49Pcs TIG Welding Torch For Stubby Gas Lens #10 Glass Cup Kit For WP-17/18/26

Features: brand new and high quality Made of high-quality materials, The electrode holder is made of copper material, with a very small amount of impurities, especially phosphorus Arsenic, aluminum, etc., will greatly reduce the conductivity of copper Product parameters: Name: TIG welding torch accessories Suitable for: synopsis WP-17 / 18/26 Color: as shown Type: Welding accessories Scope of application: TIG Package list: 6 * #10 heat-resistant glass cup 1 * #4 13N08 alumina nozzle D6.5mm x 1/4" 1 * #5 13N09 alumina nozzle D8.0mm x 5/16" 1 * #6 13N10 alumina nozzle D9.50mm x 3/8" 1 * #7 13N11 alumina nozzle D11.0mm x 7/16" 1 * #8 13N12 alumina nozzle D12.50mm x 1/2"- 1 * #4 53N58 alumina nozzle cup 6.5mm 1/4" 1 * #5 53N59 alumina nozzle cup 8.0mm 5/16" 1 * #6 53N60 alumina nozzle cup 9.5mm 3/8" 1 * #7 53N61 alumina nozzle cup 11.0mm 1/16" 1 * #8 53N61S alumina nozzle cup 12.5mm 1/2" 1 *17CB20-1.0 collet short tube pneumoscope 1 *17CB20-1.6 collet short tube pneumoscope 1 *17CB20-2.4 collet short tube pneumoscope 1 *17CB20-3.2 collet short tube pneumoscope 1 *17CB20G chuck body, used for argon arc welding torch 1.0mm 0.040" 1 *17CB20G holder body, used for 1.6mm 1/16" argon arc welding 1 *17CB20G clamp body, used for 2.4mm 3/32" argon arc welding 1 *17CB20G chuck body for 3.2mm 1/8" argon arc welding 2 *10N22S chuck for argon arc welding torch 1.0mm 0.040" 2 *10N23S chuck for argon arc welding torch 1.6mm 1/16" 2 *10N24S chuck for 2.4mm 3/32" argon arc welding 2 *10N25S chuck for 3.2mm 1/8"" argon arc welding 1 *57Y04 short back cup, used for argon arc welding 1 *57Y03 middle back cup for argon arc welding 1 *57Y02 long back cup for argon arc welding 12 *High temperature resistant O-ring) 1 *18CG-20 cup gasket 1 *18CGG coaster Note: 1.The real color of the item may be slightly different from the pictures shown on website caused by many factors such as brightness of your monitor and light brightness. 2.Please allow slight manual measurement deviation for the data.