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TECHNICAL PAPERS

Process-Driven Input Profiling for Plastics Processing

[+] Author and Article Information
Shaoqiang Dong, Chunsheng E, Bingfeng Fan, Kourosh Danai

Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA

David O. Kazmer

Department of Plastics Engineering, University of Massachusetts, Lowell, MA

J. Manuf. Sci. Eng 129(4), 802-809 (Jan 25, 2007) (8 pages) doi:10.1115/1.2738094 History: Received April 07, 2005; Revised January 25, 2007

Most plastic processing set points are variables that need to be defined for each sample point of the cycle. However, in the absence of on-line measures of part quality, the set points cannot be defined by feedback and need to be prescribed a priori for the entire cycle. In conventional practice, the shape of each set-point profile is defined either heuristically, according to qualitative experience, or mechanistically, to enforce a predefined trajectory for a simulated internal process state that is used as a surrogate measure of part quality (e.g., the velocity profile defined to dictate a constant melt front velocity). The purpose of this study is twofold: (i) to evaluate the efficacy of using a single internal state as the surrogate of part quality, and (ii) to explore the feasibility of devising a multivariate profiling approach, where indices of multiple process states act as surrogates of part quality. For this study, an injection-compression molding process used for production of digital video disks was considered as the development domain, and a pseudo-optimal cycle of the process was found by reinforcement learning to provide a basis for evaluating the ideal behavior of the process states. Compared to conventional molding, the results indicate that the asymmetric process capability index, CPK, was increased by 50% with velocity profile optimization and to 120% with both velocity profile and pressure profile optimization. Two general conclusions result. First, velocity and pressure profiling provide important degrees of freedom for optimizing process control and maximizing part quality. Second, estimators for unobservable process states, in this case birefringence and warpage, can be used to develop different machine profiles to selectively trade off multiple quality attributes according to user preferences.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 2

Simulated values of birefringence and warpage during the adaptation of the velocity profile by reinforcement learning

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Figure 5

Velocity profile to maintain a constant melt front velocity compared to the one from reinforcement learning

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Figure 6

Pressure profile by SP to enhance PB, PW, and PC with PR

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Figure 7

Process capability of birefringence and warpage for four velocity profiles (SP refers to profiles generated by sequential programing)

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Figure 8

Process capability of birefringence and warpage for three pressure profiles from sequential programing (SP), along with those for velocity profiles in Fig. 7

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Figure 4

Velocity profile by sequential programing (SP) to enhance birefringence (VB), warpage (VW), and birefringence and warpage together (VC) compared to the profile from reinforcement learning (VR)

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Figure 3

Location of the recorded internal states within the mold cavity and sprue of CD-R

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Figure 1

Schematic of injection molding control

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