We are thrilled to announce that our QMOS-Caster L2 product was successfully implemented at Nucor Darlington SBQ shop in September 2022. This marks a significant milestone in our journey to provide advanced and comprehensive solutions to our customers in the steel industry.The QMOS-Caster L2 product boasts an array of key features that enable it to provide superior quality evaluation and …
Nucor Implements QMOS MES at its Nebraska Plant
Nucor implements QMOS at its Nebraska Plant in October 2021. As a part of its strategic roadmap for factory digitalization and Industry 4.0, Nucor Steel chose QMOS as the standard MES solution for all its engineered bar mill facilities. The Plant implements the complete suite of QMOS functions covering all production and quality operations in the Melt Shop, Rolling Mill, …
Quad Infotech has developed a Scrap Optimization algorithm to help reduce scrap usage costs
Quad Infotech developed a scrap optimization program which optimizes the scrap recipe that is used by scrap yard operators to build buckets. The program shows the lowest cost recipe for multiple grades given information such as scrap cost, scrap inventory, chemistry specifications and production requirements. This will reduce scrap costs for the melt shop as they will know which scraps …
The Electrode Management Tools in QMOS help meltshop better monitor and control their electrode consumption
Due to the recent surge in electrode prices, reducing cost has become an important aspect of the business. This can be achieved through detailed tracking and management of electrodes segments. Previously existing technologies only tracked generic electrode additions into the EAF and did not consider the electrode specifications or the type and quality of scrap steel that is being melted. …
QMOS Scalehouse System’s self checkin/checkout kiosks and mobile devices help modernize the truck and rail shipping and logistics operations by providing higher levels of collaboration, transparency and accuracy
With the new North American ELD mandate, the logistics departments are looking to improve the process and minimize the truck residency times. By bringing a higher level of organization, collaboration and oversight to the shipping activities, the Quad ScaleHouse Management System is designed to address these initiatives as well as bringing many more opportunities for efficiency improvement throughout the shipping …
QMOS Recycling Management System covers all ferrous and non-ferrous operations including planning and scheduling, production tracking, material handling, performance KPIs and much more
The Quad Recycling Management system introduces fresh concepts and methodologies for the recycling operations and creates a fully linked transparency for all production units. In doing so, the QRM enables a high level of accuracy in tracking of production and consumption material as well as KPIs such as yield, utilization, efficiency, uptime and more. Managing of the raw material inventory …
Quad Infotech is looking at reducing the TRT (Truck Residency Times) by as much as 30% by optimizing the bundle yard layout using clustering algorithms
Currently, tribal knowledge and simple heuristics are used to store products in the finished goods warehouse. Quad Infotech is conducting studies into using machine learning based approaches to come up with more optimal storage locations. The goal is to reduce overall time taken for product storage and picking. Clustering algorithms are yielding storage instructions that will save up to 30% …
Quad R-factor Management System provides millions in savings thru improved rolling practice
The Quad R-factor System has paid for itself in months through significant yield improvements. and sustainable production practices. Cobble rates have been cut in half with reported yield increases of up to 2% and shift utilization increased by 3%. These results have been achieved using a holistic approach to rolling mill operations. The Quad R-factor System (QRFS) clearly shows the …
Quad Infotech develops a new Machine Learning based approach to bring significant accuracy improvements in the calculation of predicted physicals
The new approach being introduced uses the latest in machine learning techniques to more closely relate chemical spectrometry data to predicted physical properties, yielding better results than existing regression models. Several machine learning approaches were evaluated, including AdaBoost, Random Forest and MLP. After thorough analysis, the one with the highest R2 value and other useful characteristics was selected. It is …