Aljabali Bader Alwomi, Shelton Joseph, Desai Salil
Department of Industrial & Systems Engineering, College of Engineering, North Carolina A & T State University, Greensboro, NC 27411, USA.
Department of Computer Science, College of Engineering and Technology, Virginia State University, Petersburg, VA 23806, USA.
Materials (Basel). 2024 Sep 16;17(18):4544. doi: 10.3390/ma17184544.
Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.
增材制造（AM）已经影响了多种材料复杂三维物体的制造，应用范围广泛。然而，作为一个新兴领域，增材制造缺乏针对不同增材制造工艺的自动化和特定设计规则。此外，为不同几何形状选择特定的增材制造工艺需要专业知识，而这很难复制。需要一个自动化且数据驱动的系统，该系统能够获取增材制造专家知识库并将其应用于3D打印部件，以避免可制造性问题。本研究旨在为工业4.0的增材制造设计（DFAM）框架内的增材制造工艺选择开发一个数据驱动系统。使用3D CAD数据作为输入，对遗传和进化特征加权技术进行了优化，以根据若干要求和约束条件确定最佳增材制造技术。开发了一个两阶段模型，其中基于数据集的定量特征标记和扩充，阶段1模型的平均准确率为70%，阶段2模型的平均准确率更高，可达97.33%。在分别与分布估计算法（EDA）和粒子群优化（PSO）算法进行基准测试后，确定稳态遗传算法（SSGA）是最有效的算法。该系统的输出有助于确定制造3D物体的最佳增材制造工艺。本文提出了一种用于增材制造系统的自动化设计，该设计准确且可扩展到其他3D打印工艺。
