Computer-aided product design means using artificial intelligent systems to automatically design multiple industrial products. This technique has been pervasively applied in multiple domains, such as 3D printing and vehicle manufacture. One challenge of computer-aided design is to incorporate deep neural network to optimally fuse multiple decisions. Multi-objective decision encapsulates many decision-making objectives and leverages deep CNNs to evaluate/optimize the fused multiple decisions. Due to the objectives of economic and social benefit, it is necessary to use a variety of criteria to deeply evaluate and optimize schemes. In this paper, we propose a novel quality-guided deep neural network and weighting scheme to achieve multi-objective decision. We leverage RBF neural network to construct objective weight assignment model. Then, a deep CNN is designed to implement the weighting task, each of which corresponds to a single decision. Our deep CNN has five layers and contains multilayer perceptrons, which indicate the fully connected networks. Each neuron in one layer is connected to all neurons in the next layer. The target of our deep weight-based model is that the multi-objective optimization can be formulated as a single-objective optimization by assigning different weights to each objective. Finally, the non-inferior solution of the multi-objective optimization is generated by updating the weights of the deep CNN during fine tuning. In our experiment, we have demonstrated that our method has the potential to facilitate a variety of applications, such as 3D reconstruction and system optimization. We believe that our proposed algorithm can guide the optimization of various intelligent system pipeline.

计算机辅助产品设计意味着使用人工智能系统来自动设计多个工业产品。这项技术已广泛应用于多个领域，例如3D打印和车辆制造。计算机辅助设计的一项挑战是整合深度神经网络，以最佳方式融合多个决策。多目标决策封装了许多决策目标，并利用深层的CNN来评估/优化融合的多个决策。由于经济和社会利益的目标，有必要使用各种标准来深入评估和优化方案。在本文中，我们提出了一种新颖的质量指导的深度神经网络和加权方案，以实现多目标决策。我们利用RBF神经网络来构建客观的权重分配模型。然后，一个深层的CNN被设计来执行加权任务，每个加权任务都对应一个决策。我们的深层CNN有五层，并包含多层感知器，表示完全连接的网络。一层中的每个神经元都连接到下一层中的所有神经元。我们基于深度权重的模型的目标是，可以通过为每个目标分配不同的权重，将多目标优化制定为单目标优化。最后，通过在微调过程中更新深层CNN的权重来生成多目标优化的非劣解。在我们的实验中，我们证明了我们的方法具有促进各种应用（例如3D重建和系统优化）的潜力。