Search

Surface quality is one of the critical factors that affect the performance of a laser powder bed fusion part. Optimising process parameters in process design is an important way to improve surface quality. So far, a number of optimisation methods have been presented within academia. Each of these methods can work well in its specific context. But they were established on a few special surfaces and may not be capable to produce satisfying results for an arbitrary part. Besides, they do not consider the simultaneous improvement of the quality of multiple critical surfaces of a part. In this paper, an approach for optimising process parameters to improve the surface quality of laser powder bed fusion parts is proposed. Firstly, Taguchi optimisation is performed to generate a small number of alternative combinations of the process parameters to be optimised. Then, actual build and measurement experiments are conducted to obtain the quality indicator values of a certain number of critical surfaces under each alternative combination. After that, a flexible three-way technique for order of preference by similarity to ideal solution is used to determine the optimal combination of process parameters from the generated alternatives. Finally, a case study is presented to demonstrate the proposed approach. The demonstration results show that the proposed approach only needs a small amount of experimental data and takes into account the simultaneous improvement of the quality of multiple critical surfaces of an arbitrary part. [ABSTRACT FROM AUTHOR]

An aggregation operator of linguistic interval-valued intuitionistic fuzzy numbers (LIVIFNs) is an important tool for solving cognitively inspired decision-making problems with LIVIFNs. So far, many aggregation operators of LIVIFNs have been presented. Each of these operators works well in its specific context. But they are not always monotone because their operational rules are not always invariant and persistent. Dempster-Shafer evidence theory, a general framework for modelling epistemic uncertainty, was found to provide the capability for operational rules of fuzzy numbers to overcome these limitations. In this paper, a weighted averaging operator of LIVIFNs based on Dempster-Shafer evidence theory for cognitively inspired decision-making is proposed. Firstly, Dempster-Shafer evidence theory is introduced into linguistic interval-valued intuitionistic fuzzy environment and a definition of LIVIFNs under this theory is given. Based on this, four novel operational rules of LIVIFNs are developed and proved to be always invariant and persistent. Using the developed operational rules, a new weighted averaging operator of LIVIFNs is constructed and proved to be always monotone. Based on the constructed operator, a method for solving cognitively inspired decision-making problems with LIVIFNs is presented. The application of the presented method is illustrated via a numerical example. The effectiveness and advantage of the method are demonstrated via quantitative comparisons with several existing methods. For the numerical example, the best alternative determined by the presented method is exactly the same as that determined by other comparison methods. For some specific problems, only the presented method can generate intuitive ranking results. The demonstration results suggest that the presented method is effective in solving cognitively inspired decision-making problems with LIVIFNs. Furthermore, the method will not produce counterintuitive ranking results since its operational rules are always invariant and persistent and its aggregation operator is always monotone. [ABSTRACT FROM AUTHOR]

How to solve a multi-criterion decision-making (MCDM) problem with linguistic interval-valued intuitionistic fuzzy numbers (LIVIFNs) effectively is an important research topic. So far, a number of methods for solving this problem have been presented within the academia. Each of these methods can work well in specific situation. But they could produce undesirable decision-making results when the information for decision-making is insufficient or acquisition of the information needs a certain cost, since all of them are based on conventional two-way decision model. In this paper, three-way decision model is introduced into linguistic interval-valued intuitionistic fuzzy environment and a multi-criterion three-way decision-making method under this environment is presented. A specific relative loss function derived from an LIVIFN is established and corresponding three-way decision rules are developed. Based on the established function and developed rules, a three-way decision method for solving an MCDM problem with LIVIFNs is proposed. The application of the proposed method is illustrated via a practical example. The effectiveness and advantage of the method are demonstrated via an experimental comparison with some existing methods. The comparison results suggest that the proposed method is as effective as the existing methods and is more flexible than the existing methods in solving an MCDM problem with LIVIFNs. [ABSTRACT FROM AUTHOR]

In this paper, an approach for selection of materials in metal additive manufacturing based on three-way decision-making is proposed. The process of this approach is divided into three stages. First, a decision matrix for a material selection problem in metal additive manufacturing is established based on the basic components of the problem and normalised via a ratio model and a unified rule. Second, the summary loss function, conditional probability, and expected losses of each alternative material are calculated according to the weighted averaging operator, grey relational analysis, and the three-way decision theory, respectively. Third, the three-way decision-making results for the problem are generated according to the developed generation rules and the best material for the problem is selected based on the generated results. The application of the approach is illustrated via a material selection example in metal additive manufacturing. The effectiveness of the approach is demonstrated via a quantitative comparison with several existing approaches. The demonstration results suggest that the proposed approach is as effective as the existing approaches and is more flexible and advantageous in solving a material selection problem in metal additive manufacturing. [ABSTRACT FROM AUTHOR]

Part orientation is a critical task in the process of additive manufacturing product realisation. Recently, various computer-aided methods for this task have been presented in the literature. The coexistence of different methods generates a series of questions: What are the common characteristics of these methods? What are the specific characteristics of each method? What are the main issues in computer-aided part orientation for additive manufacturing currently? What are the potential research directions in this field in the future? To approach these questions, a review of the existing computer-aided part orientation methods for additive manufacturing is presented in this paper. This review starts with a clarification of a part orientation problem and a classification of the existing methods into two categories according to their process of solving the problem. An overview of the representative methods in each category is then carried out from the aspects of approaches for orientation search, generation, or selection, estimation of build orientation factors, determination of weights of factors, establishment of overall objective function, and demonstration of effectiveness. After that, a discussion about the main issues in computer-aided part orientation for additive manufacturing is documented based on the overview. Finally, a suggestion of some future research directions in this field is reported. [ABSTRACT FROM AUTHOR]

In this paper, a characterisation of diamond abrasive grains of grinding tools using industrial X-ray computed tomography (XCT) is carried out. One of the most challenge tasks in the characterisation is extracting the diamond abrasive grains from the XCT volume data. Methods that are able to extract the grains are then developed and introduced in this paper. The first step is to create a triangular mesh surface from the reconstructed volume file using a gradient anisotropic diffusion filter. The second step is to convert the measured greyscale volume into a signed distance field using a global threshold value and then a localised method for grain segmentation. To validate the proposed method, three different types of grinding tool specimens are measured and analysed. Each abrasive grain is segmented and the distributions of grains (with both random and designed patterns) are then calculated, plotted and analysed. The quantitative analysis clearly shows the deviations between the measured distribution and the designed pattern of the grinding tool, which indicates that the proposed method can provide an accurate and comprehensive characterisation of the grinding tools. [ABSTRACT FROM AUTHOR]

In this paper, a multiple criteria decision making (MCDM) method based on weighted Archimedean power partitioned Bonferroni aggregation operators of generalised orthopair membership grades (GOMGs) is proposed. Bonferroni mean operator, geometric Bonferroni mean operator, power average operator, partitioned average operator, and Archimedean T-norm and T-conorm operations are introduced into generalised orthopair fuzzy sets to develop the Bonferroni aggregation operators. Their formal definitions are provided, and generalised and specific expressions are constructed. On the basis of the specific operators, a method for solving the MCDM problems based on GOMGs is designed. The working process, characteristics, and feasibility of the method are, respectively, demonstrated via a numerical example, a qualitative comparison at the aspect of characteristics, and a quantitative comparison using the example as benchmark. The demonstration results show that the proposed method is feasible that has desirable generality and flexibility in the aggregation of criterion values and concurrently has the capabilities to deal with the heterogeneous interrelationships of criteria, reduce the negative influence of biased criterion values, and capture the risk attitudes of decision makers. [ABSTRACT FROM AUTHOR]

In computer-aided design (CAD) and process planning (CAPP), feature recognition is an essential task which identifies the feature type of a 3D model for computer-aided manufacturing (CAM). In general, traditional rule-based feature recognition methods are computationally expensive, and dependent on surface or feature types. In addition, it is quite challenging to design proper rules to recognise intersecting features. Recently, a learning-based method, named FeatureNet, has been proposed for both single and multi-feature recognition. This is a general purpose algorithm which is capable of dealing with any type of features and surfaces. However, thousands of annotated training samples for each feature are required for training to achieve a high single feature recognition accuracy, which makes this technique difficult to use in practice. In addition, experimental results suggest that multi-feature recognition part in this approach works very well on intersecting features with small overlapping areas, but may fail when recognising highly intersecting features. To address the above issues, a deep learning framework based on multiple sectional view (MSV) representation named MsvNet is proposed for feature recognition. In the MsvNet, MSVs of a 3D model are collected as the input of the deep network, and the information achieved from different views are combined via the neural network for recognition. In addition to MSV representation, some advanced learning strategies (e.g. transfer learning, data augmentation) are also employed to minimise the number of training samples and training time. For multi-feature recognition, a novel view-based feature segmentation and recognition algorithm is presented. Experimental results demonstrate that the proposed approach can achieve the state-of-the-art single feature performance on the FeatureNet dataset with only a very small number of training samples (e.g. 8–32 samples for each feature), and outperforms the state-of-the-art learning-based multi-feature recognition method in terms of recognition performances. [ABSTRACT FROM AUTHOR]

Build orientation determination is one of the essential process planning tasks in additive manufacturing since it has crucial effects on the part quality, post-processing, build time and cost, etc. This paper introduces a method based on fuzzy multi-attribute decision making to determine the optimal build orientation from a finite set of alternatives. The determination process includes two major steps. In the first step, attributes that are considered in the determination and heterogeneous relationships of which are firstly identified. A fuzzy decision matrix is then constructed and normalised based on the values of the identified attributes, which are quantified by a set of fuzzy numbers. In the second step, two fuzzy number aggregation operators are developed to aggregate the fuzzy information in the normalised matrix. By comparing the aggregation results, a ranking of all alternative build orientations can then be generated. Two determination examples are used to demonstrate the working process of the proposed method. Qualitative and quantitative comparisons between the proposed method and other methods are carried out to demonstrate its feasibility, effectiveness, and advantages. [ABSTRACT FROM AUTHOR]

In this paper, a tolerance representation model for generating tolerance specification schemes and corresponding tolerance zones is proposed to meet the requirement of the representation of tolerance information semantics. This model is hierarchically organized and consists of five layers. They are component, geometric feature, variational geometric constraint, tolerance specification scheme, and tolerance zone layers. The mating relations between components in the component layer, the mating relations between geometric features in the geometric feature layer, the variational geometric constraints between geometric features in the variational geometric constraint layer, the tolerance specification schemes of component in the tolerance specification scheme layer, and the tolerance zones of tolerance specification schemes in the tolerance zone layer are formally defined by one or more adjacency matrices, respectively. Based on the model, a method for generating tolerance specification schemes for component and their resultant tolerance zones is designed. This method shows how to adopt a top-down strategy to carry out tolerance specification for an arbitrary assembly designed in a CAD system. The paper also provides a practical example to illustrate how the method works. [ABSTRACT FROM AUTHOR]

Representing the semantics of the interaction of two or more tolerances (i.e., composite tolerance) explicitly to make them computer-understandable is currently a challenging task in computer-aided tolerancing (CAT). We have proposed a description logic (DL) ontology-based approach to complete this task recently. In this paper, the representation of the semantics of the composite positional tolerance (CPT) for patterns of holes (POHs) is used as an example to illustrate the proposed approach. This representation mainly includes representing the structure knowledge of the CPT for POHs in DL terminological axioms; expressing the constraint knowledge with Horn rules; and describing the individual knowledge using DL assertional axioms. By implementing the representation with the web ontology language (OWL) and the semantic web rule language (SWRL), a CPT ontology is developed. This ontology has explicitly computer-understandable semantics due to the logic-based semantics of OWL and SWRL. As is illustrated by an engineering example, such semantics makes it possible to automatically check the consistency, reason out the new knowledge, and implement the semantic interoperability of CPT information. Benefiting from this, the ontology provides a semantic enrichment model for the CPT information extracted from CAD/CAM systems. [ABSTRACT FROM AUTHOR]