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Simufact Engineering GmbH forming v16 software
Forming V16 Software, supplied by Simufact Engineering GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/simufact+software/forming+2022+software/ppr0239092-29-14-13
Average 90 stars, based on 1 article reviews
forming v16 software - by Bioz Stars, 2026-11
90/100 stars

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Related Articles

other:

Article Title: The Influence of the Parameters of the Skew Rolling Process for Bimetallic Elements on the Mechanical Properties and Structure of Materials
Article Snippet: Numerical simulations using finite elements analysis (FEM) were carried out in Simufact Forming 21 software.

Article Title: Experimental and Numerical Study of Printing Strategy Impact on the Mechanical Properties of Sustainable PLA Materials.
Article Snippet: Additionally, numerical simulations using the Simufact Forming 2022 software will be Polymers 2023, 15, 4639 3 of 17 conducted to predict the mechanical properties of the three types of PLA filaments under uniaxial tension, this software is usually used for predicting metal forming processes, in this work it was used to predict the mechanical behavior of PLA materials.

Article Title: The Optimization of Extrusion Process Parameters Utilizing the Taguchi Method
Article Snippet: The selection of process parameters in the extrusion process has a significant impact on the forming quality of the workpiece.. Based on the finite element simulation software simufact.. Forming, an orthogonal experiment was designed, and the Taguchi algorithm was used to optimize the extrusion parameters, including extrusion temperature, extrusion speed, and thinning rate, to achieve the best forming quality of the workpiece.

Article Title: Experimental and Numerical Study of Printing Strategy Impact on the Mechanical Properties of Sustainable PLA Materials
Article Snippet: Additionally, numerical simulations using the Simufact Forming 2022 software will be conducted to predict the mechanical properties of the three types of PLA filaments under uniaxial tension, this software is usually used for predicting metal forming processes, in this work it was used to predict the mechanical behavior of PLA materials.

Article Title: Experimental and Numerical Analysis of Rotary Friction Welding for Al-Cu Joints: Effects of Friction Time on Plastic Deformation and Joint Integrity.
Article Snippet: Numerical simulation of the friction welding process was performed using the Simufact forming 15.0 software.

Article Title: Springback Analysis and Prediction of Automotive Steel Sheets Used in Compression Bending.
Article Snippet: A numerical prediction of springback was conducted using Simufact Forming 2022 software.

Software:

Article Title: CNN-based preform design: effect of training data configuration on strain distribution in forged products
Article Snippet: This study investigates the effect of convolutional neural network (CNN)–based preform design on uniform strain distribution and microstructure in the forging of harmonic drive flex splines.. Two CNN-based strategies were employed to achieve uniform strain distribution: one using NURBS (non-uniform rational B-splines) curves to generate a comprehensive training dataset and the other iteratively refining the training dataset by incorporating preform shapes from previous iterations to enhance learning.. The CNN-designed preform reduced mean strain by 4.81%, standard deviation by 25.95%, and maximum strain by 19.22%.

Produced:

Article Title: CNN-based preform design: effect of training data configuration on strain distribution in forged products
Article Snippet: This study investigates the effect of convolutional neural network (CNN)–based preform design on uniform strain distribution and microstructure in the forging of harmonic drive flex splines.. Two CNN-based strategies were employed to achieve uniform strain distribution: one using NURBS (non-uniform rational B-splines) curves to generate a comprehensive training dataset and the other iteratively refining the training dataset by incorporating preform shapes from previous iterations to enhance learning.. The CNN-designed preform reduced mean strain by 4.81%, standard deviation by 25.95%, and maximum strain by 19.22%.



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