While additive manufacturing allows more complex shapes than conventional manufacturing processes, there is a clear benefit in leveraging both new and old processes in the definition of new parts. For example, one could create complex part shapes where the main “body” is defined by extrusion and machining, while small protruding features are defined by additive manufacturing. This paper looks at how optimization and geometric reasoning can be combined to identify optimal separation planes within a complex three-dimensional shapes. These separations indicate the joining processes in reverse. The optimization method presents possible manufacturing alternatives to an engineering designer where optimality is defined as a minimization of cost. The process identifies the cutting planes as well as the combination of processes required to join the individual parts together. The paper presents several examples of complex shapes and describes how the optimization finds the optimal results.

While additive manufacturing allows more complex shapes than conventional manufacturing processes, there is a clear benefit in leveraging both new and old processes in the definition of metal parts. For example, one could create complex part shapes where the main “body” is defined by extrusion and machining, while small protruding features are defined by additive manufacturing. This paper looks at how optimization and geometric reasoning can be combined to identify cutting planes within complex three-dimensional (3D) shapes. These cutting planes are used to divide realistic mechanical parts into subparts that can be joined together through additive manufacturing or linear friction welding (LFW). The optimization method presents possible manufacturing alternatives to an engineering designer where optimality is defined as a minimization of cost. The paper presents and compares several cutting planes identification methods and describes how the optimization finds the optimal results for several example parts.

This paper presents a method to decompose three dimensional complex parts into readily available stock material to take advantage of advanced joining to build up a rigid assembly. The method generates many alternative assemblies by decomposing the solid geometry iteratively with cutting planes. Each assembly is then evaluated based on cost. The process continues until the developed search algorithm converges on a near optimal solution. Application of this method will reduce material waste, thus reducing per part processing time, energy consumption, and associated production costs. Example parts for a variety of metals show how the computational tool finds near optimal solutions for complex three dimensional solids.

Advanced joining processes can be used to build-up complex parts from stock shapes, thereby reducing waste material. For high-cost metals, this can significantly reduce the manufacturing cost. Nevertheless, determining how to divide a complex part into subparts requires experience and currently takes hours for an engineer to evaluate alternative options. To tackle this issue, we present an artificial intelligence (AI) tree search to automatically decompose parts for advanced joining and generate minimum cost manufacturing plans. The AI makes use of a multi-fidelity optimization approach to balance exploration and exploitation. This approach is shown to provide good manufacturing feedback in less than 30 minutes and produce results that are competitive against experienced design engineers. Although the manufacturing plan models presented were developed specifically for linear and rotary friction welding, the primary algorithms are applicable to other advanced joining operations as well.

Design of closed-die forgings is often too difficult and time consuming to be done early in the design process, but the manufacturing cost is heavily influenced by early design decisions. This paper describes a method for quickly predicting closed-die forging designs from tessellated shape files. This is performed by computationally slicing the 3D solid into 2D cross-sections and applying forging design rules to these cross-sections. This method can be used to identify the best parting line and stroke direction as well as estimate both the forging weight and shape in seconds. This information can be used early in the design process to quickly help engineers make informed decisions and comparisons even before a part design is finalized. Initial results show that this method can be applied quickly and accurately to a wide array of realistic mechanical parts.