Rib-web structures are used for lightweight design in various applications. The most prominent cases are found in aerospace engineering, where intricate structures are produced by forging and subsequent machining or by machining from solid blocks of material. Due to the large scrap rate involved in conventional manufacturing, rib-web structures are suitable applications for additive manufacturing (AM) processes. Among the AM processes, wire-arc additive manufacturing (WAAM) is highly suitable for rib-web structures due to its high deposition rate and the potential to manufacture large-size parts. In WAAM, the welding strategy greatly influences the properties and quality of deposited parts. With an increasing number of starts and stops, the danger of uneven material build-up and welding defects increases. Unfortunately, most rib-web structures do not represent Eulerian paths, i.e. they cannot be manufactured with a continuous welding motion, in which every edge is visited only once. This study presents a novel strategy for generating optimal tool paths for WAAM of lightweight rib-web structures, mitigating the disadvantages of discontinuous welding paths such as welding defects and uneven build-up. It is shown that doubling the number of welding passes on each edge of the rib-web structure turns non-Eulerian paths into Eulerian paths, which can be welded continuously. When two or more weld beads are deposited on each edge, the vertices of the rib-web structure may suffer from underfilling. It is shown that this can be avoided by a correction strategy, which consists in manufacturing the part once, evaluating the size of voids in the junctions, and computing a correction to deposit the required amount of material into the center of the junction. While this strategy may be used if a single part is considered, it is shown that the tool path correction to be applied to arbitrary junction geometries can be represented by a neural network that is derived from an experimental database consisting of representative junction types. With this approach, paths for any rib-web geometry can be generated, which saves lead time in variant-rich production. The paths proposed in this work avoid non-welding moves and may hence outperform even single weld-bed strategies in terms of welding efficiency.

肋腹板结构用于各种应用中的轻量化设计。最显着的情况是在航空航天工程中发现的，其中复杂的结构是通过锻造和随后的机加工或通过固体材料块的机加工来生产的。由于常规制造中的废品率高，因此肋腹板结构适用于增材制造（AM）工艺。在增材制造工艺中，线弧增材制造（WAAM）由于其高沉积速率和制造大型零件的潜力而非常适合肋腹板结构。在WAAM中，焊接策略会极大地影响沉积零件的性能和质量。随着启停次数的增加，材料不均匀堆积和焊接缺陷的危险增加。不幸，大多数肋腹板结构不代表欧拉路径，即不能通过连续的焊接运动来制造，在连续的焊接运动中，每个边缘仅被访问一次。这项研究提出了一种新颖的策略，用于为轻质肋腹板结构的WAAM生成最佳工具路径，从而减轻了不连续焊接路径（如焊接缺陷和不均匀堆积）的缺点。结果表明，肋腹板结构每个边缘上的焊接次数增加一倍，就可以将非欧拉路径转变为可以连续进行焊接的欧拉路径。当在每个边缘上沉积两个或更多焊缝时，肋腹板结构的顶点可能会发生填充不足的情况。结果表明，可以通过一种校正策略来避免这种情况，该策略包括一次制造零件，评估接合处空隙的大小，计算校正，以将所需数量的材料沉积到接合点的中心。尽管如果考虑单个零件时可以使用此策略，但显示出可以应用于神经网络的刀具路径校正可以通过神经网络来表示，该神经网络是从包含代表性结类型的实验数据库中得出的。通过这种方法，可以生成任何肋腹板几何形状的路径，从而节省了变量丰富的生产中的交货时间。在这项工作中提出的路径避免了非焊接运动，因此就焊接效率而言甚至可以胜过甚至单一的焊接床策略。结果表明，可以应用于神经网络的工具路径校正可以通过神经网络来表示，该神经网络是从包含代表性结类型的实验数据库中得出的。通过这种方法，可以生成任何肋腹板几何形状的路径，从而节省了变量丰富的生产中的交货时间。在这项工作中提出的路径避免了非焊接运动，因此就焊接效率而言甚至可以胜过甚至是单个焊床策略。结果表明，可以应用于神经网络的工具路径校正可以通过神经网络来表示，该神经网络是从包含代表性结类型的实验数据库中得出的。通过这种方法，可以生成任何肋腹板几何形状的路径，从而节省了变量丰富的生产中的交货时间。在这项工作中提出的路径避免了非焊接运动，因此就焊接效率而言甚至可以胜过甚至是单个焊床策略。