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In digital manufacturing, converting advanced designs into quality products is hampered by manufacturers' limited design knowledge, restricting the adoption and enhancement of innovative solutions. This paper addresses this challenge through a novel generative denoising diffusion model (DDM) trained on historical 3D design data, enabling the creation of voxel-based designs that meet manufacturing standards. By integrating a surrogate model for evaluating the manufacturability of generated designs, the proposed DDM is able to optimize manufacturability during the generative process. This paper takes a leap forward from the predominant 2D focus of existing generative models towards 3D generative design, which not only broadens manufacturers' design capabilities but also accelerates the development of practical and optimized products. We demonstrate the efficacy of this approach via a case study on Microbial Fuel Cell (MFC) anode design, illustrating how this method can significantly enhance manufacturing workflows and outcomes. Our research offers a path for manufacturers to deepen their design expertise and foster innovation in digital manufacturing.

Both artificial intelligence (AI) and domain knowledge from human experts play an important role in manufacturing decision-making. While smart manufacturing emphasizes a fully automated data-driven decision-making, the AI incubation process involves human experts to enhance AI systems by integrating domain knowledge for modeling, data collection and annotation, and feature extraction. Such an AI incubation process will not only enhance the domain knowledge discovery, but also improve the interpretability and trustworthiness of AI methods. In this paper, we focus on the knowledge transfer from human experts to a supervised learning problem by learning domain knowledge as interpretable features and rules, which can be used to construct rule-based systems to support manufacturing decision-making, such as process modeling and quality inspection. Although many advanced statistical and machine learning methods have shown promising modeling accuracy and efficiency, rule-based systems are still highly preferred and widely adopted due to their interpretability for human experts to comprehend. However, most of the existing rule-based systems are constructed based on deterministic human-crafted rules, whose parameters, e.g., thresholds of decision rules, are suboptimal. On the other hand, the machine learning methods, such as tree models or neural networks, can learn a decision-rule based structure without much interpretation or agreement with domain knowledge. Therefore, the traditional machine learning models and human experts' domain knowledge cannot be directly improved by learning from data. In this research, we propose an interpretable neural network (INN) model with a center-adjustable Sigmoid activation function to efficiently optimize the rule-based systems. Using the rule-based system from domain knowledge to regulate the INN architecture will not only improve the prediction accuracy with optimized parameters, but also ensure the interpretability by adopting the inte