Computer Aided Fixture Design

Configure-to-Order (CTO) assembly systems are becoming pivotal in meeting the rising demand for mass customization. However, enabling rapid and cost-effective system reconfiguration remains a major challenge in manufacturing. Despite the flexibility of general-purpose machines, fixture design introduces significant bottlenecks, often requiring extensive manual effort to access, organize, and interpret fragmented or incomplete data. This leads to inefficient design pipelines that can compromise product functionality and result in costly redesign iterations, even when using advanced Computer-Aided Fixture Design (CAFD) tools. Although the Model-Based Definition (MBD) paradigm offers a promising response to these information-related challenges, its adoption within the CAFD domain remains limited. This work aims to support the broader adoption of MBD in this field through a multi-level contribution. First, a structured literature review identifies the essential semantic information required in an MBD dataset tailored to CAFD. Second, this content is structured in a machine-readable form using the ISO standard STEP AP242. Third, an MBD-driven CAFD framework is proposed. Finally, a proof-of-concept CAFD tool is developed by integrating established methodologies with commercial software to automate non-value-added tasks, such as configuring Computer-Aided Technologies (CAx). The results demonstrate the potential of this approach to streamline CAFD processes and establish a fully connected digital thread.

Chapter 3 - Computer-aided fixture design

A review and analysis of current computer-aided fixture design approaches

Advances in the technology of flexible manufacturing systems, and the introduction of concurrent engineering of product and process design have propounded the need for computer aided fixture design systems. Computer aided process planning is the interface between design and manufacturing and has a key role in achieving integration between the various modules in an FMS environment. Fixture design is inseparable from process planning, and hence to be of practical use any CAPP system must include fixture design. The past few years have seen great advances in automated fixture design systems and the tools used to achieve them. With the exception of fixture design for a limited range of products with close family similarity, fully automated design of practical fixtures is not yet achievable. The advances described in this book have widened the scope of application of computer aided fixture design and provide enhanced links with other product and process design activities.

Fixture design has traditionally been regarded as a manual process due to extensive requirements of heuristic knowledge and skilled craftsmanship. There are many mathematical and scientific formulae which can be used to calculate cutting forces, deflection of structural members, tolerance analysis of locating datum, etc. However many of the good fixture design features such as ease of loading/unloading, safety considerations, ingenuity in securing the workpieces, etc come from the experience and skill of the designers. Due to the extensive requirements of heuristic knowledge and craftsmanship, the automation of fixture design has not been considered possible in the past. Recent developments in artificial intelligence techniques and, in particular, knowledge representation have provided great opportunities in automating this field.

Investigate on computer-aided fixture design and evaluation algorithm for near-net-shaped jet engine blade

Flexible fixturing is an essential ingredient of flexible manufacturing systems (FMS) and computer-integrated manufacturing systems (CIMS). Computer-aided fixture design (CAFD) has become a research focus in implementing FMS and CIMS. This paper, presents an overview of interactive computer software developed with CAD interface for designing fixtures for machining centers (HMC/VMC). An exhaustive set of structured queries incorporated in the preprocessor prompts the designer to extract qualitative and quantitative part features. The database and decision support system (i.e., the rule base and the knowledge base) built into the design module assist the designer to select and position locators, calculate clamping forces, decide number and types of clamps, their locations and orientations, etc. Finally, in the post processor the bill of material, part and assembly drawings are obtained. A case study for the design of fixture for a Roller-head is presented and results are discussed. The implementation of the system has proved to be a quick and effective tool that reduces the design lead time from a few days to a few hours and requires fractional efforts and expertise on the part of the designer.

Modular fixture is conventionally designed less concerning the detailed specifications of machine tool. A little of literatures involve the effectively application to the existing CAD systems in CAFD (computer-aided fixture design). The determination of the validity of modular fixture during NC machining lacks for a practical method. This paper put forward that: Firstly, the feature-model repository of elements of modular fixture can be built in CAD packages; the design of modular fixture in NC machining should be accomplished under the concept of NC Manufacturing System (NMS); the Post-NC verification can be applied to check the performance of modular fixture applied in NC machining. Part 1 of the paper focuses on the feature-model repository of the modular-fixture elements. The other jobs will be introduced in the Part 2.

In this paper, the manufacturing software interoperability (MSI) was investigated. The definition of manufacturing software interoperability was proposed on the definition of semantic interoperability. And, some distinctions and correlations between interoperability and integration were analysed. The interoperability problem of manufacturing software was discussed. Furthermore, the Manufacturing Software Interoperability Model (MSIM) and its supported technologies were advocated. To verify MSIM model, the primary study on interoperation between Computer Aided Process Planning (CAPP) system and computer aided fixture design (CAFD) was carried out by the Model Driven Architecture (MDA) methodology. The results showed that the layered MSIM model can be a promise of the product data exchange, sharing and reuse without data loss and corruption among applications.

In this paper we introduce the heuristic Decision Support Problem (HDSP) as the means for integrating heuristic search and knowledge-based systems to provide decision support in design. Furthermore, we present a unique application of the HDSP to the design of products made by process manufacturing (e.g., gasoline, cosmetics) rather than products made by discrete manufacturing (e.g., machinery). The HDSP provides a format for representing the design problem that is independent of the specific application. The representation of the design problem is not restricted to mathematically exact information. A heuristic search algorithm, called the synthesis engine, is used to provide decision support in the form of solutions. The synthesis engine is able to evaluate feasibility and goodness of competing designs even though design variables and their associated relationships are not mathematically exact. The design of lubricant blends is used as a specific example of continuous-manufacturing products.

Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, a comprehensive framework that consists of three steps is proposed to predict defects and improve yield by using semi-supervised learning, time-series analysis, and classification model. In Step 1, semi-supervised learning using both labeled and unlabeled data is applied to generate quality values. In addition, feature values are predicted in time-series analysis in Step 2. Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. Compared to a conventional production plan, the suggested plan increases yield by up to 8.7%. The production plan proposed in this study is expected to contribute to not only the continuous manufacturing process but the discrete manufacturing process. In addition, it can be used in early diagnosis of equipment failure.

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Production Management (PM) in a discrete multi-product manufacturing enviroment consists in deciding, for each order of final products from customer, (A) when the manufacturing system must initiate to process items (B) by what machining center (or sequence of machining centers) the items must be processed (C) in what rates the working time of each machining center must beshared and assigned to items of different product families

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