An approach to collaborative assembly design modification and assembly planning

Ph.DDOCTOR OF PHILOSOPH

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

A statistical tolerance analysis approach for over-constrained mechanism based on optimization and Monte Carlo simulation

Tolerancing decisions can profoundly impact the quality and cost of the mechanism. To evaluate the impact of tolerance on mechanism quality, designers need to simulate the influences of tolerances with respect to the functional requirements. This paper proposes a mathematical formulation of tolerance analysis which integrates the notion of quantifier: ‘‘For all acceptable deviations (deviations which are inside tolerances), there exists a gap configuration such as the assembly requirements and the behavior constraints are verified’’ & ‘‘For all acceptable deviations (deviations which are inside tolerances), and for all admissible gap configurations, the assembly and functional requirements and the behavior constraints are verified’’. The quantifiers provide a univocal expression of the condition corresponding to a geometrical product requirement. This opens a wide area for research in tolerance analysis. To solve the mechanical problem, an approach based on optimization is proposed. Monte Carlo simulation is implemented for the statistical analysis. The proposed approach is tested on an over-constrained mechanism

Concurrent design for optimal quality and cycle time

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001.Includes bibliographical references (p. 113-116).Product and manufacturing system design are the core issues in product development and dominate the profitability of a company. In order to assess and optimize the product and manufacturing system design, an objective evaluation framework is needed. Despite the many existing tools for product and manufacturing system design, there is a missing link between the product design and the production performances under system variability. The goal of the thesis is to explore and understand the interactions among part design and tolerancing, processes and system variability, and system control decision, then provide an integrated model to assess the total cost in a system. This model will be used to aid part design, tolerancing, batching, as well as strategy analysis in process improvement. A two-stage modeling approach is used to tackle the problem: quality prediction and production prediction. The quality prediction model projects the process variations into the output quality variations at each manufacturing stage, then predict the yield rate from the stochastic behavior of the variations and the tolerance. The production prediction model projects the demand rate and variability, processing times and variability, yield rates and batch-sizes into the manufacturing cycle time and inventories. After the performances are predicted through the previous two models, concurrent optimization of part design, tolerance, and batch-sizes are achieved by varying them to find the minimum cost. A case study at Boeing Tube shop is used to illustrate this approach. The result shows that the costless decisions in part design, tolerancing, and batch- sizes can significantly improve the system performance. In addition, conducting them separately or without using the system performance as the evaluation criteria may only lead to the local optima.by Yu-Feng Wei.Ph.D

Posture optimization algorithm for large structure assemblies based on skin model

Geometric deviations inevitably occur in product manufacturing and seriously affect the assembly quality and product functionality. Assembly simulations on the basis of computer-aided design (CAD) package could imitate the assembly process and thus find out the design deficiencies and detect the assemblability of the components. Although lots of researches have been done on the prediction of assembly variation considering the geometric errors, most of them only simplify the geometric variation as orientation and position deviation rather than the manufacturing deformation. However, in machinery manufacturing, even if the manufacturing defects are limited, they could propagate and accumulate through components and lead to a noncompliant assembly. Recently, many point-based models have been applied to assembly simulation; however they are mainly interested in simulating the resulting positions of the assembled parts and lack the consideration of the postprocessing after positioning. This paper enriches the complete assembly simulation process based on skin model and presents a simple and effective posture evaluation and optimization method. The studied approach includes a software algorithm applied to evaluate the contact state of the assembly parts and a mathematical model based on the particle swarm optimization to acquire the optimal assembly posture. To verify the efficiency and feasibility of the proposed method, a case study on the aircraft wing box scaling model assembly is performed

Tolerance analysis by static analogy on 2D assemblies with fits and fasteners

In tolerance analysis, the effect of clearance fits is especially difficult to estimate because the mating parts are not necessarily in actual contact and can take an infinite number of relative positions. The treatment of these situations is allowed in most of the available methods, possibly introducing additional elements in the dimension chains with appropriate statistical assumptions. The paper provides a similar extension for the static analogy, a previously proposed method that converts the tolerance analysis problem into an equivalent problem of force analysis. The procedure represents each fit, possibly between patterns of features (e.g., fasteners and holes), with a proper constraint in the equivalent static model. The ability of the constraint to transmit forces and torques is determined according to the types and directions of misalignments allowed by the joint clearance. With simple rules, this avoids complications in the static model, which must include only the constraint between parts rather than the geometric details of the mating features. The extended method, currently limited to 2D dimension chains, is demonstrated on examples involving both dimensional and geometric tolerances. The comparison with existing methods shows the correctness of the proposed procedure. The simplicity of the workflow confirms the possibility, already demonstrated for the static analogy, of avoiding numerical simulations or even the use of computer-based tools

A statistical tolerance analysis approach for over-constrained mechanism based on optimization and Monte Carlo simulation

Tolerancing decisions can profoundly impact the quality and cost of the mechanism. To evaluate the impact of tolerance on mechanism quality, designers need to simulate the influences of tolerances with respect to the functional requirements. This paper proposes a mathematical formulation of tolerance analysis which integrates the notion of quantifier: ‘‘For all acceptable deviations (deviations which are inside tolerances), there exists a gap configuration such as the assembly requirements and the behavior constraints are verified’’ & ‘‘For all acceptable deviations (deviations which are inside tolerances), and for all admissible gap configurations, the assembly and functional requirements and the behavior constraints are verified’’. The quantifiers provide a univocal expression of the condition corresponding to a geometrical product requirement. This opens a wide area for research in tolerance analysis. To solve the mechanical problem, an approach based on optimization is proposed. Monte Carlo simulation is implemented for the statistical analysis. The proposed approach is tested on an over-constrained mechanism

Tolerance optimization with statistical approach in the CAT environment

Nowadays, product development in all its phases plays a fundamental role in the industrial chain. The need for a company to compete at high levels, the need to be quick in responding to market demands and therefore to be able to engineer the product quickly and with a high level of quality, has led to the need to get involved in new more advanced methods/ processes. In recent years, we are moving away from the concept of 2D-based design and production and approaching the concept of Model Based Definition. By using this approach, increasingly complex systems turn out to be easier to deal with but above all cheaper in obtaining them. Thanks to the Model Based Definition it is possible to share data in a lean and simple way to the entire engineering and production chain of the product. The great advantage of this approach is precisely the uniqueness of the information. In this specific thesis work, this approach has been exploited in the context of tolerances with the aid of CAD / CAT software. Tolerance analysis or dimensional variation analysis is a way to understand how sources of variation in part size and assembly constraints propagate between parts and assemblies and how that range affects the ability of a project to meet its requirements. It is critically important to note how tolerance directly affects the cost and performance of products. Worst Case Analysis (WCA) and Statistical analysis (RSS) are the two principal methods in DVA. The thesis aims to show the advantages of using statistical dimensional analysis by creating and examining various case studies, using PTC CREO software for CAD modeling and CETOL 6σ for tolerance analysis. Moreover, it will be provided a comparison between manual and 3D analysis, focusing the attention to the information lost in the 1D case. The results obtained allow us to highlight the need to use this approach from the early stages of the product design cycle

Tolerance analysis — Form defects modeling and simulation by modal decomposition and optimization

Tolerance analysis aims on checking whether specified tolerances enable functional and assembly requirements. The tolerance analysis approaches discussed in literature are generally assumed without the consideration of parts’ form defects. This paper presents a new model to consider the form defects in an assembly simulation. A Metric Modal Decomposition (MMD) method is henceforth, developed to model the form defects of various parts in a mechanism. The assemblies including form defects are further assessed using mathematical optimization. The optimization involves two models of surfaces: real model and difference surface-base method, and introduces the concept of signed distance. The optimization algorithms are then compared in terms of time consumption and accuracy. To illustrate the methods and their respective applications, a simplified over-constrained industrial mechanism in three dimensions is also used as a case study