Hot Billet Surface Qualifier

OG Technologies, Inc. (OGT), developed a prototype of a Hot Billet Surface Qualifier (“Qualifier”) based on OGT’s patented HotEye™ technology and other proprietary imaging and computing technologies. The Qualifier demonstrated its ability of imaging the cast billets in line with high definition pictures, pictures capable of supporting the detection of surface anomalies on the billets. The detection will add the ability to simplify the subsequent process and to correct the surface quality issues in a much more timely and efficient manner. This is challenging due to the continuous casting environment, in which corrosive water, temperature, vibration, humidity, EMI and other unbearable factors exist. Each installation has the potential of 249,000 MMBTU in energy savings per year. This represents a cost reduction, reduced emissions, reduced water usage and reduced mill scale

Conceptual robustness in simultaneous engineering: A formulation in continuous spaces

This paper develops a robust and distributed decision-making procedure for mathematically modeling and computationally supporting simultaneous decision-making by members of an engineering team. The procedure (1) treats variations in the design posed by other members of the design team as conceptual noise ; (2) incorporates such noise factors into conceptually robust decision-making; (3) provides preference information to other team members on the variables they control; and (4) determines whether to execute the conceptually robust decision or to wait for further design certainty. While Chang et al. (1994) extended Taguchi's approach to such simultaneous decision-making, this paper uses a continuous formulation and discusses the foundations of the procedure in greater detail. The method is demonstrated by a simple distributed design process for a DC motor, and the results are compared with those obtained for the same problem using sequential decision strategies in Krishnan et al. (1991).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45880/1/163_2005_Article_BF01606903.pd

Conceptual robustness in simultaneous engineering: An extension of Taguchi's parameter design

Simultaneous engineering processes involve multifunctional teams; team members simultaneously make decisions about many parts of the product-production system and aspects of the product life cycle. This paper argues that such simultaneous distributed decisions should be based on communications about sets of possibilities rather than single solutions. By extending Taguchi's parameter design concepts, we develop a robust and distributed decision-making procedure based on such communications. The procedure shows how a member of a design team can make appropriate decisions based on incomplete information from the other members of the team. More specifically, it (1) treats variations among the designs considered by other members of the design team as conceptual noise; (2) shows how to incorporate such noises into decisions that are robust against these variations; (3) describes a method for using the same data to provide preference information back to the other team members; and (4) provides a procedure for determining whether to release the conceptually robust design or to wait for further decisions by others. The method is demonstrated by part of a distributed design process for a rotary CNC milling machine. While Taguchi's approach is used as a starting point because it is widely known, these results can be generalized to use other robust decision techniques.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45879/1/163_2005_Article_BF01608400.pd

Conceptual robustness in distributed concurrent engineering and design-in-modularity.

This research addresses robustness against conceptual noise: the uncertainty posed by the indecision of members in a design team, or variations of systems in which a single component will be used. Conceptually robust solutions, solutions that would function as desired regardless of team member indecision or system variation, help achieve prompt decision making in distributed concurrent engineering and modular product designs. Dr. G. Taguchi advocates product designs that are robust against physical noise, factors not controllable by the designers, yet affecting product performance. High quality products are insensitive to these factors. This noise concept is extended here to treat both uncertainty in a design team and system variation as conceptual noise, and conceptual robustness is pursued by designers. The idea of conceptual robustness supports two formal design methodologies: distributed concurrent engineering (DCE) and design-in-modularity (DiM). In DCE, conceptual robustness enables all participating engineers to concurrently proceed with their engineering tasks, even though these are coupled to one another. In DiM, conceptual robustness provides both a formal measure of product modularity and a formal procedure for pursuing product modularity. Conceptual noise is differentiated from its physical counterpart in that conceptual noise can be eliminated, for example, by solving a more complex centralized problem. On the other hand, physical noise can only be reduced, not eliminated, by efforts such as intensive control of the manufacturing process and environment. The procedures described here exploit this difference, modeling, for instance, the value of information and the cost of time. The conceptual robustness approach provides a new theoretical basis for DCE and DiM, and new procedures for their practices. The expected advantages include quality assurance, short lead-time, modular design, and distributed system optimization. The applications of conceptual robustness in DCE and DiM were tested in industry and on problems in the literature. The DCE procedure delivered good solutions on the tested cases with the advantages of distributed problem solving. The solutions show comparable performance to those obtained through other optimization methods, and good robustness. The proposed DiM approach identified a modular design for an automotive component and proved its modularity quantitatively.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/104756/1/9610093.pdfDescription of 9610093.pdf : Restricted to UM users only

SQA(TM): Surface Quality Assured Steel Bar Program

OG Technologies, Inc. (OGT) has led this SQA (Surface Quality Assured Steel Bar) program to solve the major surface quality problems plaguing the US special quality steel bars and rods industry and their customers, based on crosscutting sensors and controls technologies. Surface defects in steel formed in a hot rolling process are one of the most common quality issues faced by the American steel industry, accounting for roughly 50% of the rejects or 2.5% of the total shipment. Unlike other problems such as the mechanical properties of the steel product, most surface defects are sporadic and cannot be addressed based on sampling techniques. This issue hurts the rolling industry and their customers in their process efficiency and operational costs. The goal of this program is to develop and demonstrate an SQA prototype, with synergy of HotEye® and other innovations, that enables effective rolling process control and efficient quality control. HotEye®, OGT’s invention, delivers high definition images of workpieces at or exceeding 1,450C while the workpieces travel at 100 m/s. The elimination of surface defect rejects will be achieved through the integration of imaging-based quality assessment, advanced signal processing, predictive process controls and the integration with other quality control tools. The SQA program team, composed of entities capable of and experienced in (1) research, (2) technology manufacturing, (3) technology sales and marketing, and (4) technology end users, is very strong. There were 5 core participants: OGT, Georgia Institute of Technology (GIT), University of Wisconsin (UW), Charter Steel (Charter) and ArcelorMittal Indiana Harbor (Inland). OGT served as the project coordinator. OGT participated in both research and commercialization. GIT and UW provided significant technical inputs to this SQA project. The steel mills provided access to their rolling lines for data collection, design of experiments, host of technology test and verification, and first-hand knowledge of the most advanced rolling line operation in the US. This project lasted 5 years with 5 major tasks. The team successfully worked through the tasks with deliverables in detection, data analysis and process control. Technologies developed in this project were commercialized as soon as they were ready. For instance, the advanced surface defect detection algorithms were integrated into OGT’s HotEye® RSB systems late 2005, resulting in a more matured product serving the steel industry. In addition to the commercialization results, the SQA team delivered 7 papers and 1 patent. OGT was also recognized by two prestigious awards, including the R&D100 Award in 2006. To date, this SQA project has started to make an impact in the special bar quality industry. The resulted product, HotEye® RSB systems have been accepted by quality steel mills worldwide. Over 16 installations were completed, including 1 in Argentina, 2 in Canada, 2 in China, 2 in Germany, 2 in Japan, and 7 in the U.S. Documented savings in reduced internal rejects, improved customer satisfaction and simplified processes were reported from various mills. In one case, the mill reported over 50% reduction in its scrap, reflecting a significant saving in energy and reduction in emission. There exist additional applications in the steel industry where the developed technologies can be used. OGT is working toward bringing the developed technologies to more applications. Examples are: in-line inspection and process control for continuous casting, steel rails, and seamless tube manufacturing

Online Multi-Channel Forging Tonnage Monitoring and Fault Pattern Classification using Principal Curve

ABSTRACT Due to late response to process condition changes, forging processes are normally exposed to large number of defective products. To achieve online process monitoring, multi-channel tonnage signals are often collected from the forging press. The tonnage signals contain significant amount of real time information regarding the product and the process conditions. In this paper, a methodology is developed to detect profile changes of multi-channel tonnage signals for forging process monitoring and to classify fault patterns. The changes include global or local profile deviations, which correspond to deviations of a whole process cycle or process segment(s) within a cycle respectively. Principal curve method is used to conduct feature extraction and discrimination of tonnage signals. The developed methodology is demonstrated with industry data from a crankshaft forging processes