Numerical and experimental determinations of contact heat transfer coefficients in nonisothermal glass molding

Abstract

Heat transfer at the interfacial contact is a dominant factor in the thermal behavior of glass during nonisothermal glass molding process. Recent research is developing reliable numerical approaches to quantify contact heat transfer coefficients. In most previous studies, however, both theoretical and numerical models of thermal contact conductance in glass molding attempted to investigate this factor by either omitting surface topography or simplifying the nature of contact surfaces. In fact, the determination of the contact heat transfer coefficient demands a detailed characterization of the contact interface including the surface topography and the thermomechanical behavior of the contact pair. This paper introduces a numerical approach to quantify the contact heat transfer by means of a microscale simulation at the glass‐mold interface. The simulation successfully incorporates modeling of the thermomechanical behaviors and the three‐dimensional topographies from actual surface measurements of the contact pair. The presented numerical model enables the derivation of contact heat transfer coefficients from various contact pressures and surface finishes. Numerical predictions of these coefficients are validated by transient contact heat transfer experiments using infrared thermography to verify the model.

Real-Time Quality Control in Thin Glass Forming Using Infrared Thermography and Deep Learning

“…It is, however, emphasized that imperfections of the molded components such as form deviation, chill ripples, or glass cracks are primarily driven by the molding step [17][18][19][20]. Those are the common defects observed in the nonisothermal molding process, mainly resulted from the high heat exchanges at the glass-mold interface [21,22] as well as the complex thermo-viscoelastic material behaviors of glass at elevated molding temperatures [23][24][25]. In serial production, if the defects are not detected during the molding step, it commonly results in a large number of glass rejects and high energy cost vain to anneal the glass failures [26].…”

Enabling Sustainability in Glass Optics Manufacturing by Wafer Scale Molding

“…The latter is influenced both by process parameters, e.g. temperature and molding force (e.g., Maxwell [15,16] or Burgers' models [17,18]), the heat transfers at the glassmold interface [19][20][21] and by system variables such as the geometry of the blank and that of the mold, as well as the surface properties [22]. Another challenge is the control of shrinkage, which is also significantly influenced by thermal and system parameters [23].…”

Machine learning-based predictive modeling of contact heat transfer

“…They can be categorized in three groups: the properties of the contacting materials, surface textures, and interfacial conditions such as temperature, pressure, and interlayers e.g., coating thickness. The scope of this study is to investigate the predictive performance of machine learning-based models and to compare with a theoretical approach such as Cooper-Mikic-Yovanovich model [9] or with a numerical simulation method [18]. For this study, we selected a glass type, the Borosilicate glass (SUPRAX ® 8488), and a temperature-resistant stainless steel (AISI 431) as the materials of the contact pair.…”

“…To overcome this deficit, recent modeling focuses rely on numerical techniques where the actual three-dimensional (3D) surface topographies of the contact pair were incorporated in the simulation models, enabled by Finite Element Method (FEM) [15,16] or half-space procedure [17]. It is emphasized that the prediction accuracy of the numerical models strongly requires adequate material models that are able to describe the deformation behaviors of the contact pair [18]. In addition, high computation effort is typically the major drawback held by the numerical method.…”