Search results

In the implementation of large-size additive manufacturing (AM), the large printing area can be established by using the tiled and fixed multiple printing heads or the single dynamic printing head moving in the x–y plane, which requires a layer decomposition after the mesh slicing to generate segmented infill areas. The data processing flow of these schemes is redundant and inefficient to some extent, especially for the processing of complex stereolithograph (STL) models. It is of great importance in improving the overall efficiency of large-size AM technics software by simplifying the redundant steps. This paper aims to address these issues.

In this paper, a method of directly generating segmented layered infill areas is proposed for AM. Initially, a vertices–mesh hybrid representation of STL models is constructed based on a divide-and-conquer strategy. Then, a trimming–mapping procedure is performed on sliced contours acquired from partial surfaces. Finally, to link trimmed open contours and inside-signal square corners as segmented infill areas, a region-based open contour closing algorithm is carried out in virtue of the developed data structures.

In virtue of the proposed approach, the segmented layered infill areas can be directly generated from STL models. Experimental results indicate that the approach brings us the good property of efficiency, especially for complex STL models.

The proposed approach can generate segmented layered infill areas efficiently in some cases.

The region-based layered infill area generation approach discussed here will be a supplement to current data process technologies in large-size AM, which is very suitable for parallel processing and enables us to improve the efficiency of large-size AM technics software.

The purpose of this paper is to explore how functional and appropriateness arguments influence the adoption of modularity principles during the design of a professional service architecture.

Action design research was conducted to examine the design process of a modular service architecture for specialised elderly care by a multi-professional group. Data collection methods included, partly participatory, observations of the interactions between professionals during the design process, interviews and document analysis. Data analysis focussed on the emerging design choices and the arguments underlying them.

A wide range of both functional and appropriateness considerations were enlisted during the design process. The three core modularity principles were adapted to varying degrees. In terms of the design outcome, the interdependencies between the modularity principles necessitated two trade-offs in the modular design. A third trade-off occurred between modularity and the need for professional inference where services were characterised by uncertainty. Appropriateness was achieved through the professionals reframing and translating the abstract modularity concept to reconcile the concept’s functionality with their professional norms, values and established practices.

The study adds to service modularity theory by formulating three trade-offs that are required in translating the core modularity principles into a functional set of design choices for a multi-professional service environment. Moreover, the inherent intertwinedness of the core modularity principles in professional services requires an iterative design process. Finally, the authors saw that the ambiguity present in the service modularity concept can be used to develop a design that is deemed appropriate by professionals.

This paper aims to develop a signal acquisition system of surface electromyography (sEMG) and use the characteristics of (sEMG) signal to interference action pattern.

This paper proposes a fusion method based on combining the coefficient of AR model and wavelet coefficient. It improves the recognition rate of the target action. To overcome the slow convergence speed and local optimum in standard BP network, the study presents a BP algorithm which combine with LM algorithm and PSO algorithm, and it improves the convergence speed and the recognition rate of the target action.

Experiments verify the effectiveness of the system from two aspects the target motion recognition rate and the corresponding reaction speed of the robotic system.

The study developed a signal acquisition system of sEMG and used the characteristics of (sEMG) signal to interference action pattern. The myoelectricity integral values are presented to determine the starting point and end point of target movement, which is more effective than using single sample point amplitude method.

This paper presents a technique for mapping the modelling of manufacturing processes, in which process maps are used to represent information on the models and modelling technique (including assumptions used), process and equipment parameters, physical sub‐processes, process variables, and the process performance in terms of quality and/or defects. The mapping approach uses the top‐down methodology, in which any manufacturing process can be represented in a structured, multi‐layered manner, with each layer representing a different level of the modelling spectrum. This structure is designed to provide a clear overview of the process and sub‐processes, and their interactions, while the finer details of the modelling process are still presented at the lower levels of the map. This mapping approach is illustrated with the modelling of the Printing of Solder Paste for the reflow soldering of SMT devices. This case study shows how the mapping process can be used to identify the key research issues, specify the experimental work required, and also identify the analytical modelling techniques which are appropriate for each process (and sub‐process).

A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection.

The proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected.

Typical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold.

The arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development.

This proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.

The use of aviation incident data to carry out aviation risk prediction is of great significance for improving the initiative of accident prevention and reducing the occurrence of accidents. Because of the nonlinearity and periodicity of incident data, it is challenging to achieve accurate predictions. Therefore, this paper aims to provide a new method for aviation risk prediction with high accuracy.

This paper proposes a hybrid prediction model incorporating Prophet and long short-term memory (LSTM) network. The flight incident data are decomposed using Prophet to extract the feature components. Taking the decomposed time series as input, LSTM is employed for prediction and its output is used as the final prediction result.

The data of Chinese civil aviation incidents from 2002 to 2021 are used for validation, and Prophet, LSTM and two other typical prediction models are selected for comparison. The experimental results demonstrate that the Prophet–LSTM model is more stable, with higher prediction accuracy and better applicability.

This study can provide a new idea for aviation risk prediction and a scientific basis for aviation safety management.

The innovation of this work comes from combining Prophet and LSTM to capture the periodic features and temporal dependencies of incidents, effectively improving prediction accuracy.

A mathematical model to describe the principle of layered manufacturing and layered fabrication error is presented in this paper. In this model, the layered manufacturing process is characterized by the model decomposition and material accumulation. A 3D design model is represented by a set of points with sequence functions to correlate the layered processing information. Iso‐sequence planes are defined as the processing layers to collect points with the same processing sequence and to define the material accumulation along its gradient direction. Examples of using the proposed model to describe the layered manufacturing to process flat and no‐flat surfaces and the description of the layered processing error are also presented.

The objective of this paper is to develop geometric algorithms and planning strategies to enable the development of a novel hybrid manufacturing process, which combines rapidly re‐configurable mold tooling and multi‐axis machining.

The presented hybrid process combines advantages of both reconfigurable molding and machining processes. The mold's re‐configurability is based on the concept of using an array of discrete pins. By positioning the pins, the reconfigurable molding process allows forming the mold cavity directly from the object's 3D design model, without any human intervention. After a segment of the part is molded using the reconfigurable molding process, a multi‐axis machining operation is used to create accurate parts with better surface finish. Geometric algorithms are developed to decompose the design model into segments based on the part's moldability and machinability. The decomposed features are used for planning the reconfigurable molding and the multi‐axis machining operations.

Computer implementation and illustrative examples are also presented in this paper. The results showed that the developed algorithms enable the proposed hybrid re‐configurable molding and multi‐axis machining process. The developed decomposition and planning algorithms are used for planning the reconfigurable molding and the multi‐axis machining operations. Owing to the decomposition strategy, more geometrically complex parts can be fabricated using the developed hybrid process.

This paper presents geometric analysis and planning to enable the development of a novel hybrid manufacturing process, which combines rapidly re‐configurable mold tooling and multi‐axis machining. It is expected that the proposed hybrid manufacturing process can produce highly customized parts with better surface finish, and part accuracy, with shorter build times, and reduced setup and tooling costs.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.