Assembly Of Parts Research Articles

Key deviation source diagnosis of complex thin-walled structures based on complex networks and weighted transfer entropy

Abstract There are many deviation sources in the assembly process of aircraft complex thin-walled structures. To get important factors that affect quality, it is crucial to diagnose the key deviation resources. The deviation transfer between deviation sources and assembly parts has the characteristics of small sample size, nonlinearity, and strong coupling, so it is difficult to diagnose the key deviation sources by constructing assembly dimension chains. Therefore, based on the deviation detection data, transfer entropy and complex network theory are introduced. Integrating the depth-first traversal algorithm with degree centrality theory, a key deviation diagnosis method for complex thin-walled structures is proposed based on weighted transfer entropy and complex networks. The application shows that key deviation sources that affect assembly quality can be accurately identified by the key deviation source diagnosis method based on complex networks and weighted transfer entropy.

MATHEMATICAL MODEL OF TECHNOLOGICAL ASSEMBLY PROCESSES
 IN MACHINE-BUILDING AND REPAIR PRODUCTION

In a logical representation, the technological process of assembling any mechanism or assembly, including the crank mechanism of the engine, can always be considered as one of the forms of displaying functional and constructive signs of searching for some objective function. (Research purpose) The research purpose is substantiating the effectiveness of original approaches to mathematical modeling of assembly processes in machine-building and repair industries. (Materials and methods) Conducted research using complete information about the modeling object as part of a unified system of mathematical modeling of assembly processes formed at the levels of abstraction of structure, logical and quantitative predicates. (Results and discussion) It has been shown that modern principles of mathematical modeling are based on the basic provisions of the theory of sets, graphs and mathematical logic. It was noted that of all the considered spaces of production systems describing assembly technological processes, the space of predicates of the mechanism and technological system is most closely related to the problem of ensuring a given accuracy of the closing link in a particular real dimensional chain. As a result of the assembly process, in general, parts or assemblies are transformed into a connected system of Cartesian bodies with a limited number of degrees of freedom. Mathematical modeling of assembly processes at the level of quantitative predicates is characterized by the least degree of response, but it gives a description in a more rigorous mathematical form that allows for a well-founded optimization of accuracy parameters and methods of performing technological techniques that provide unambiguous optimization. The applied value of modeling at the level of quantitative predicates is significantly higher than the description at the levels of structural and logical properties and relationships. (Conclusions) Mathematical modeling of assembly technological processes with a cybernetic approach at the levels of multiple, logical and quantitative predicates provides the best adequacy and applied significance in the case of building analytical models.

A method for chronic and semi-chronic microelectrode array implantation in deep brain structures using image guided neuronavigation

BackgroundAccurate targeting of brain structures for in-vivo electrophysiological recordings is essential for basic as well as clinical neuroscience research. Although methodologies for precise targeting and recording from the cortical surface are abundant, such protocols are scarce for deep brain structures. New methodWe have incorporated stable fiducial markers within a custom cranial cap for improved image-guided neuronavigation targeting of subcortical structures in macaque monkeys. Anchor bolt chambers allowed for a minimally invasive entrance into the brain for chronic recordings. A 3D-printed microdrive allowed for semi-chronic applications. ResultsWe achieved an average Euclidean targeting error of 1.6 mm and a radial error of 1.2 mm over three implantations in two animals. Chronic and semi-chronic implantations allowed for recording of extracellular neuronal activity, with single-neuron activity examples shown from one macaque monkey. Comparison with existing method(s)Traditional stereotactic methods ignore individual anatomical variability. Our targeting approach allows for a flexible, subject-specific surgical plan with targeting errors lower than what is reported in humans, and equal to or lower than animal models using similar methods. Utilizing an anchor bolt as a chamber reduced the craniotomy size needed for electrode implantation, compared to conventional large access chambers which are prone to infection. Installation of an in-house, 3D-printed, screw-to-mount mechanical microdrive is in contrast to existing semi-chronic methods requiring fabrication, assembly, and installation of complex parts. ConclusionsLeveraging commercially available tools for implantation, our protocol decreases the risk of infection from open craniotomies, and improves the accuracy of chronic electrode implantations targeting deep brain structures in large animal models.

Research on Design and Optimization of Large Metal Bipolar Plate Sealing for Proton Exchange Membrane Fuel Cells

The sealing system, as the most important load-bearing component, is a critical part of the stack assembly in a proton exchange membrane fuel cell (PEMFC). Currently, flat or single-peak sealing gaskets are commonly used for large metal bipolar plate sealing, which can easily cause problems such as significant internal stress and distortion displacement. In order to solve this problem, an innovative double-peak sealing gasket structure is proposed. Based on the Mooney–Rivlin constitutive model, the impact of the sealing material hardness, friction coefficient, and compression ratio on the sealing performance are investigated. Meanwhile, the double-peak seal is fabricated and assembled into a single fuel cell for testing. The results show that the sealing performance of a double-peak sealing gasket with extended wings has been optimized, and the maximum contact pressure on the upper and lower contact surfaces is 1.2 MPa and 0.67 MPa, respectively, which is greater than the given air pressure of 0.1 MPa. And the sealing effect is optimal with a 45 Shore A hardness rubber, a friction coefficient of 0.05, and an initial compression ratio of 35%. The simulation and experimental sealing performance of the sealing gasket under different compression ratios remain similar.

Infrared Thermography testingduring the welding process

Metal parts assembly by welding processes requires the performance of post-fabrication inspection to ensure the integrity of the weld seams. These inspections are timeconsuming and require the completion of welding operations before being performed. Implementation of an "in-situ inspection" to adapt or stop the process during a drift of it would allow to obtain a gain in the immobilization of the parts, to improve the quality of the assemblies, and to make the welding processes more reliable. Online monitoring of many manufacturing processes is of real interest to guarantee the quality of manufactured products.

Protein-Specific Signal Peptides for Mammalian Vector Engineering.

Expression of recombinant proteins in mammalian cell factories relies on synthetic assemblies of genetic parts to optimally control flux through the product biosynthetic pathway. In comparison to other genetic part-types, there is a relative paucity of characterized signal peptide components, particularly for mammalian cell contexts. In this study, we describe a toolkit of signal peptide elements, created using bioinformatics-led and synthetic design approaches, that can be utilized to enhance production of biopharmaceutical proteins in Chinese hamster ovary cell factories. We demonstrate, for the first time in a mammalian cell context, that machine learning can be used to predict how discrete signal peptide elements will perform when utilized to drive endoplasmic reticulum (ER) translocation of specific single chain protein products. For more complex molecular formats, such as multichain monoclonal antibodies, we describe how a combination of in silico and targeted design rule-based in vitro testing can be employed to rapidly identify product-specific signal peptide solutions from minimal screening spaces. The utility of this technology is validated by deriving vector designs that increase product titers ≥1.8×, compared to standard industry systems, for a range of products, including a difficult-to-express monoclonal antibody. The availability of a vastly expanded toolbox of characterized signal peptide parts, combined with streamlined in silico/in vitro testing processes, will permit efficient expression vector re-design to maximize titers of both simple and complex protein products.

Relationship between microstructure, and residual strain and stress in stainless steels in-situ alloyed by double-wire arc additive manufacturing (D-WAAM) process

In the field of welding, assembly of thick parts requires the base metal to be chamfered, followed by multipass welding. The process generates thermal, mechanical, and metallurgical phenomena resulting in the formation of residual strains and stresses. Strains must be limited, as they can cause misalignments during welding and leads to geometrical defects. Residual stresses are especially problematic in structural assemblies, as they add to the external stresses applied to the part during service, often reducing its in-service lifetime and increasing the risk of failure.The objective of this study is to use two stainless steel filler metals with different coefficients of thermal expansion (austenitic 304 L and ferritic 430) to reduce residual stresses and strains in the context of deposits produced by the Double-Wire Arc Additive Manufacturing process on 304 L baseplates. To achieve such a goal, the two filler are mixed in various proportions. After deposition and cooling, residual stresses are determined using neutron diffraction and contour method, strains are studied with profilometry. Microstructure is analyzed via EBSD, free dilatometry tests and XRD.We were able to reduce strains and stresses with a three-phase microstructure (austenite, ferrite, and martensite). The part that offers the best compromise (50% 430 - 50% 304 L filler on 304 L baseplate) exhibits a 67% reduction in strain, and stresses are reduced down to 40%, as compared to the 100% 304 L filler on 304 L baseplate part. The results obtained from neutron diffraction and contour method exhibit a close agreement, with a discrepancy standing below 100 MPa.

Thermal and Structural Response of Beam-end Shear Connections During a Large Compartment Fire Experiment

The role of steel connections is essential in structural fire design and analysis for steel-framed composite structures. The current structural design provisions provide strength reduction factors of load-carrying members and their end-connection elements at elevated temperatures, based on small-scale experiments under uniform heating conditions. The realistic thermal and structural evolution in member connections, especially as part of full-scale floor assemblies exposed to a large compartment fire, has not been well characterized. A large compartment fire experiment was recently conducted on a 9.1 m by 6.1 m composite floor assembly as part of a two-story steel-framed building. The test assembly had a total of ten shear-tab (fin-plate) connections subjected to combined fire and mechanical loading. This paper presents the measured thermal response of these connections to fire and comparison with the corresponding Eurocode 3 predictions with two methods (1) incorporating the beam bottom flange temperature at midspan and (2) the section factor method. The results show that the Eurocode 3 methods conservatively predict the maximum temperature during heating and the cooling rate but overestimate the high-temperature strength of connections when estimated using the section factor method, showing that the Eurocode 3 simplified approaches are not meant to provide the details of the failure mode for connections. This study suggests that estimating the strength of connections using strength reduction factors may not guarantee a safe structural fire design. In addition, this paper estimated the total axial force (from slab and beam) at the composite connection via using the strain gauge measurements close to the column bases which were not exposed to fire. It suggests realistic axial load and rotational demand on the shear connection due to restraints to thermal elongation or contraction of supported members should be considered in future design guidance as should designing and detailing the connections for ductility to withstand the inelastic deformation demands during the heating and the cooling phases.

Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation

Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs deep learning on a limited number of annotated images to identify a set of keypoints on the parts of interest. To compensate for network shortcomings and enhance accuracy we incorporated a Bayesian updating stage that leverages our detailed knowledge of the assembly part design. This Bayesian updating step refines the network output, significantly improving pose estimation accuracy. For this purpose, we utilized a subset of network-generated keypoint positions with higher quality as measurements, while for the remaining keypoints, the network outputs only serve as priors. The geometry data aid in constructing likelihood functions, which in turn result in enhanced posterior distributions of keypoint pixel positions. We then employed the maximum a posteriori (MAP) estimates of keypoint locations to obtain a final pose, allowing for an update to the nominal assembly trajectory. We evaluated our method on a 14-point snap-fit dash trim assembly for a Ford Mustang dashboard, demonstrating promising results. Our approach does not require tailoring to new applications, nor does it rely on extensive machine learning expertise or large amounts of training data. This makes our method a scalable and adaptable solution for the production floors.

Α Behavior Trees-based architecture towards operation planning in hybrid manufacturing

ABSTRACT In modern manufacturing, the capability of process scheduling and task allocation is a major feature for the proper organization of complex production schedules. More particularly, the case of human-robot collaboration within assembly lines is considered as a quite challenging field, where an efficient process scheduling can reduce products’ delivery times, increasing in parallel its quality. The purpose of this paper is to propose an approach focusing on operation planning for Human-Robot Collaborative processes that consist of many tasks and multiple resources, such as the assembly of large-scale parts. The implementation of the Human-Robot Operation Planning (HROP) module is presented, which aim at the allocation of multiple operations between multiple and different types of resources. This development’s main pillar is a dynamic decision-making logic that combines both constraints, that exclude resources from the evaluation, as well as mathematical criteria, that provide finally a specific solution. The HROP particularity is that it is developed under the Behavior Trees (BT) architecture. For the validation of the proposed approach, a case study under a real industrial environment of the automotive industry is presented, based on the assembly of large-scale parts, such as buses, in a hybrid cell of both human operators and multi-type robots.

Opracowanie skutecznych zespołów uszczelniających dla urządzeń naftowych w systemie wydobywczym ropy i gazu

The paper presents the results of many years of experience in studying the efficiency of sealing units in the conditions of their operation in the oil and gas production system, as well as the influence of technological parameters that ensure normal operation and protect them from failure due to leakage through the working surface of the seals in the friction zone. In addition, according to the results of practical studies, it has been established that, in order to improve the elastic properties, for example, of cup springs of a direct-flow valve, we calculated heat treatment of the operational values of spring compression for various material thicknesses. The work of the elastic elements of the packer was improved mainly by the design, and the optimal parameters for the development of the elastic element of the packer were determined. The study of the stress-strain state of the sealing element of packers during setting and improving its performance is of both practical and scientific interest. One of the factors that significantly affect operation of pumps is the fluid leakage in valve assemblies in order to identify the effect of the above parameters on tightness based on a preliminary theoretical analysis. Comparison of data during the study showed that the flow rate of work with low viscosities is greater than with high viscosities. The technological processes of hardening the parts of the shut-off assembly of X-mas tree valves, to increase their reliability, are considered. The dependence of the compression force of the spring and its deformation because of geometric parameters of the disk springs has been determined; it has been found that the residual deformation of the disk spring after collapse has a negative effect on tightness. Calculations show that the amount of leakage of the elastic elements in the sealing assembly should not exceed ≤0.309, since the seal at the value of elastic energy deformation provides a self-compacting mode. Thus, the degree of uneven pressing of the seat to the surface of the gate and its regulation to prevent leakage is practically determined.

Design, development, and testing of rice-husk fueled mixed-flow rice dryer for small-scale rice producer farmers

In underdeveloped nations, rice is frequently harvested at a relatively high moisture content and then dried under the open sun to a storage moisture content. However, direct UV exposure, rain, dust, premature drying, contamination, and open sun drying are all associated with problems like poor grains, discolorations, and cracking. Commercial mechanical dryer utilization is not practical in remote regions due to the absence of electrical service, high startup costs, and ongoing operating expenses. In this work, a pilot mixed-flow rice drier that ran on rice husk energy and solar energy to power the driving fan was designed, produced, and tested. The major parts of the dryer assembly that were conceived and created were the combustion chamber, heat exchanger, and mixed-flow dryer. The results of the trial revealed that the combustion chamber's average temperature was 347.3 °C, the drying air at the heat exchanger output, where the drying chamber was attached, was 63.3 °C, and the combustion chamber and heat exchanger's respective efficiencies were 52.4% and 14.9%. The mixed-flow dryer powered by rice has a significantly lower operating cost than a comparable dryer powered by electricity, petrol, and diesel. The study makes it evident that such a reasonably priced and effective rice dryer might significantly help small-scale rice producers to extend the storage duration and maintain the quality of their product.

Development of Information System for Evaluation of Work and Prediction of Countermeasures for Problems in Assembly Work

In this study, we develop an information system to evaluate work and predict countermeasures for resolving problems to continuously promote the work process and design in parts assembly work. The information system is constructed using a subsystem including image processing to detect the locations of the tips and joints of the fingers of a worker’s hands and the locations of parts. To predict the problems and generate countermeasures to resolve the challenges, three types of data structures are constructed and they are connected related to works, evaluation values, the causes of the problems, and the countermeasures for problems in the works. We explain the characteristics of the developed system and perform the system in the actual assembly process to evaluate the system’s effectiveness.

Effect of Vibrator Parameters and Physical Characteristics of Parts on Conveying Velocity

Automation is generally employed in the area of orienting, lifting, and moving parts for production in industries including automotive, electronic, food, and packaging. With the help of automation, it is possible to reduce the manufacturing time and labour required. The most adaptable tools for feeding small, designed pieces during part assembly are vibratory feeders. Industries have been effectively using vibratory feeders for more than 30 years, indicating that such technology is advanced. Although research in this area has not been lacking, a fundamental understanding of the interactions between a part’s physical characteristics and the various vibratory feeder operating parameters in relation to optimal performance, defined as conveying a part with maximum stability and maximum velocity, remains lacking in linear feeders. While several papers discuss the effect of vibratory parameters (excitation frequency and amplitude of vibration) and the coefficient of friction, the effect of characteristics of part (l/w ratio and mass) is neglected. In this work, the effect of these factors on the conveying velocity of prismatic parts made of aluminium and brass on a horizontal track without inclination was determined, and an attempt was made to develop a predictive model based on the above factors. Using Taguchi’s design of experiments (DOE), an L16 orthogonal array was designed. A response table for the signal-to-noise ratio has yielded optimal values for each parameter taken into consideration. ANOVA predicted frequency as the most influential parameter, followed by the coefficient of friction. The regression analysis yields an R2 value of 99.3 % for aluminium and 98.7 % for brass. The results of the regression model and random experiments show a high correlation of 91.66 %. This model is required to set the desired conveying velocity of parts so that continuous flow can be maintained in automated assembly or packaging industries.

Method of individual selection of parts for selective assembly of electrohydraulic servo drives

A method for selecting parts and assembly units (hereinafter briefly referred to as parts) in sets for assembling a batch of electrohydraulic servo drives is presented, based on the measured (individual) mechanical and hydraulic characteristics of the parts. The preparatory stage is described: construction of a mathematical model of the product, determination of the parameters of parts and adjustments that have the greatest impact on the parameters of end products, formation of a system for assessing the quality of products in the batch, mathematical formulation of the task of selecting parts. The task of selecting parts is formulated as the assignment problem in the maximin formulation. A deterministic algorithm for solving the problem is presented, based on the branch and bound method and taking into account the fact that adjustments are made during the assembly of drives. A significant reduction in the number of steps (iterations) when searching for the optimal distribution of parts across assembly sets achieved due to the chosen mathematical formulation of the task, as well as due to the chosen strategy of selecting parts in the next assembly set. We suggest that an assembly set should include, first of all, the most troublesome parts the parameters of which do not allow obtaining a set, the projected quality indicator of which will be higher than the established value. At the same time, the negative impact of these parts on the product parameters is compensated by combining them with other parts in one assembly set. The results of mathematical modeling of assembling 992 drives with random formation of assembly sets and with individual selection of parts are presented. Modeling shows that as a result of implementing individual selection of parts in accordance with the described method, a significant reduction in the spread of parameters of end products within batches can be expected, as well as improvements in functional performance of the products. The presented method of individual selection of parts can be used in the assembly of various products.

3D Assembly Completion

Automatic assembly is a promising research topic in 3D computer vision and robotics. Existing works focus on generating assembly (e.g., IKEA furniture) from scratch with a set of parts, namely 3D part assembly. In practice, there are higher demands for the robot to take over and finish an incomplete assembly (e.g., a half-assembled IKEA furniture) with an off-the-shelf toolkit, especially in human-robot and multi-agent collaborations. Compared to 3D part assembly, it is more complicated in nature and remains unexplored yet. The robot must understand the incomplete structure, infer what parts are missing, single out the correct parts from the toolkit and finally, assemble them with appropriate poses to finish the incomplete assembly. Geometrically similar parts in the toolkit can interfere, and this problem will be exacerbated with more missing parts. To tackle this issue, we propose a novel task called 3D assembly completion. Given an incomplete assembly, it aims to find its missing parts from a toolkit and predict the 6-DoF poses to make the assembly complete. To this end, we propose FiT, a framework for Finishing the incomplete 3D assembly with Transformer. We employ the encoder to model the incomplete assembly into memories. Candidate parts interact with memories in a memory-query paradigm for final candidate classification and pose prediction. Bipartite part matching and symmetric transformation consistency are embedded to refine the completion. For reasonable evaluation and further reference, we design two standard toolkits of different difficulty, containing different compositions of candidate parts. We conduct extensive comparisons with several baseline methods and ablation studies, demonstrating the effectiveness of the proposed method.

Overcoming limitations of polymer additive manufacturing for snap-fit joint design and part assembly

Overcoming limitations of polymer additive manufacturing for snap-fit joint design and part assembly

Ultra-fast 3D printing of assembly—free complex optics with sub-nanometer surface quality at mesoscale

Complex-shaped optical lenses are of great interest in the areas of laser processing, machine vision, and optical communications. Traditionally, the processing of complex optical lenses is usually achieved by precision machining combined with post-grinding or polishing, which is expensive, labor-intensive and difficult in the processing of ultra-complex optical lenses. Additive manufacturing is an emerging technology that provides significant advantages in producing highly intricate optical devices. However, the layer-by-layer method employed in such manufacturing processes has resulted in low printing speeds, as well as limitations in surface quality. To address these challenges, we apply tomographic volumetric printing (TVP) in this work, which can realize the integrated printing of complex structural models without layering. By coordinating the TVP and the meniscus equilibrium post-curing methods, ultra-fast fabrication of complex-shaped lenses with sub-nanometric roughness has been achieved. A 2.5 mm high, outer diameter 9 mm spherical lens with a roughness value of RMS = 0.3340 nm is printed at a speed of 3.1 × 104 mm3 h−1. As a further demonstration, a complex-shaped fly-eye lens is fabricated without any part assembly. The designed spherical lens is mounted on a smartphone’s camera, and the precise alignments above the circuit board are captured. Upon further optimization, this new technology demonstrates the potential for rapid fabrication of ultra-smooth complex optical devices or systems.

FINITE ELEMENT ANALYSIS AND TOPOLOGY OPTIMIZATION OF A HIGH-PERFORMANCE LOWER PROSTHETIC LIMB

Due to the recent development of additive manufacturing technologies, it is now possible to achieve rapid fabrication of fully functional customized products with high geometric complexity and without the constraints of traditional manufacturing techniques, such as machining. In the current paper, a lower prosthetic limb with the knee joint was designed according to the international standards and the recommendations from existing literature. Finite element analyses were performed to investigate the mechanical behavior of the prosthetic limb and detect the stress concentration regions under realistic operation conditions. For this analysis, Nickel alloy 718 was employed as a construction material. The applied loads were derived for a human body weight of 75kg after an extensive literature review. Then, design optimization was applied, employing design for additive manufacturing (DfAM) techniques in order to minimize the number of assembly parts and improve the shape of the object. Moreover, topology optimization processes were performed in order to develop a lightweight prosthetic limb and achieve a mass reduction of 67% compared to the original design. It is worth mentioning that, this remarkable weight reduction was attained without compromising the structural integrity of the part, as the factor of safety was calculated at 1.22.

A new method for laser grooving titanium alloy with the assist of a hybrid of gas jet and waterjet

High efficiency and low thermal damage grooving with a good surface quality is critical for the positioning and assembly of titanium alloy parts, which are widely used in aviation manufacturing and shipbuilding industry. In this paper, a new method of laser grooving by the assist of a hybrid of gas jet and waterjet was proposed, and Ti6Al4V titanium alloy was used as the specimen. The gas jet was applied co-axially to the laser, while the waterjet was applied off-axially. The effects of relative direction between the waterjet and laser traverse, the gas/water pressure, and the waterjet nozzle diameter on the groove characteristics and heat-affected zone were investigated. The surface macro morphology and microstructure were also examined, as well as the compositional change. Results show that larger grooves with a better quality can be fabricated and the range of heat-affected zone is still limited below 100 μm, when compared with other laser grooving methods. In addition, reverse-grooving creates a deeper and cleaner groove than the other two directions used in this study. The largest groove size can be obtained when the water pressure, gas pressure and nozzle diameter are 10 MPa, 0.1 MPa, and 0.4 mm, respectively. Moreover, the metallographic photo and spectrogram obtained by scanning electron microscope (SEM) indicate that fine acicular martensite and titanium oxidation can be formed near the groove. This work provides a new approach for improving the waterjet assisted laser processing quality and helps understand its mechanism. All the data sets supporting the results are included within the article.