Abstract: Lattice metamaterials provide an important foundation for lightweight and multifunctional structural design, while additive manufacturing enables the fabrication of complex lattice geometries. However, multiphysics lattice unit-cell design for additive manufacturing still faces two challenges. First, existing methods struggle to efficiently construct multi-objective Pareto fronts with adequate coverage under limited computational budgets. Second, manufacturing constraints, including overhangs, enclosed cavities, and restricted powder-removal channels, reduce the feasible design space. To address these challenges, a method driven by manufacturing constraints is proposed for lattice structure optimization and Pareto front construction. First, manufacturing constraints are incorporated into topology optimization in a differentiable form within an inverse homogenization framework, allowing physical performance and manufacturability to be considered within the same optimization process. Then, a progressive Pareto front construction mechanism is developed. A density generation network learns latent representations of high-quality lattice structures; the latent representations of neighboring nondominated solutions are interpolated, and the decoded density fields are used to initialize subsequent topology optimization. Newly obtained nondominated solutions are used to update the network and the sample set, progressively expanding the set of manufacturable Pareto solutions. Experiments on three-dimensional periodic lattice unit cells show that, under an identical budget of 1,000 optimization runs, the network initialization strategy achieves an optimization success rate of 92.60%, compared with 78.30% for random initialization. It yields 916 manufacturable samples, compared with 776 obtained through random initialization. The Pareto front obtained through network initialization has a hypervolume of 0.078 7, exceeding the value of 0.067 5 obtained through random initialization. These results demonstrate that the proposed method can efficiently construct Pareto fronts for multiple physical properties while accounting for manufacturing constraints and obtain manufacturable nondominated solution sets with broader coverage.