ISO 5139:2023

International Standard   Current Edition · Approved on 04 May 2023

Dentistry — Polymer-based composite machinable blanks

ISO 5139:2023 Files

English 18 Pages
Current Edition
BHD 47.23

ISO 5139:2023 Scope

This document specifies the characteristics of polymer-based composite machinable blanks with respect to the milling process and provides the test methods that address the clinical issues specific to those materials. In addition, this document specifies the items to be described on the packaging and materials, as well as descriptions to be included in the instructions for use.

The polymer-based composite machinable blanks covered in this document are blanks that are used for fabricating permanent dental restorative appliances (e.g. single crowns or inlays) by milling processes. They do not include large-sized blanks (e.g. discs) that allow for the fabrication of two or more units of crowns or bridges from one blank or materials for temporary use.

Best Sellers

GSO 150-2:2013
 
Gulf Standard
Expiration dates for food products - Part 2 : Voluntary expiration dates
BH GSO 150-2:2015
GSO 150-2:2013 
Bahraini Standard
Expiration dates for food products - Part 2 : Voluntary expiration dates
GSO 9:2022
 
Gulf Technical Regulation
Labeling of prepackaged food stuffs
BH GSO 9:2023
GSO 9:2022 
Bahraini Technical Regulation
Labeling of prepackaged food stuffs

Recently Published

ISO/TS 4966:2026
 
International Standard
Nanotechnologies — Silica nanomaterials — Specification of characteristics and measurement methods for nanoporous silica microparticles applied in liquid chromatography
ISO/TS 44005:2026
 
International Standard
Collaborative business relationship management system — Guidance on leadership for collaborative working
ISO 10325:2026
 
International Standard
Fibre ropes — High modulus polyethylene — 8-strand braided ropes, 12-strand braided ropes and covered ropes
ISO/IEC TS 42112:2026
 
International Standard
Information technology — Artificial intelligence — Guidance on machine learning model training efficiency optimization