Intralattice is a plugin for Grasshopper used to generate solid lattice structures within a 3D design space. It was developed as an extensible, open-source alternative to current commercial solutions. As an ongoing project developed at McGill’s Additive Design & Manufacturing Laboratory (ADML), it has been a valuable research tool, serving as a platform for breakthroughs in multi-scale design and optimization. By giving you full access to the source, we hope to collectively explore lattice design at a deeper level, and consequently, engineer better products.

The rise of additive manufacturing (i.e. 3D printing) has allowed engineers to integrate new orders of complexity into their designs. In that regard, this software generates lattice structures as a means to:

• Reduce volume/weight while maintaining structural integrity.
• Increase surface area as a means of maximizing heat transfer.
• Generate porosity in bone scaffolds and implants.
• Serve as a platform for structural optimization..

SimuLattice is a MATLAB based software to support the simulaion-based design of lattice structure. Previous simulation models for lattice structure is either computational heavy or lack of accuracy. The proposed simulation scheme in SimuLattice has three simulation models that can be used in three design stages. It can help designers quickly and accurately get the mechanical property of lattice structures fabricated by additive manufacturing.

The design of lattice structures can be divided into three design stages. In each stage, a simulation model is proposed. Furthermore, a material model is established in SimuLattice to consider the manufacturing influence of AM. There are totally four modules in SimuLattice:

• Numerical homogenization models for initial design.
• Joint stiffening element model for design optimization.
• Hybrid element model for design validation.
• As-built material property model.

ConsolidDesign is a plugin for Rhinoceros to support the identification and redesign of parts candidate for consolidation, which is specifically driven by the extremely expanded design freedoms of additive manufacturing. The design helps designers to semi-automatically identify part candidacy out of a complex product and guides them through the embodiment design step by step.

The functionality offered by this tool include:

• Interactive tool for establishing DSM-like part function interaction map.
• Automated identification of part candidates for consolidation.
• Guided steps for fusing the assembly into one single design space in preparation for structure optimization.

SimuLPBF is self-developed simulation tool for modeling laser powder bed fusion (LPBF) process at full part-level. It is developed based on open source finite element library named Deal.II which can simulate multiple physics process using advanced finite element techniques. This simulation tool helps designers, manufacturers, and researchers in the AM field to estimate the potential printing failures induced by the thermal cycles and to optimize process parameters to improve part performance.

The ability of this simulation tool includes:

• Self-defined temperature-dependent material property file.
• Simulation driven by scanning path file.
• Parallel computing.
• Incorporate Line heat source and hybrid heat source.
• Efficient adaptive mesh strategy and material deposition strategy.

With the rapid advancement of AM technologies, identifying parts which are eligible for AM as well as gaining insight on what value it may add to a product needs to be modelled in an objective and transferrable way. This tool aims at determining the candidacy of a part or assembly for AM based on its economic feasibility and potential for AM-specific benefits (such as lightweight, freeform shapes, and customization). This tool is also featured by its employment of machine learning algorithms with carefully sampled historical data.

Some basic functionality included:

• Several CAD format is supported (STEP, IGES, STL, CATPRODUCT, etc.)
• AM expertise is not required
• AM potentials are analyzed with regard to multiple criteria
• It also serves as a AM potential learning platform for beginners.

MAR-AM is a web-based automated ML-assisted manufacturability analyzer and recommender for AM. This tool can serve as a first-level evaluation of designs for novice AM users such as designers to determine whether their given design is printable through their selected printer. As an ongoing project developed at McGill’s Additive Design & Manufacturing Laboratory (ADML), it has been a valuable research tool. By giving full access to the source, we hope this tool can be useful to lower the threshold of AM processes and support wider applications of AM in the future.

The main functions of MAR-AM can be summarized as the following:

• With the selected AM process, printer, process parameters, and material, the evaluation of manufacturability on the uploaded design will be provided before the real fabrication. It helps to avoid unnecessary printing failure and reduce the waste of cost.
• Specific recommendations on how to avoid printing failure will be given for each case based on the evaluation. Users can use those recommendations as the starting point to print their samples.
• Users are also welcomed to contribute to the development of MAR-AM by donating their printing data to the MAR-AM which can be used to update the manufacturability models.

The MLACCD software is designed based on the python tkinter GUI library, and powered by the tensorflow. It contains models of MLACCD implementation, and an expandable design data base. This software provides a user-friendly interface that guide the researchers and industrial users to create their own MLACCD channels with desired conformal cooling topologies to minimize the resulting part surface temperature and increase the part quality.

The functions of the software include:

• Provide the mesh design of the temperature variance minimized (TVM) cooling channels with zigzag, spiral, and conformal porous structures (CPS) based on the user defined cooling surface in .stl format.
• Provide the data of the control lines for MLACCD cooling channels for simulation and research purposes.
• A flexible and expandable machine learning design database that can improve the speed of TVM simulations.

The emergence of Metal Additive Manufacturing has provided gas turbine design engineers with valuable design options, including the utilization of cellular solids. Nevertheless, there remains a dearth of research concerning the early selection of these structures and their interactions with fluid flows. Lattice structures, known for their adaptability in achieving specific properties such as high porosity, strength, energy absorption, and lightweight characteristics, have been thoroughly explored in terms of their mechanical performance. However, there is a significant paucity of research investigating their flow and heat transfer performance, particularly for strut-based lattice structures.

LatticeFLOAT provides design engineers with flow and thermal property charts to select a suitable topology of structures for their requirements. It is a complete correlation between flow and thermal characteristics of different topologies of lattice structures.

Two very important thermal properties which are compared for different topologies are:

• 1. Heat transfer rate (Q) per unit temperature difference (∆T) in W/K across different structures
• 2. Convective heat transfer coefficient (h)

Welcome to LatticeQuery - an open-source software designed for the modeling of lattice structures. This tool allows modeling of heterogeneous lattice structures, both beam-based and surface-based, and it is built upon the robust foundation of the CadQuery GUI editor. LatticeQuery supports Linux, Windows, and MacOS, making it accessible to a wide array of users.

Key features of LatticeQuery include:

• Beam-based and surface-based lattice structure modeling
• Compatibility with Linux, Windows, and MacOS
• Parametric modeling capabilities with OpenCASCADE and PyQT
• Flexible lattice topologies including Simple Cubic, BCC, FCC, and many more
• Conformal heterogeneous lattice examples

This is an interactive knowledge database for Multifunctional Bio-inspired Design (MBID) Ideation System

Multifunctional Bio-inspired Design (MBID) is a unique rapid ideation system for generating multifunctional and multiscale conceptual bio-inspired designs. This system consists of five components:

• Abstraction of biological features and their functions: BIKAS
• A method for classification, mapping, and integration of biological features: DOMAIN INTEGRATED DESIGN (DID)
• Parameters for selection of relevant biological features under convergent evolution: META-LEVEL DESIGN PARAMETERS
• An extension of DID and a model representation of the ideation system: EXPANDABLE DOMAIN INTEGRATED DESIGN (xDID) MODEL
• Generation of unique multifunctional bio-inspired conceptual designs: VERIFICATION

This web application provides an overview of the MBID ideation system and includes a link to Bio-inspired Knowledge Acquisition and Simulacrum (BIKAS). Key features of BIKAS include:

• Representing the function exhibited by a biological feature as a combination of integrated structure and structural strategy.
• Classifying biological features based on their characteristics into their respective geometric designations, called domains.

SciHIT IE (Scientific Human–AI Teaming for Information Extraction) is a human-centered framework designed to extract structured scientific information from literature using large language models (LLMs). It combines automated natural language processing with expert-in-the-loop feedback to ensure accuracy, transparency, and adaptability.

The framework is composed of three main components:

• Base IE System – Allows users to upload and parse scientific PDFs, retrieve semantically relevant paragraphs, and customize retrievals through interactive labeling (positive/negative).
• Paragraph Classification Tier – Trains classifiers to categorize paragraphs into key information types (e.g., data, model, sensing, system), enabling scalable filtering across large document collections.
• Query Tier – Uses a generative LLM to answer user-defined questions based on filtered content, supporting dynamic and focused information extraction.

SciHIT IE is designed for continuous improvement and domain adaptation, making it a reusable tool for accelerating literature mining in AM and other scientific fields.

DeepBead supports the on-machine integration of video-based deep learning models for defect detection during the additive manufacturing of metal matrix composites (MMCs). It can be deployed on industrial machines to support both R&D activities and production by providing near real-time analysis of monitoring data. The current version supports the integration of up to two monitoring systems and the detection of up to six different co-existing anomalies in the microstructure of MMCs. To enhance the analysis of monitoring data, predictions from individual video models can be fused for more effective defect detection.

DeepBead provides the following functions:

• Selection of different types of machine learning models and monitoring systems.
• Integration with optical monitoring systems to support on-machine process parameter qualification.
• Fusion of data from different monitoring systems to enhance machine learning-based predictions.
• Visualization of process parameters, machine learning inferences, monitoring feeds, microstructural defect analysis, and recommendations.