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Advance Steel 2012

... and MORE: low financial outlay! Historically based on the AutoCAD platform, Advance Steel 2012 allows users to use their software with or without AutoCAD. In fact, the product includes its own graphics engine, and the user can select the desired CAD platform. Regardless of the selected platform, all the essential functionality of the software is retained, as well as DWG compatibility. This major change has also allowed GRAITEC to simplify the user interface by focusing on and organizing the industry specific functionality of the software. Another major impact for users: the overall cost of the solution and the ease of installation and deployment. The return on investment for the current version is improved by 30%.

Advance Steel 2012

Advance Steel is powerful yet user friendly 3D structural steel detailing and fabrication software that automatically creates drawings, BOMs and NC files and that communicates with Autodesk Revit among other systems.

Version 2012 of Advance Steel provides the user with the choice of CAD platform, AutoCAD or the GRAITEC CAD platform. The team will be demonstrating Advance Steel with both platforms at the NASCC.

GRAITEC will also be demonstrating Advance Design 2012, a superior solution for the structural analysis and design of Reinforced Concrete, Steel and Timber structures according to the latest versions of Eurocodes and North American codes

Advance Steel is a CAD software application for 3D modeling and detailing of steel structures and automatic creation of fabrication drawings, bill of materials and NC files. It was initially developed by GRAITEC, but was acquired by Autodesk in 2013.[1] The software runs on AutoCAD.

Advance Steel has a library of more than 300 preset parametric steel connections to connect Advance elements grouped in the following categories: beam end to end joints, base plate joints, general bracing joints, cantilever beam to column joints, plate joints, clip angle joints, pylon joints, tube brace joints, purlin joints, stiffener joints, and turnbuckle bracings.

I first saw steel detailing software in action in the mid 1980s at the AISC Steel Conference. AutoCAD had launched just a couple years earlier and it was rather quickly becoming the CAD software for the masses. A handful of visionary entrepreneurs developed software for the steel industry by piggybacking it on AutoCAD, and so were able to produce complete sets of steel detailing drawings and then upload code to NC machines. This allowed steel members to be cut to size, copped, and beveled as needed. Back then, AutoLISP was not yet added to AutoCAD, and so BASIC was pretty much the only game in town.

Advanced Steel and Crane was founded in Tulsa in 1970 by Bill Pleasant. It serves more than 70 major utilities and rural cooperatives across the United States and Canada with transmission, sub-station steel structures and components.

In July 2012, Advanced Steel and Crane became part of the EMC Group, which has pioneered turnkey solutions for extra high-voltage power system infrastructures throughout the world for six decades. During this time they have made significant investments in their Tulsa operations, from adding facilities and equipment to process and quality improvements.

2012 IL App (1st) 111977-U No. 1-11-1977 THIRD DIVISION May 30, 2012 NOTICE: This order was filed under Supreme Court Rule 23 and may not be cited as precedent by any party except in the limited circumstances allowed under Rule 23(e)(1). IN THE APPELLATE COURT OF ILLINOIS FIRST JUDICIAL DISTRICT ADVANCE STEEL ERECTION, INC., ) Appeal from the an Illinois Corporation, ) Circuit Court of ) Cook County. Plaintiff-Appellant, ) ) v. ) No. 11 CH 7771 ) DESIGN DATA CORPORATION, ) a Nebraska Corporation, ) Honorable ) LeRoy K. Martin, Jr., Defendant-Appellee. ) Judge Presiding. PRESIDING JUSTICE STEELE delivered the judgment of the court. Justices Murphy and Salone concurred in the judgment. O R D E R 1 Held: The circuit court properly granted defendant's motion to transfer venue to Nebraska pursuant to a forum selection clause contained in the parties' valid and fully executed software license agreement that plaintiff failed to show was unreasonable to enforce under the circumstances. 2 Plaintiff Advance Steel Erection, Inc. (Advance Steel) appeals from an order of the circuit court of Cook County granting defendant Design Data Corporation's (Design Data) motion to transfer venue to Lancaster County in Nebraska filed pursuant to sections 2-102,

Bowl Size: 16" x 14"Description: Drop-In Sink, 1-compartment, 16"W x 14"D front-to-back, 8" deep bowl, Deep Drawn? sink bowl, 18 gauge 304 stainless steel, includes: deck mounted gooseneck faucet (K-52), & basket drain, NSF Weight: 20 Gauge: 18 Cubes: 3 Specs Need Parts? Template Add to Quote Cart? Printer Friendly

Bowl Size: 20" x 16"Description: Drop-In Sink, 1-compartment, 20"W x 16"D front-to-back x 12" deep bowl, Deep Drawn? sink bowl, includes: deck mounted 8" swing spout faucet (K-50) & basket drain, 18 gauge 304 stainless steel, NSF Weight: 25 Gauge: 18 Cubes: 6 Specs Need Parts? Template Add to Quote Cart? Printer Friendly

Bowl Size: 20" x 16"Description: Drop-In Sink, 1-compartment, 20"W x 16"D front-to-back x 8" deep bowl, Deep Drawn? sink bowl, includes: deck mounted 8" swing spout faucet (K-50) & basket drain, 18 gauge 304 stainless steel, NSF Weight: 20 Gauge: 18 Cubes: 3 Specs Need Parts? Template Add to Quote Cart? Printer Friendly

Bowl Size: 28" x 20"Description: Drop-In Sink, 1-compartment, 28"W x 20"D front-to-back x 12" deep sink bowl, includes: deck mounted 12" swing spout faucet (K-53) & basket drain, 18 gauge 304 stainless steel, NSF Weight: 47 Gauge: 18 Cubes: 7 Specs Need Parts? Template Add to Quote Cart? Printer Friendly

Bowl Size: 14" x 10"Description: Drop-In Sink, 1-compartment, 14"W x 10"D front-to-back x 5" deep bowl, Deep Drawn? sink bowl, up-turn on sides & rear, includes: deck mounted gooseneck faucet (K-52) & basket drain, 20 gauge 304 stainless steel, NSF Weight: 12 Gauge: 20 Cubes: 3 Specs Need Parts? Template Add to Quote Cart? Printer Friendly

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Steel is still one of the most important and extensively used classes of materials because of its excellent mechanical properties while keeping costs low which gives a huge variety of applications1,2. The mechanical properties of steel are mainly determined by its microstructure3 shown in Fig. 1, so that the performance of the material highly depends on the distribution, shape and size of phases in the microstructure4. Thus, correct classification of these microstructures is crucial5. The microstructure of steels has different appearances, influenced by a vast number of parameters such as alloying elements, rolling setup, cooling rates, heat treatment and further post-treatments6. Depending on how the steel is produced due to these parameters, the microstructure consists of different constituents such as ferrite, cementite, austenite, pearlite, bainite and martensite7 shown in Fig. 1.

This motivation leads us to use Deep Learning methods which are recently grabbing the attention of scientists due to their strong ability to learn high-level features from raw input data. Recently, these methods have been applied very successfully to computer vision problems8,9. They are based on artificial neural networks such as Convolutional Neural Networks (CNNs)9. They can be trained for recognition and semantic pixel-wise segmentation tasks. Unlike traditional methods in which feature extraction and classification are learnt separately, in Deep Learning methods, these parts are learnt jointly. The trained models have shown successful mappings from raw unprocessed input to semantic meaningful output. As an example, Masci et al.10 used CNNs to find defects in steel. In this work, we show that Deep Learning can be successfully applied to identify microstructural patterns. Our method uses a segmentation-based approach based on Fully Convolutional Neural Networks (FCNNs) which is an extension of CNNs accompanied by a max-voting scheme to classify microstructures. Our experimental results show that the proposed method considerably increases the classification accuracy compared to state of the art. It also shows the effectiveness of pixel-based approaches compared to object-based ones in microstructural classification.

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