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The company manages NM400 wear-resistant plate, NM450 wear-resistant plate, Q355NH cold rolled railway steel strip, NM500 wear-resistant plate and other wear-resistant plate products. The wear-resistant plates are widely used in many industries, such as engineering, mining, cement, power, construction, machinery, coal, glass manufacturing, paper making, metallurgy, petrochemical, etc.. And the wear-resistant plates are also used in wheel device, grab equipment, bucket body, edge, lining plate, hopper, funnel, bucket valve, etc. parts.
In 2013, one of our clients in Korea purchased 890 tons Q345B square tube from us for one building construction project. Our clients required no burr in the side of the tube, we finally produce the goods with great condition and +/-0.25mm tolerance for wall thickness, which is a very great tolerance with customers’ required size. Our square tube full available range is 2-16mm for wall thickness, and 40-500mm for width. In addition, our Q/C department will seriously control all of the process of every order.
The company is deeply supported and loved by customers with its unique business philosophy and business model. The company has always been adhering to the "credibility first, integrity-based, quality first" purpose, and sincerely serve new and old customers. Over the past twenty years, the company has won the trust of customers by relying on good service and good reputation. Our aftersales team’s main responsibility is to deal with quality feedback and complaints from Customer.
Our company stocks a wide range of hot rolled carbon steel plate distributed globally from our service centers in the U.S., Canada, Mexico, U.K, France, Shanghai, and Singapore. Customers calling for Q355NH cold rolled railway steel strip A516 a lot of times are also trying to get their arms around forecast issues. As our customers continue to work on their forecasts, IRON & Steel has options to help you conduct structured forecast reviews and put inventory strategies in place to effectively manage any impact from forecast variability.