International Journal of Advanced Manufacturing Technology最新文献

Abstract

This paper aims to develop a simulation-based chatter prediction system using relative entropy—Kullback-Leibler divergence (KLD), and the NC program can be modified to become non-chatter. Chatter is one of the major concerns when machining mechanical components on a CNC machine. In general, the majority of the previous research methods achieved non-chatter stable machining by assigning the appropriate machining parameters: (1) spindle speed, (2) feed rate, and (3) depth of cut based on the generated SLD (stability lobe diagram). Non-chatter stable machining can also be accomplished by manually adjusting the spindle override percentage on the operation panel or the values in the CNC controller via networking once chatter is detected during the machining processes. The creation of SLD must consider two essential parameters: cutting force coefficients (CFCs) and frequency response function (FRF). The CFCs can be obtained from cutting experiment data related to a paired tool and workpiece, and the FRF can be calculated from the tapping test experiment. Then, the CFCs and FRF are stored in the database of the developed system. The simulation-based chatter prediction calculates the KLD value based on the relativity of the dynamic cutting force and the static cutting forces so as to predict whether there is chatter in the NC program or not. The NC program can be adjusted to become non-chatter if there is chatter predicted. The proposed method has been successfully verified through on-site machining, showing very promising achievement.

Abstract

Niobium (Nb) is a transition metal commonly used as an alloying element for increasing strength, toughness, corrosion resistance, and other properties of steel and superalloys. Pure Nb, however, is a very interesting metal for its excellent superconductivity. This makes it suitable for producing superconducting magnets and devices for particle acceleration systems and particle physics research (e.g., superconducting resonant cavities). In this work, the production of Nb by the Laser-Based Powder Bed Fusion (PBF-LB/M, also known as Laser Powder Bed Fusion or LPBF) process was examined. Manufacturing parameters were investigated to achieve additively manufactured parts with a relative density higher than 99.5% and showing a down-skin surface roughness in the range of 20–70 μm, depending on the inclination angle. Studies related to the limiting angle of self-supported Nb parts were also conducted, and innovative non-contact supporting structures were successfully developed. These allowed to creation of parts with very small overhang angles, without compromising the downward-facing surfaces; indeed at the same time, the as-built surface finish was improved.

For weight reduction, multi-material designs comprising metal and fiber-reinforced plastic (FRP) components in vehicle body structures have been increasingly used. However, the commonly used resistance spot welding (RSW) technology for car body assembly cannot be employed to join sheet metal and FRPs, limiting the use of FRPs. To solve this problem, a novel resistance insert spot welding (RISW) technique was developed in this work for RSW of steel parts and FRP structure parts made by injection molding. Small inserts were developed by using finite element method and experiments that may be welded to different micro-alloyed and dual-phase sheet steels using the projection welding method. The usual flange width of original equipment manufacturers could be kept unchanged. Using the developed insert and welding parameters, the maximum temperature in the FRPs surrounding the inserts was limited to 255 °C, minimizing the damage to polyamide 6 (PA6) material (with 40 wt% glass fiber). A weldability range between 2.5 and 7 kA could be achieved. The joining strength of RISW between a micro-alloyed HC340 steel in 0.75 mm and 1.5 mm thickness and a 2.5 mm/3.0 mm PA6-GF40 material is 20 to 80% higher than self-piercing riveting (SPR). For high-speed loading, RISW strength increases by 39 to 56% further. Finally, RISW was successfully applied to an FRP–steel roof-frame sub-assembly that consists of 19 simultaneously integrated inserts, achieving 10% weight reduction.

The thermal characteristics of the motorized spindle significantly affect the machining accuracy and efficiency, and many thermal models have been developed to investigate the factors that affect the spindle thermal characteristics. However, the thermomechanical coupling of the bearings with constant pressure preload is rarely considered in the present works. Thus, this paper developed a transient temperature model of motorized spindle to study the influence of the radial thermal stress on the heat generation of the constant pressure preloaded bearings. In this research, an analytical thermal stress model was established first by simplifying the components of the bearings into a rotating ring geometry to calculate the thermal stress loaded on the bearings. Meanwhile, a transient temperature model of the motorized spindle was established based on the finite element method (FEM). Then, the analytical model was integrated into the spindle transient thermal model, so that the heat generated by bearings and the motorized spindle temperature can be revised constantly, through the iterative calculation between these two models. Finally, verification experiments with different work conditions clarify that the proposed transient thermal characteristic model of the motorized spindle is valid, and the study shows that it is necessary to consider the bearing heat generation induced by the radial thermal stress when the spindle runs at a high speed.

Abstract

To improve the reliability of the connection of various steel/aluminum dissimilar materials, an integrated optimization method of material-structure-process-performance is proposed to realize the optimal design of process parameters. First, the Johnson–Cook material model and failure fracture model are established to ensure the accuracy of the simulation model. Then, an integrated simulation analysis for self-piercing riveted joint forming and tensile mechanical performance is established considering the residual information of the joint forming process. Compared with the experimental results, the accuracy of the established model is higher than the model without considering the residual information. Finally, a hybrid sequence approximate optimization that comprehensively considers the forming quality and tensile mechanical performance is constructed to determine the optimal riveting parameters. Compared with the initial design, the maximum pull-out force, maximum shear force, and maximum peeling force of the optimized design for DC01 and 5754 rivets are increased by 35.66%, 8.6%, and 22.43%, respectively, and the maximum pullout force, maximum shear force and maximum peeling force of the optimized design for HC280 and 5754 rivets are increased by 1.490%, 1.292%, and 6.867%, respectively. Moreover, the accuracy and efficiency of self-piercing riveting process design are improved.

In industrial applications of laser welding of aluminum alloys, it is often a challenge to find optimal process parameters to produce welded joints of good quality. This study investigates the effect of changes in welding parameters on the resulting welding defects and tensile properties of laser-welded aluminum alloy joints. A high-power disk laser was used for welding 2.54 mm thick AA6061-T6 sheets with four different sets of process parameters. X-ray computed microtomography and full-field microhardness mapping were first used to characterize the as-welded joints. Surface strain mapping based on digital image correlation and a two-mirror optical setup was applied to tensile testing of welded joints up to ductile failure. It was found that the fusion zone of all four laser-welded joints has similar microhardness levels and spatial distributions. Small pores were detected in all four weld joints, except one had a few large pores. Both microstructural heterogeneities and surface geometric irregularities were found to induce highly non-uniform local tensile deformation in laser-welded joints. One set of laser welding parameters was identified to produce the aluminum welds with the best tensile properties even though its weld joint may contain a few relatively large pores.

This paper aims to investigate the contribution of tool wear and process parameters to the shape defects induced by the micro-machining of glassy polymers such as polycarbonate. An experimental approach is proposed in this study using an instrumented orthogonal cutting configuration. Diamond inserts have been considered as a cutting tool and have been subjected to different levels of accelerated wear. The worn tools were first characterized to identify the wear mechanisms that occurred on the active cutting zone and were then used to perform orthogonal cutting experiments on polycarbonate. Micro-cutting depth values (3–20 µm) were considered to respect the micro-machining configuration. Chip morphology, cutting forces, and machined surfaces’ topography have been acquired to analyze and assess the effectively removed material depth regarding the theoretical cutting depth for each worn tool. Results show that the increase of the cutting edge radius is the main wear mechanism that occurred in the diamond inserts. This tool wear evolution was found to be the most influential factor on the induced shape defect by increasing the elastic deformation of polycarbonate rather than its shear when using micro-cutting depths, which induces a spring-back of polycarbonate after cutting.

Abstract

Ultrasonic additive manufacturing (UAM) is an advanced joining technique that utilizes ultrasonic vibrations to bond layers of metal foil together. UAM offers several benefits over traditional manufacturing methods, including enhanced design flexibility and reduced material waste, and its potential applications in various industries such as aerospace, automotive, and biomedical engineering are being actively explored. The study employs a nanoindentation apparatus to investigate the effect of the UAM process on the local mechanical properties of the bonded interface, along with changes in microstructure, which were investigated using scanning electron microscopy and electron back-scattered diffraction. The results revealed a significant correlation between material hardness and local plasticity. EBSD has revealed that the grain size distribution of Al far from the interface contains 57% of the grains less than 3 µm in size, while at the interface this number rises to approximately 78%, indicating that the average grain size decreases as it approaches the interface. This result is consistent with nanoindentation results that demonstrated a gradual change in the hardness of Al foil far from the interface to close to the interface (the maximum penetration depth near the interface was 500 nm less than far from the interface). Both EBSD and nanoindentation disclose the effect of work hardening close to the interface, which is related to dislocation accumulation with a density of (8.6times {10}^{-10} {{text{cm}}}^{-2}) beneath the interface. The consistency of hardness and Young’s modulus with the pole figures and microscopic images demonstrated that plasticity flow and fine grain distribution would only occur in the vicinity of the interface in the softer metal region. Although the harder metal did not exhibit plasticity or recrystallization, the hardness, and Young’s modulus map indicated the formation of a layer of small grains close to the interface on the aluminum side owing to strain hardening and dynamic recrystallization.

This work addresses the problem of the development of a robotic system for the picking of parts cut by a CNC machine and the optimization of the sequencing of this picking process. An automated parts collection system is optimized to reduce the time required to perform the task of both picking and the subsequent classification by the type of part. The automated picking system, which is located at the end of a cutting machine, uses a robot equipped with an additional axis to expand its working space. Therefore, in this proposal, the industrial equipment necessary to automate this process is designed and the process to be optimized is computationally modeled. In particular, three discrete optimization algorithms are analyzed, with different evolution strategies and operators, but all of them are free of specific configuration parameters. The whole process is shown in this research, from the design of the procedure to the design of the tool, the algorithm selection, and elements validation. Finally, the first steps towards its industrial implementation are presented, and the hypothesis behind this project is validated.

This paper describes a novel end-to-end approach for automatic welding-robot programming based on a product-process-resource (PPR) model, for one-of-a-kind manufacturing systems. Traditionally, the information needed to program a welding robot is processed and transferred along the manufacturing organisation’s value chain by using several stand-alone digital systems which require extensive human input and high skill to operate. A PPR model is proposed through this research as a platform for storing and processing the necessary information along the value chain seamlessly. Unlike existing approaches which make use of complex algorithms to automatically identify the weldment seams, the approach suggested in this research makes use of information already digitalised by design engineers under the form of ISO 2553:2019 compliant weldment annotations. Hence, the PPR model contains the weldment annotations; it enables the automatic programming of welding robots and reduces human input down to a few minutes only. The applicability in manufacturing of the theoretical concept is demonstrated through technical implementations tested in the laboratory and on the value chain of an engineering-to-order (ETO) industrial partner involved in the metal fabrication industry. The experiments were conducted by creating several products using the proposed artefact. Experiments show that automatic programming of welding robots can be achieved using PPR models. The conducted experiments showed a reduction of about 80% in human input measured in terms of time, when using the proposed solution. The reduction of the human input can free up skilled labour resource which ETO SMEs can reallocate to other tasks.