Additive Manufacturing Feature Taxonomy and Placement of Parts in AM Enclosure

Additive Manufacturing (AM) is a layer-by-layer deposition of material for the production of the desired product. The design flexibility associated with AM is much more when compared to the conventional manufacturing process. To manufacture a part with AM, two things play a critical role: the designing of the part and the other is the placement of the part in the build volume. As already mentioned, design flexibility associated with AM is much more when compared to the conventional manufacturing process. However, to correctly implement the design flexibility, we need a knowledge base at our disposal so that appropriate features can be used for the part production. The AM feature taxonomy forms the backbone of the knowledge base. The taxonomy comprises AM features classified based on different categories, which helps us understand every feature's importance. Talking about the part placement, we know that optimal placement is the key factor that makes the AM process economically feasible.

Algebra of Technological Process

A formal apparatus for modeling the structure of the technological process of mechanical processing based on the algebra of design and technological elements is presented. Design and technological element (manufacturing feature) is considered as a set of geometric processing area and the tool trajectory applied to it, set by a set of technological parameters. Algebra includes an addition operation (adding an element to the process structure) and a multiplication operation (merging elements). The set of processing elements forms an associative and generally noncommutative algebraic group. The possibility of using algebra for analysis and synthesis of technological process structures is shown

A multi-object posture coordination method with tolerance constraints for aircraft components assembly

Purpose This paper aims to improve the alignment accuracy of large components in aircraft assembly and an evaluation algorithm, which is based on manufacture accuracy and coordination accuracy, is proposed. Design/methodology/approach With relative deviations of manufacturing feature points and coordinate feature points, an evaluation function of assembly error is constructed. Then the optimization model of large aircraft digital alignment is established to minimize the synthesis assembly error with tolerance requirements, which consist of three-dimensional (3D) tolerance of manufacturing feature points and relative tolerance between coordination feature points. The non-linear constrained optimization problem is solved by Lagrange multiplier method and quasi-Newton method with its initial value provided by the singular value decomposition method. Findings The optimized postures of large components are obtained, which makes the tolerance of both manufacturing and coordination requirements be met. Concurrently, the synthesis assembly error is minimized. Compared to the result of the singular value decomposition method, the algorithm is validated in three typical cases with practical data. Practical implications The proposed method has been used in several aircraft assembly projects and gained a good effect. Originality/value This paper proposes a method to optimize the manufacturing and coordination accuracy with tolerance constraints when the postures of several components are adjusted at the same time. The results of this paper will help to improve the quality of component assemblies.

A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure the manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals. Furthermore, the wide variety of AM processes, materials, and machines creates challenges in determining manufacturability constraints. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to semantically model DFAM knowledge and retrieve that knowledge. The goal of the proposed DFAM ontology is to provide a structure for information on part design, AM processes, and AM capability to represent design rules. Furthermore, the manufacturing feature concept is introduced to indicate design features that are considerably constrained by given AM processes. After developing the DFAM ontology, queries based on design rules are represented to explicitly retrieve DFAM knowledge and analyze manufacturability using Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules enable effective reasoning to evaluate design features against manufacturing constraints. The usefulness of the DFAM ontology is demonstrated in a case study where design features of a bracket are selected as manufacturing features based on a rule development process. This study contributes to developing a reusable and upgradable knowledge base that can be used to perform manufacturing analysis.

A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals, e.g., reducing build-time. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to formalize DFAM knowledge and support queries for retrieving that knowledge. The DFAM ontology has three high level classes to represent design rules specifically: feature, parameter, and AM capability. Furthermore, the manufacturing feature concept is defined to link part design to AM process parameters. Since manufacturing features contain information on feature constraints of AM processes, the DFAM ontology supports manufacturability analysis of design features by reasoning with Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules in this study also help retrieve design recommendations for improving manufacturability. A case study is performed to illustrate usefulness of the DFAM ontology and SQWRL rule application. This study contributes to developing a knowledge base that can be reusable and upgradable and to analyzing manufacturing analysis to provide feedback about part designs to designers.