Allocation of geometric tolerances in one-dimensional stackup problems

Many tolerancing problems on mechanical assemblies involve a functional requirement depending on a chain of parallel dimensions on individual parts. In these one-dimensional cases, simple methods are available for the analysis and the allocation of dimensional tolerances. However, they are difficult to extend to geometric tolerances, which must be translated into equivalent dimensional tolerances; this allows the analysis but makes the allocation generally impossible without Monte Carlo simulation and complex search strategies. To overcome this difficulty, the paper proposes a way of dealing directly with geometric tolerances in the allocation problem. This consists in expressing the functional requirement as a linear model of geometric tolerances rather than equivalent dimensional tolerances; the coefficients of the model (sensitivities) are calculated considering both the dimension chain and the standard definition of the geometric tolerances. The approach can be combined with any constrained optimization method based on sensitivities. The optimal scaling method, previously proposed for dimensional tolerances, is extended to geometric tolerances and used in two examples to demonstrate the simplicity of the overall workflow and the quality of the optimal solution

Integrated Tolerance and Fixture Layout Design for Compliant Sheet Metal Assemblies

Part tolerances and fixture layouts are two pivotal factors in the geometrical quality of\ua0a compliant assembly. The independent design and optimization of these factors for compliant\ua0assemblies have been thoroughly studied. However, this paper presents the dependency of these\ua0factors and, consequently, the demand for an integrated design of them. A method is developed\ua0in order to address this issue by utilizing compliant variation simulation tools and evolutionary\ua0optimization algorithms. Thereby, integrated and non-integrated optimization of the tolerances and\ua0fixture layouts are conducted for an industrial sample case. The objective of this optimization is\ua0defined as minimizing the production cost while fulfilling the geometrical requirements. The results\ua0evidence the superiority of the integrated approach to the non-integrated in terms of the production\ua0cost and geometrical quality of the assemblies

Multi-objective optimization for optimum tolerance synthesis with process and machine selection using a genetic algorithm

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the Genetic Algorithm (GA) technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology

Concurrent tolerance allocation and scheduling for complex assemblies

Traditionally, tolerance allocation and scheduling have been dealt with separately in the literature. The aim of tolerance allocation is to minimize the tolerance cost. When scheduling the sequence of product operations, the goal is to minimize the makespan, mean flow time, machine idle time, and machine idle time cost. Calculations of manufacturing costs derived separately using tolerance allocation and scheduling separately will not be accurate. Hence, in this work, component tolerance was allocated by minimizing both the manufacturing cost (sum of the tolerance and quality loss cost) and the machine idle time cost, considering the product sequence. A genetic algorithm (GA) was developed for allocating the tolerance of the components and determining the best product sequence of the scheduling. To illustrate the effectiveness of the proposed method, the results are compared with those obtained with existing wheel mounting assembly discussed in the literature

Improvement of Geometric Quality Inspection and Process Efficiency in Additive Manufacturing

Additive manufacturing (AM) has been known for its ability of producing complex geometries in flexible production environments. In recent decades, it has attracted increasing attention and interest of different industrial sectors. However, there are still some technical challenges hindering the wide application of AM. One major barrier is the limited dimensional accuracy of AM produced parts, especially for industrial sectors such as aerospace and biomedical engineering, where high geometric accuracy is required. Nevertheless, traditional quality inspection techniques might not perform well due to the complexity and flexibility of AM fabricated parts. Another issue, which is brought up from the growing demand for large-scale 3D printing in these industry sectors, is the limited fabrication speed of AM processes. However, how to improve the fabrication efficiency without sacrificing the geometric quality is still a challenging problem that has not been well addressed. In this work, new geometric inspection methods are proposed for both offline and online inspection paradigms, and a layer-by-layer toolpath optimization model is proposed to further improve the fabrication efficiency of AM processes without degrading the resolution. First, a novel Location-Orientation-Shape (LOS) distribution derived from 3D scanning output is proposed to improve the offline inspection in detecting and distinguishing positional and dimensional non-conformities of features. Second, the online geometric inspection is improved by a multi-resolution alignment and inspection framework based on wavelet decomposition and design of experiments (DOE). The new framework is able to improve the alignment accuracy and to distinguish different sources of error based on the shape deviation of each layer. In addition, a quickest change point detection method is used to identify the layer where the earliest change of systematic deviation distribution occurs during the printing process. Third, to further improve the printing efficiency without sacrificing the quality of each layer, a toolpath allocation and scheduling optimization model is proposed based on a concurrent AM process that allows multiple extruders to work collaboratively on the same layer. For each perspective of improvements, numerical studies are provided to emphasize the theoretical and practical meanings of proposed methodologies

Peer to peer metrological data sharing model

Present manufacturing systems often generate enormous amounts of data, that are often forgotten or lost. A major reason for ignoring such data is the heterogeneity of data. This research focuses on the heterogeneity between the manufacturing machine’s capacity parameters and part design. In manufacturing factories, the machine capacity data is available in form of machine specifications, while part data is stored in 2D or 3D-CAD models. In this thesis, a framework is proposed to provide guidelines and strategies for acquiring, pre-processing, and storing manufacturing capacity data in the form of structured table-oriented database systems. The framework also proposes the extraction, pre-processing, and storage of dimensional data of Computer-Aided Design (CAD) part models into feature-based-logical storage within XML files. Such a database storage system can improve vendor search using advanced predictive modeling. Such a system is beneficial for small-medium scale machine shops for quantifying their manufacturing capability and constraints and linking such with a prospective pool of manufacturing part’s designs