40pcs/Set 5inch 125mm Waterproof Wet Sandpaper Sanding Disc Grinding Polishing High Cutting Strength Silicon Carbide |

40pcs/Set 5inch 125mm Waterproof Wet Sandpaper Sanding Disc Grinding Polishing High Cutting Strength Silicon Carbide |

Item Details:

* 100% brand new and high quality

* Made of high quality material, durable and practical to use

* High cutting strength, good abrasion resistance and high cost performance.

* Widely used for polishing of artificial stone, furniture and wood products, metal, automobile and other items. And polishing sticky disc support

* Easy to use, simple and low cost. It is the first option for polishing wood products.

* Made of high-quality silicon carbide and waterproof sandpaper. Abrasive paper can be used for dry and wet grinding. Electroplating ensures uniform particle distribution

* Prevents seizure, antistatic, good flexibility, abrasion resistance, provides excellent service life and excellent surface treatment. It is more effective when using wet, because the grip is reduced by particles washed away from the abrasive surface. Can be used for precision polishing of mold surface, also for stone, lacquered surface, mirror surface treatment, such as headlight polishing. Waterproof sand disc can also be used for dry grinding, harder than white dry sandpaper

* Finish wooden furniture, polish gypsum, polish automobile metal, almost all polishing work can be done with them. Just choose the right number of particles for your product. Particles mean the average diameter of particles. The smaller the number of particles, the rougher it is, otherwise the finer it is

* Color: As pictures show

* Diameter: 125mm (Approx.)

* Grit: 800/1500/2000/2500/3000/5000/7000 /10000

Package Contents:

40 * Sandpapers (800/1500/2000 / 2500/3000/5000/7000/10000 5pcs for Each)

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.