Date of Award
Doctor of Philosophy (PhD)
Summers, Joshua D
Gramopadhye , Anand K
Neyens , David
The overall goal of this research is to create an automated assembly time estimation method that is accurate and repeatable in an effort to reduce the analysis time required in estimating assembly times. Often, design for assembly (DFA) approaches are not used in industry due to the amount of time required to train engineers in the use of DFA, the time required to conduct the analysis, and the product level of detail needed. To decrease the analysis time and effort required in implementing the assembly time estimation portion of DFA, a tool is needed to estimate the assembly time of products while reducing the amount of information required to be manually input from the designer.
The Interference Detection Method (IDM) developed in this research retrieves part connectivity information from a computer-aided design (CAD) assembly model, based on a parts' relative location in the assembly space. The IDM is used to create the bi-partite graphs that are parsed into complexity vectors used with the artificial neural network complexity connectivity method to predict assembly times. The IDM is compared to the Assembly Mate Method which creates the connectivity graph based on the assembly mates used in creating the assembly model in CAD (SolidWorks). The results indicate that the IDM has a similar but larger percent error in estimating assembly time than the AMM. However, the variance of the AMM is larger than the variance observed with the IDM.
The AMM requires the assembly mates to create the connectivity graph, which may vary based on the designer creating the assembly model. The IDM, based on part location within the assembly model, is independent of any mates used to create the assembly. Finally, the assembly mate information is only stored in the SW assembly file, limiting the functionality of the AMM to SolidWorks assembly files. The IDM operates on the solid bodies in the assembly model, and therefore can be executed on an assembly after being imported by SW using common CAD exchange file types: assembly file (*.sldasm), IGES (*.iges), parasolid(*.x_t), and STEP (*.step;*.stp).
The IDM was also trained and tested as a tool for use during the conceptual phase of the design process. Assembly models were reduced in fidelity to represent a solid model created early in the design process when detailed information regarding the part geometry is not known. The complexity vectors of the reduced fidelity model are used as the input into a modified complexity connectivity method to estimate assembly time. The results indicate that the IDM can be used to predict the assembly time of products early in the design phase and performs best using a neural network trained using complexity vectors from high fidelity models.
To explore the potential for separating the objective handling times from the subjective insertion times, a Split Interference Detection Method is developed to use CAD part information to determine the handling time of the Boothroyd and Dewhurst assembly time estimation method and a modified complexity connectivity method approach is used to determine the insertion times. The handling and insertion times are separated because the handling times can be mostly determined using quantitative objective product information, while the insertion questions are subjective and cannot be quantitatively determined. The results suggest separation of the insertion and handling time does not reduce the percent error in estimating the assembly time of a product in comparison to the IDM. The handling portion of the SIDM can be used as a separate automated tool to determine the handling code and handling time of a product. The insertion portion of the Boothroyd and Dewhurst assembly time estimation method would still need to be calculated manually. The ultimate goal of this research is to develop and automated assembly time estimation method.
Namouz, Essam, "Automated Complexity Based Assembly Time Estimation Method" (2013). All Dissertations. 1165.