With manufacturers across industries racing to enlarge and diversify a client base, every new technology or process could mean another step towards reaching the full potential of their products or services, providing a valuable competitive advantage. Realizing the growing need for functionality enhancements due to the lack of 3D geometry support and extended time for manual cost scenario preparation, the client selected AMC Bridge as an industry professional capable of moving their existing cost estimation system to a new technological level.

  • Customer Benefits
    • Proper visualization of part geometry in 3D
    • Automatic analysis of CAD model geometry to identify manufacturing features
    • Reduction of the time and expense required to produce the estimates
    • Expanded and diversified client base
  • Project highlights
    • Development of executable modules aimed to optimize a cost estimation system
    • Identification and development of geometric algorithms needed for automatic manufacturing feature recognition
    • CAD conversion based on HOOPS® Exchange
    • Robust cost estimates that take into consideration detailed manufacturing data
  • Why AMC Bridge?
    • Unique expertise and practical experience in building feature recognition algorithms
    • Understanding of the specific manufacturing processes
    • Similar projects in the development team’s portfolio
    • Vast experience and deep knowledge of using HOOPS SDKs
    • A strong geometry group with Ph.D. in Mathematics as a part of the development team
    • Recommended by AMC Bridge client and partner—Tech Soft 3D Japan

Client

Known for providing top 3D printing services, AMC Bridge’s client specializes in creating state-of-the-art 3D printing solutions, providing digital fabrication support, and ensuring cloud-based manufacturing management.


Challenges

Increasing the operational performance of a particular product or service is a critical goal of a company that aims to gain a competitive advantage over its rivals. To effectively estimate and control the manufacturing cost, the client created a system that allows measuring and visualizing part geometry throughout the manufacturing process. But over the course of time, the client realized the growing need for functionality optimization to overcome limitations caused by the lack of 3D geometry support and massive manual effort when preparing a cost scenario. Aimed to achieve the solution’s full potential, the client looked for an experienced partner to implement proper recognition algorithms for automatic feature identification and make the switch from 2D to 3D modeling. On the recommendation of another AMC Bridge client and partner—Tech Soft 3D Japan—and due to the required technical credentials, AMC Bridge was selected for a project.


Solution

Commissioned to add 3D geometry support and develop automatic manufacturing feature recognition, which allows extracting feature parameters, the AMC Bridge team upgraded the existing cost estimation system with the following functionality:

  • Generation of HOOPS image data for client rendering. 
  • Automatic recognition of a predefined set of manufacturing features.

Particularly, the project scope included the development of two executable modules:

  1. CAD model conversion. Built on HOOPS Exchange, the module allows generating STEP files for geometry processing, extracting PMI data, and generating a 3D image for visualization on the front end. As a result, manufacturing features are highlighted according to additional information contained in the image file.
  2. Geometry recognition. Implemented from scratch, the module allows processing STEP files and PMI data to automatically generate a list of manufacturing features and their properties needed for preparing cost estimation. The module also provides the ability to handle complicated cases when a few features intersect. Open CASCADE® is used as the primary CAD engine.

Figure 1. A system architecture, including the implemented modules

As a result, the modules included the following components ready for seamless integration with the client’s system:

  • Geometry feature recognition algorithms:
    • To recognize 34 types of features, considering manufacturability specifics ​
    • To associate features with setup directions according to their accessibility​
    • To calculate the optimal stock orientation and its dimensions​

Figure 2. Feature types and their combinations

  • A tool to generate model data for visualization in the web UI​.
  • The infrastructure for further development and regression testing.


Process

The client provided a generic structure of their solution and a set of manufacturing features expected to be automatically processed. That was sufficient to initiate the project.

Having analyzed the project specifics, the AMC Bridge team, equipped with a strong geometry group with Ph.D. in Mathematics, started the project with the identification and further development of recognition algorithms and heuristics that would consider various cases of parts design. Given that the isolated features are relatively straightforward to recognize, the team dove deeper into the implementation of proper algorithms for feature traits that intersect. Based on their experience, the AMC Bridge experts identified overlooked critical aspects in the requirements and implemented heuristics to improve recognition by taking into account additional factors such as accessibility and manufacturability.

Overall, the solution was developed in four phases:

  1. Discovery. During this initial phase, the AMC Bridge team explored all the technical details and the best approach to achieve the project goals and provided a detailed development plan with time and cost estimation.
  2. Implementation. At this stage, the project team implemented two executable modules to provide the functionality for 3D model analysis, automatic feature recognition, and HOOPS file (SCS) generation to visualize 3D models within the client’s web application.
  3. Stabilization. This phase included feature freeze, bug fixing, and optimization of the application.
  4. Delivery. At this stage, the team focused on project deliverables to properly transfer them to the client and provided post-delivery support.

During the active development stage, the team provided regular deliverables of the developed functionality, which included weekly email reports, biweekly demos, and sprint planning.

To ensure the proper functioning of each developed feature, the QA team performed functional manual testing along with mechanical engineering analysis, regression testing, and bug verification.

Upon successful recognition of features included in the predefined test suite and after meeting the required performance characteristics, the project was accepted by the client and completed as scheduled.


Results

The resulting solution included two executable modules that were seamlessly integrated into the overall solution by the client’s development team.

As a result, the client achieved the following improvements to the existing cost estimation system:

  • The enhanced cost estimation solution with 3D CAD support and the capability to handle the following CAD formats: STEP, SOLIDWORKS®, Parasolid®, Inventor®, and IGES.
  • The reduced cost estimation preparation time by automating feature recognition. The solution automatically recognizes a predefined set of features of 3D CAD models and identifies feature parameters according to their type.
  • The reduced amount of manual labor required from end users to prepare cost estimations.
  • An essential toolset for further product development by the client’s team:
    • Basic math utilities and extendable project structure
    • Visualization and logging tools
    • Automated regression test framework

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AMC Bridge labs

  • AsBuilt

    AsBuilt™ is a technology preview of a plugin for SOLIDWORKS®, enabling users to load, manipulate and compare point cloud with mesh or BRep models. 3D scanning becomes a commonly used way for comparison of the manufactured part and initial CAD model. To prototype this workflow for a popular 3D CAD software, AMC Bridge conducted a research of various comparison algorithms, and as a result created AsBuilt, a technology demonstration add-in for SOLIDWORKS. It adds an option to load point cloud model to SOLIDWORKS session and visualize it with OpenGL, so that it could be aligned to a CAD model for detecting differences. 
  • AsBuilt Plus

    AsBuilt Plus is a technology demonstration of comparing CAD model prototypes with point clouds of the manufactured parts to establish and analyze deviations between the designed and produced parts. The technology demonstration is developed for the Autodesk Inventor® CAD software to interact with the Unreal Engine® real-time 3D creation tool.
  • Construction Safety—Hard-hat Detector

    Construction Safety—Hard-hat Detector is a web-based technology demonstration of identifying people without hard hats on construction sites from a video file. Using the TensorFlow™ open-source software library with embedded machine learning and image recognition algorithms, the technology demonstration performs video analysis and extracts the frames where non-hard hat wearers are detected.
  • AI for Facility Management

    AI for Facility Management is an IOS®-based technology demonstration of using machine learning to extract elements of a building façade from a plain photo image and automate the creation of a bill of materials (BOM). With AI for Facility Management, users can: Take or upload images of façade openings to their iPad® mobile digital devices for recognition Use the Recognize functionality to label each image Get an editable list of manufacturing details for each opening (door or window) Edit image notes Select which recognized images to include in the BOM Send the resulting BOM to their email as an email draft Review the BOM in the email draft Email the BOM to other recipients
  • PictureToMap

    PictureToMap™ is a stand-alone application that offers the unique capability to place an image directly onto the surface of Google Maps™. PictureToMap enables the control of scale, orientation and transparency of an image. It lets users save a project to a public archive visible to all other users for comment, or to a private archive visible only to the user.
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