Moldflow Monday Blog

Snis-896.mp4

Learn about 2023 Features and their Improvements in Moldflow!

Did you know that Moldflow Adviser and Moldflow Synergy/Insight 2023 are available?
 
In 2023, we introduced the concept of a Named User model for all Moldflow products.
 
With Adviser 2023, we have made some improvements to the solve times when using a Level 3 Accuracy. This was achieved by making some modifications to how the part meshes behind the scenes.
 
With Synergy/Insight 2023, we have made improvements with Midplane Injection Compression, 3D Fiber Orientation Predictions, 3D Sink Mark predictions, Cool(BEM) solver, Shrinkage Compensation per Cavity, and introduced 3D Grill Elements.
 
What is your favorite 2023 feature?

You can see a simplified model and a full model.

For more news about Moldflow and Fusion 360, follow MFS and Mason Myers on LinkedIn.

Previous Post
How to use the Project Scandium in Moldflow Insight!
Next Post
How to use the Add command in Moldflow Insight?

More interesting posts

Snis-896.mp4

return { 'avg_color': (avg_r, avg_g, avg_b) }

To generate features from a video, you might want to extract metadata and analyze the content. Metadata includes information like the video's duration, resolution, and creation date. Content features could involve analyzing frames for color histograms, object detection, or other more complex analyses. Step 1: Install Necessary Libraries You'll need libraries like opencv-python for video processing and ffmpeg-python or moviepy for easy metadata access. SNIS-896.mp4

import cv2 import numpy as np

metadata = extract_metadata("SNIS-896.mp4") print(metadata) For a basic content analysis, let's consider extracting a feature like the average color of the video: return { 'avg_color': (avg_r, avg_g, avg_b) } To

Check out our training offerings ranging from interpretation
to software skills in Moldflow & Fusion 360

Get to know the Plastic Engineering Group
– our engineering company for injection molding and mechanical simulations

PEG-Logo-2019_weiss

return { 'avg_color': (avg_r, avg_g, avg_b) }

To generate features from a video, you might want to extract metadata and analyze the content. Metadata includes information like the video's duration, resolution, and creation date. Content features could involve analyzing frames for color histograms, object detection, or other more complex analyses. Step 1: Install Necessary Libraries You'll need libraries like opencv-python for video processing and ffmpeg-python or moviepy for easy metadata access.

import cv2 import numpy as np

metadata = extract_metadata("SNIS-896.mp4") print(metadata) For a basic content analysis, let's consider extracting a feature like the average color of the video: