Advances in Manufacturing最新文献

Production logistics (PL) is considered as a critical factor that affects the efficiency and cost of production operations in discrete manufacturing systems. To effectively utilize manufacturing big data to improve PL efficiency and promote job shop floor economic benefits, this study proposes a PL trajectory analysis and optimization decision making method driven by a manufacturing task data chain (MTDC). First, the manufacturing task chain (MTC) is defined to characterize the discrete production process of a product. To handle manufacturing big data, the MTC data paradigm is designed, and the MTDC is established. Then, the logistics trajectory model is presented, where the various types of logistics trajectories are extracted using the MTC as the search engine for the MTDC. Based on this, a logistics efficiency evaluation indicator system is proposed to support the optimization decision making for the PL. Finally, a case study is applied to verify the proposed method, and the method determines the PL optimization decisions for PL efficiency without changing the layout and workshop equipment, which can assist managers in implementing the optimization decisions.

An online detection technology must be developed for realizing the real-time control of friction stir welding. In this study, the three-dimensional force exerted on a material during friction stir welding was collected synchronously and the relationship between the forces and welding quality was investigated. The results indicated that the fluctuation period of the traverse force was equal to that of the lateral force during the stable welding stage. The phase difference between two horizontal forces was π/2. The values of the horizontal forces increased with welding speed, whereas their amplitudes remained the same. The proposed force model showed that the traverse and lateral forces conformed to an elliptical curve, and this result was consistent with the behavior of the measured data. The variational mode decomposition was used to process the plunge force. The intrinsic mode function that represented the real fluctuation in the plunge force varied at the same frequency as the spindle rotational speed. When tunnel defects occurred, the fluctuation period features were consistent with those obtained during normal welding, whereas the ratio parameter defined in this study increased significantly.

In this research, a method employing micro-extrusion was designed to produce the micro-scaled barrel-shaped parts with complex geometrical features to study the feasibility of the proposed microforming method and its grain size effect on the formability of the complicated internal features in terms of deformation behavior, material evolution, accuracy of dimensions and final components quality. The results reveal that the deformation behavior is highly affected by grain size and becomes unpredictable with increased grain size. In addition, assembly parameters including feature dimension, tolerance and coaxiality also vary with grain size, and the variation of grain size needs to be accommodated by different assembly types, viz., clearance fit or transition fit. From the microstructural evolution aspect, it was identified there were two dead zones and four shear bands, and the formation of these deformation zones was barely affected by the variation in grain size. Though bulges, cracks, and fracture induced voids were observed on the surface of the final components, tailoring the microstructure of the working material with finer grains could significantly avoid these defects. This study advances the understanding of forming microparts by extrusion processes and provides guidance for microforming of similar microparts.

The mixing of powders is a highly relevant field under additive manufacturing, however, it has attracted limited interest to date. The in-situ mixing of various powders remains a significant challenge. This paper proposes a new method utilizing a static mixer for the in-situ mixing of multiple powders through the laser-based directed energy deposition (DED) of functionally graded materials. Firstly, a powder-mixing experimental platform was established; WC and 316L powders were selected for the mixing experiments. Secondly, scanning electron microscopy, energy dispersive spectroscopy, and image processing were used to visually evaluate the homogeneity and proportion of the in-situ mixed powder. Furthermore, powder-mixing simulations were conducted to determine the powder-mixing mechanism. In the simulations, a powder carrier gas flow field and particle mixing were employed. Finally, a WC/316L metal matrix composite sample was produced using laser-based DED to verify the application potential of the static mixer. It was found that the static mixer could adjust the powder ratio online, and a response time of 1–2 s should be considered when adjusting the ratio of the mixed powder. A feasible approach for in-situ powder mixing for laser-based DED was demonstrated and investigated, creating the basis for functionally graded materials.

This work aims to present and explore thermal management techniques for the wire arc additive manufacturing (WAAM) of IN718 components. Excessive heat can be mitigated via air or water cooling. In this study, the material was deposited under four different heat-input conditions with air or water cooling. In air cooling, the layer is deposited in a normal atmospheric air environment, whereas with water cooling, the material is deposited inside a water tank by varying the water level. To validate the air and water cooling thermal management techniques, IN718 single-pass and multilayer linear walls were deposited using the bidirectional gas metal arc welding based WAAM setup under four different heat input conditions. During the deposition of single layers, the temperature profiles were recorded, and the geometric and microstructural features were explored. For multilayer wall structures, the mechanical properties (hardness, tensile strength, and elongation) were determined and assessed using the corresponding microstructural features explored through scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD) analyses. The microstructure observed through SEM analysis in the building direction was found to be nonhomogenous compared with that in the deposition direction. Moreover, water cooling was found to govern bead characteristics, such as wall width and height. The grain size and anisotropy of the mechanical properties also decreased in the water-cooled case. Hence, water cooling is an economical and efficient method to mitigate excessive heat accumulation in WAAM-deposited IN718.

Miniature scalpels are mainly used in microsurgeries such as ophthalmic and cardiovascular surgeries. The size of a miniature scalpel is only a few millimeters, and the precision of the blade shape is high, which makes production of miniature scalpels extremely difficult. This study proposes a new sharpening process for grinding miniature scalpels on a four-axis machine tool. A post-processing algorithm for a four-axis grinding machine based on a kinematics model is established. We then propose a corresponding parameter calibration method for the parameters used in the kinematics model. Because of possible errors in the parameter calibration, a contour-based error compensation method is proposed for accurate adjustments to the edge shape following grinding. This can solve the problem of large deviations between the actual edge shape after grinding and the ideal edge shape. The effectiveness of the proposed process planning and error compensation method is verified experimentally, and the grinding process parameters of the miniature scalpel are optimized to improve its surface processing quality. The sharpness of the optimized miniature scalpel is less than 0.75 N, and the blade shape is symmetrical, which meets the technical requirements of miniature scalpels.

The automatic cutting of intersecting pipes is a challenging task in manufacturing. For improved automation and accuracy, this paper proposes a model-driven path planning approach for the robotic plasma cutting of a branch pipe with a single Y-groove. Firstly, it summarizes the intersection forms and introduces a dual-pipe intersection model. Based on this model, the moving three-plane structure (a description unit of the geometric characteristics of the intersecting curve) is constructed, and a geometric model of the branch pipe with a single Y-groove is defined. Secondly, a novel mathematical model for plasma radius and taper compensation is established. Then, the compensation model and groove model are integrated by establishing movable frames. Thirdly, to prevent collisions between the plasma torch and workpiece, the torch height is planned and a branch pipe-rotating scheme is proposed. Through the established models and moving frames, the planned path description of cutting robot is provided in this novel scheme. The accuracy of the proposed method is verified by simulations and robotic cutting experiments.

Accurate intelligent reasoning systems are vital for intelligent manufacturing. In this study, a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters. The developed system consists of a self-learning algorithm with an improved particle swarm optimization (IPSO) learning algorithm, prediction model determined by an improved case-based reasoning (ICBR) method, and optimization model containing an improved adaptive neural fuzzy inference system (IANFIS) and IPSO. Experimental results showed that the IPSO algorithm exhibited the best global convergence performance. The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods. The IANFIS model, in combination with IPSO, enabled the optimization of multiple objectives, thus generating optimal milling parameters. This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.

In recent years, green concepts have been integrated into the product iterative design in the manufacturing field to address global competition and sustainability issues. However, previous efforts for green material optimal selection disregarded the interaction and fusion among physical entities, virtual models, and users, resulting in distortions and inaccuracies among user, physical entity, and virtual model such as inconsistency among the expected value, predicted simulation value, and actual performance value of evaluation indices. Therefore, this study proposes a digital twin-driven green material optimal selection and evolution method for product iterative design. Firstly, a novel framework is proposed. Subsequently, an analysis is carried out from six perspectives: the digital twin model construction for green material optimal selection, evolution mechanism of the digital twin model, multi-objective prediction and optimization, algorithm design, decision-making, and product function verification. Finally, taking the material selection of a shared bicycle frame as an example, the proposed method was verified by the prediction and iterative optimization of the carbon emission index.

This paper presents a predictive defect detection method for prototype additive manufacturing (AM) based on multilayer susceptibility discrimination (MSD). Most current methods are significantly limited by merely captured images, disregarding the differences between layer-by-layer manufacturing approaches, without combining transcendental knowledge. The visible parts, originating from the prototype of conceptual design, are determined based on spherical flipping and convex hull theory, on the basis of which theoretical template image (TTI) is rendered according to photorealistic technology. In addition, to jointly consider the differences in AM processes, the finite element method (FEM) of transient thermal-structure coupled analysis was conducted to probe susceptible regions where defects appeared with a higher possibility. Driven by prior knowledge acquired from the FEM analysis, the MSD with an adaptive threshold, which discriminated the sensitivity and susceptibility of each layer, was implemented to determine defects. The anomalous regions were detected and refined by superimposing multiple-layer anomalous regions and comparing the structural features extracted using the Chan-Vese (CV) model. A physical experiment was performed via digital light processing (DLP) with photosensitive resin of a non-faceted scaled V-shaped engine block prototype with cylindrical holes using a non-contact profilometer. This MSD method is practical for detecting defects and is valuable for a deeper exploration of barely visible impact damage (BVID), thereby reducing the defect of prototypical mechanical parts in engineering machinery or process equipment via intellectualized machine vision.