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In-Mold Sensors for Injection Molding: On the Way to Industry 4.0.
Ageyeva, Tatyana; Horváth, Szabolcs; Kovács, József Gábor.
Afiliación
  • Ageyeva T; Department of Polymer Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary.
  • Horváth S; Department of Polymer Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary.
  • Kovács JG; Department of Polymer Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary. kovacs@pt.bme.hu.
Sensors (Basel) ; 19(16)2019 Aug 15.
Article en En | MEDLINE | ID: mdl-31443164
ABSTRACT
The recent trend in plastic production dictated by Industry 4.0 demands is to acquire a great deal of data for manufacturing process control. The most relevant data about the technological process itself come from the mold cavity where the plastic part is formed. Manufacturing process data in the mold cavity can be obtained with the help of sensors. Although many sensors are available nowadays, those appropriate for in-mold measurements have certain peculiarities. This study presents a comprehensive overview of in-mold process monitoring tools and methods for injection molding process control. It aims to survey the recent development of standard sensors used in the industry for the measurement of in-mold process parameters, as well as research attempts to develop unique solutions for solving certain research and industrial problems of injection molding process monitoring. This review covers the established process monitoring techniques-direct temperature and pressure measurement with standard sensors and with the newly developed sensors, as well as techniques for the measurement of indirect process parameters, such as viscosity, warpage or shrinkage.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2019 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2019 Tipo del documento: Article