Assembly Time Estimation: Assembly Mate Based Structural Complexity Metric Predictive Modeling

This paper presents an automated tool for estimating assembly times of products based on a three step process: connectivity graph generation from assembly mate information, structural complexity metric analysis of the graph, and application of the complexity metric vector to predictive artificial neural network models. The tool has been evaluated against different training set cases, suggesting that partially defined assembly models and training product variety are critical characteristics. Moreover, the tool is shown to be robust and insensitive to different modeling engineers. The tool has been implemented in a commercial CAD system and shown to yield results of within ±25% of predicted values. Additional extensions and experiments are recommended to improve the tool

SENSITIVITY AND PRECISION ANALYSIS OF THE GRAPH COMPLEXITY CONNECTIVITY METHOD

In the Graph Complexity Connectivity Method (GCCM), twenty nine complexity metrics applied against engineering design graphs are used to create surrogate prediction models of engineering design representations (assembly models and function structures) for given product performance values (assembly time and market value). The performance of these prediction models has been previously assessed solely based on accuracy. In this thesis, the predictive precision of the surrogate models is evaluated in order to assess the GCCM\u27s ability to generate consistent results under the same conditions. The Assembly Model - Assembly Time (AM-AT) prediction model performed the best in terms of both accuracy and precision. This demonstrates that when given assembly models, one can consistently predict accurate assembly times. Further, a sensitivity analysis is conducted to identify the significant complexity metrics in the estimation of the performance values, assembly time and market value. The results of the analysis suggest that for each prediction model, there exists at least one metric from each complexity class (size, interconnection, centrality, and decomposition) which is identified as a significant predictor. Two of the twenty nine complexity metrics are found to be significant for all four prediction models: number of elements and density of the in-core numbers. The significant complexity metrics were used to create simplified surrogate models to predict the product performance values. The test results indicate that the precision of the prediction models increases but the accuracy decreases when the unique significant metric sets are used. Finally, three experiments are conducted in order to investigate the effect of manipulation of the significant complexity metrics in predicting the performance values. The results suggest that the significant metric sets perform better in predicting the product performance values as compared to the manipulated metric sets of either union or intersection of metrics

Comparison of Graph Generation Methods for Structural Complexity Based Assembly Time Estimation

This paper compares two different methods of graph generation for input into the complexity connectivity method to estimate the assembly time of a product. The complexity connectivity method builds predictive models for assembly time based on 29 complexity metrics applied to the product graphs. Previously, the part connection graph was manually created, but recently the assembly mate method and the interference detection method have introduced new automated tools for creating the part connectivity graphs. These graph generation methods are compared on their ability to predict the assembly time of multiple products. For this research, eleven consumers products are used to train an artificial neural network and three products are reserved for testing. The results indicate that both the assembly mate method and the interference detection method can create connectivity graphs that predict the assembly time of a product to within 45% of the target time. The interference detection method showed less variability than the assembly mate method in the time estimations. The assembly mate method is limited to only solidworks assembly files, while the interference detection method is more flexible and can operate on different file formats including IGES, STEP, and Parasolid. Overall, both of the graph generation methods provide a suitable automated tool to form the connectivity graph, but the interference detection method provides less variance in predicting the assembly time and is more flexible in terms of file types that can be used

A practical approach to goal modelling for time-constrained projects

Goal modelling is a well known rigorous method for analysing problem rationale and developing requirements. Under the pressures typical of time-constrained projects its benefits are not accessible. This is because of the effort and time needed to create the graph and because reading the results can be difficult owing to the effects of crosscutting concerns. Here we introduce an adaptation of KAOS to meet the needs of rapid turn around and clarity. The main aim is to help the stakeholders gain an insight into the larger issues that might be overlooked if they make a premature start into implementation. The method emphasises the use of obstacles, accepts under-refined goals and has new methods for managing crosscutting concerns and strategic decision making. It is expected to be of value to agile as well as traditional processes

The Inhuman Overhang: On Differential Heterogenesis and Multi-Scalar Modeling

As a philosophical paradigm, differential heterogenesis offers us a novel descriptive vantage with which to inscribe Deleuze’s virtuality within the terrain of “differential becoming,” conjugating “pure saliences” so as to parse economies, microhistories, insurgencies, and epistemological evolutionary processes that can be conceived of independently from their representational form. Unlike Gestalt theory’s oppositional constructions, the advantage of this aperture is that it posits a dynamic context to both media and its analysis, rendering them functionally tractable and set in relation to other objects, rather than as sedentary identities. Surveying the genealogy of differential heterogenesis with particular interest in the legacy of Lautman’s dialectic, I make the case for a reading of the Deleuzean virtual that departs from an event-oriented approach, galvanizing Sarti and Citti’s dynamic a priori vis-à-vis Deleuze’s philosophy of difference. Specifically, I posit differential heterogenesis as frame with which to examine our contemporaneous epistemic shift as it relates to multi-scalar computational modeling while paying particular attention to neuro-inferential modes of inductive learning and homologous cognitive architecture. Carving a bricolage between Mark Wilson’s work on the “greediness of scales” and Deleuze’s “scales of reality”, this project threads between static ecologies and active externalism vis-à-vis endocentric frames of reference and syntactical scaffolding

Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks

Assembly time estimation is traditionally a time-intensive manual process that requires detailed geometric and process information, which is often subjective and qualitative in nature. As a result, assembly time estimation is rarely applied during early design iterations. In this paper, the authors explore the possibility of automating the assembly time estimation process while reducing the level of design detail required. In this approach, they train artificial neural networks (ANNs) to estimate the assembly times of vehicle subassemblies using either assembly connectivity or liaison graph properties, respectively, as input data. The effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results indicate that this method can provide time estimates of an assembly process with ±15% error while relying exclusively on the geometric part information rather than process instructions

Automated Complexity Based Assembly Time Estimation Method

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\u27 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

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

Supporting connectivism in knowledge based engineering with graph theory, filtering techniques and model quality assurance

[EN] Mass-customization has forced manufacturing companies to put significant efforts to digitize and automate their engineering and production processes. When new products are to be developed and introduced the production is not alone to be automated. The application of knowledge regarding how the product should be designed and produced based on customer requirements also must be automated. One big academic challenge is helping industry to make sure that the background knowledge of the automated engineering processes still can be understood by its stakeholders throughout the product life cycle. The research presented in this paper aims to build an infrastructure to support a connectivistic view on knowledge in knowledge based engineering. Fundamental concepts in connectivism include network formation and contextualization, which are here addressed by using graph theory together with information filtering techniques and quality assurance of CAD-models. The paper shows how engineering knowledge contained in spreadsheets, knowledge-bases and CAD-models can be penetrated and represented as filtered graphs to support a connectivistic working approach. Three software demonstrators developed to extract filtered graphs are presented and discussed in the paper.The work presented has evolved during the IMPACT project, funded by the Swedish Knowledge Foundation, and has been partly presented on three conferences [8-10]. The three conference papers show the rendering of graphs for CAD-models, spread sheets and KBE-rules together with the first case example in this article. The work has also been partially supported by grant DPI2017-84526-R (MINECO/AEI/FEDER, UE), project CAL-MBE.Johansson, J.; Contero, M.; Company, P.; Elgh, F. (2018). Supporting connectivism in knowledge based engineering with graph theory, filtering techniques and model quality assurance. Advanced Engineering Informatics. 38:252-263. https://doi.org/10.1016/j.aei.2018.07.005S2522633