007/s00170-017-0901-1

Cited by 70 publications

(33 citation statements)

References 41 publications

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“…Their study indicated that conformal cooling channels decrease the cycle time by about 30% in comparison to traditional cooling systems. Park et al [13] performed a method for maximizing the efficiency of conformal cooling channels manufactured with 3D printing technology. The research results showed that the cooling and cycle time can be reduced by more than 50%.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Efficiency in the cooling process is then achieved by designing an optimal channel system that maximizes the process of thermal exchange between the part and the coolant flow. On the other hand, the temperature distribution at the end of the filling phase depends fundamentally on the geometrical features of the part [13]. A complex and non-optimized part topology can cause shearing in the injected material and a lack of uniformity in the surface temperatures.…”

Section: Introductionmentioning

confidence: 99%

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A New Conformal Cooling Design Procedure for Injection Molding Based on Temperature Clusters and Multidimensional Discrete Models

et al. 2020

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This paper presents a new method for the automated design of the conformal cooling system for injection molding technology based on a discrete multidimensional model of the plastic part. The algorithm surpasses the current state of the art since it uses as input variables firstly the discrete map of temperatures of the melt plastic flow at the end of the filling phase, and secondly a set of geometrical parameters extracted from the discrete mesh together with technological and functional requirements of cooling in injection molds. In the first phase, the algorithm groups and classifies the discrete temperature of the nodes at the end of the filling phase in geometrical areas called temperature clusters. The topological and rheological information of the clusters along with the geometrical and manufacturing information of the surface mesh remains stored in a multidimensional discrete model of the plastic part. Taking advantage of using genetic evolutionary algorithms and by applying a physical model linked to the cluster specifications the proposed algorithm automatically designs and dimensions all the parameters required for the conformal cooling system. The method presented improves on any conventional cooling system design model since the cooling times obtained are analogous to the cooling times of analytical models, including boundary conditions and ideal solutions not exceeding 5% of relative error in the cases analyzed. The final quality of the plastic parts after the cooling phase meets the minimum criteria and requirements established by the injection industry. As an additional advantage the proposed algorithm allows the validation and dimensioning of the injection mold cooling system automatically, without requiring experienced mold designers with extensive skills in manual computing.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Conformal Cooling Design Procedure for Injection Molding Based on Temperature Clusters and Multidimensional Discrete Models

et al. 2020

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“…Liang [9] derived the optimization model to achieve the highest transfer efficiency of the cooling system. Li et al [10] used a topology optimization to design the conformal cooling system for the mold and simplified the analysis of the cooling process by the cycle averaged approach. The boundary element method was employed to find solutions and to calculate the sensitives.…”

Section: Introductionmentioning

confidence: 99%

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

2020

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In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q-network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

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“…is scenario has elicited research activities on ways to reduce the cooling time while ensuring acceptable product quality. Majority of these research activities have focused on conformal cooling channels within the molds to enhance and accelerate cooling [7][8][9][10][11][12][13][14][15][16][17][18]. In conformal cooling, the cooling channels are designed to conform to the shape of the mold cavity.…”

Section: Introductionmentioning

confidence: 99%

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

Mukras

2020

Advances in Polymer Technology

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This paper presents a framework for optimizing injection molding process parameters for minimum product cycle time subjected to constraints on the product defects. Two product defects, namely, volumetric shrinkage and warpage, as well as seven process parameters including injection speed, injection pressure, cooling time, packing pressure, mold temperature, packing time, and melt temperature, were considered. Injection molding experiments were conducted on specifically chosen test points and results were used to compute the volumetric shrinkage and warpage (at each test point). Thereafter, three relationships between the product cycle time (one relationship), the two product defects (two relationships), and the injection molding parameters were constructed using the kriging technique. An optimization problem to minimize the product cycle time (described by the first relationship) subject to constraints on the product defects (described by the latter two relationships) was then formulated. A combination set of points between the lower and upper extreme values of acceptable product defect was generated to serve as constraints for the two product defects. The optimization problem was then solved using the Fmincon function, available in the Matlab optimization toolbox. A plot of the optimization results revealed an appreciable tradeoff between the cycle time and the two product defects. To validate the optimization, an additional injection molding experiment was conducted for one of the optimization results. Results from the additional experiment showed reasonably close agreement with simulation optimization results differing in the cycle time, the warpage and volumetric shrinkage by 6.7%, 3.2%, and 8%, respectively.

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Topology Optimization for the Design of Conformal Cooling System in Thin-wall Injection Molding Based on BEM

Cited by 70 publications

References 41 publications

A New Conformal Cooling Design Procedure for Injection Molding Based on Temperature Clusters and Multidimensional Discrete Models

A New Conformal Cooling Design Procedure for Injection Molding Based on Temperature Clusters and Multidimensional Discrete Models

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

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