Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications, owing to their high biocompatibility, significantly good mechanical properties. In order to improve the Ti–6Al–4V SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization methodology based on an artificial neural network was developed to relate four key process parameters (laser power, laser scanning speed, layer thickness, and hatch distance) and two target properties of a part fabricated by the SLM technique (density ratio and surface roughness). A supervised learning deep neural network based on the backpropagation algorithm was applied to optimize input parameters for a given set of quality part outputs. Several methods were utilized to solve undesired problems occurring during neural network training to increase the model accuracy. The model’s performance was proven with a value of R² of 99% for both density ratio and surface roughness. A selection system was then built, allowing users to choose the optimal process parameters for fabricated products whose properties meet a specific user requirement. Experiments performed with the optimal process parameters recommended by the optimization system strongly confirmed its reliability by providing the ultimate part qualities nearly identical to those defined by the user with only about 0.9–4.4% of errors at the maximum. Finally, a graphical user interface was developed to facilitate the choice of the optimum process parameters for the desired density ratio and surface roughness.

通过选择性激光熔化（SLM）生产的钛基合金产品，由于它们具有高生物相容性，非常好的机械性能，已被广泛用于生物医学应用。为了提高Ti-6Al-4V SLM制造的零件质量并帮助制造工程师选择最佳工艺参数，开发了一种基于人工神经网络的优化方法，将四个关键工艺参数（激光功率，激光扫描速度，层厚度和剖面线距离）以及通过SLM技术制造的零件的两个目标特性（密度比和表面粗糙度）。基于反向传播算法的有监督学习深度神经网络被用于为给定的一组优质零件输出优化输入参数。几种方法被用来解决神经网络训练过程中出现的不良问题，从而提高了模型的准确性。R的值证明了模型的性能密度比和表面粗糙度均达到99％的²。然后建立了一个选择系统，使用户可以为性能满足特定用户要求的制成品选择最佳工艺参数。使用优化系统推荐的最佳工艺参数进行的实验通过提供与用户定义的最终零件质量几乎相同的最大零件质量来最大程度地证实了其可靠性，最大误差仅为0.9-4.4％。最后，开发了图形用户界面，以便于为所需的密度比和表面粗糙度选择最佳工艺参数。