Assembly Of Parts Research Articles

Plant virus community structuring is shaped by habitat heterogeneity and traits for host plant resource utilisation.

Host plants provide resources critical to viruses and the spatial structuring of plant communities affects the niches available for colonisation and disease emergence. However, large gaps remain in the understanding of mechanisms that govern plant-virus disease ecology across heterogeneous plant assemblages. We combine high-throughput sequencing, network, and metacommunity approaches to test whether habitat heterogeneity in plant community composition corresponded with virus resource utilisation traits of transmission mode and host range. A majority of viruses exhibited habitat specificity, with communities connected by key generalist viruses and potential host reservoirs. There was an association between habitat heterogeneity and virus community structuring, and between virus community structuring and resource utilisation traits of host range and transmission. The relationship between virus species distributions and virus trait responses to habitat heterogeneity was scale-dependent, being stronger at finer (site) than larger (habitat) spatial scales. Results indicate that habitat heterogeneity has a part in plant virus community assembly, and virus community structuring corresponds to virus trait responses that vary with the scale of observation. Distinctions in virus communities caused by plant resource compartmentalisation can be used to track trait responses of viruses to hosts important in forecasting disease emergence.

Foundation models assist in human–robot collaboration assembly

Human–robot collaboration (HRC) is a novel manufacturing paradigm designed to fully leverage the advantage of humans and robots, efficiently and flexibly accomplishing customized manufacturing tasks. However, existing HRC systems lack the transfer and generalization capability for environment perception and task reasoning. These limitations manifest in: (1) current methods rely on specialized models to perceive scenes; and need retraining the model when facing unseen objects. (2) current methods only address predefined tasks, and cannot support undefined task reasoning. To avoid these limitations, this paper proposes a novel HRC approach based on Foundation Models (FMs), including Large Language models (LLMs) and Vision Foundation Models (VFMs). Specifically, a LLMs-based task reasoning method is introduced, utilizing prompt learning to transfer LLMs into the domain of HRC tasks, supporting undefined task reasoning. A VFMs-based scene semantic perception method is proposed, integrating various VFMs to achieve scene perception without training. Finally, a FMs-based HRC system is developed, comprising perception, reasoning, and execution modules for more flexible and generalized HRC. The superior performances of FMs in perception and reasoning are demonstrated by extensive experiments. Furthermore, the feasibility and effectiveness of the FMs-based HRC system are validated through an part assembly case involving a satellite component model.

Position and orientation estimation method based on 3D digital morphology contour registration

Abstract Accurately and quickly obtaining the positions and orientations of mechanical parts based on the digital morphologies of mechanical parts are the key to achieving efficient and accurate assembly of mechanical parts. However, due to poor robustness and compactness in extracting digital morphology contours of mechanical parts, the accuracy of assembly positions and orientations obtained by using digital morphology contours cannot meet the requirements of high-precision assembly. Therefore, this paper proposes a position and orientation estimation method based on 3D digital morphology contour registration. This method extracts and optimizes digital morphology contours of mechanical parts and obtains the assembly positions and orientations by using an improved iterative closest point method to register the extracted digital morphology contours with those of the mechanical parts in assembly targets with desired positions and orientations. Experiments are conducted using mechanical parts from the ABC dataset and inertial confinement fusion micro-target. From the experimental results, when using the assembly positions and orientations obtained through the method proposed in this paper to assemble mechanical parts, it can achieve translation absolute errors of 8 μm, 5 μm, and 9 μm along the X-, Y-, and Z-axes, respectively. Similarly, the angular absolute errors in rotations around the Z-, Y-, and X-axes can be less than or equal to 0.16°, 0.15°, and 0.11°, respectively. The results prove that the proposed method in this paper exhibits high computational efficiency and accuracy, providing an effective approach for digital assembly of mechanical parts.

Worker-centered evaluation and redesign of manufacturing tasks for ergonomics improvement using axiomatic design principles

Humans are considered the most valuable resource in manufacturing systems thanks to their craftsmanship, dexterity, and autonomy significantly affecting productivity, quality, and the overall company competitiveness. This paper introduces the SAGE (Systematic Approach to Generating Ergonomic Manufacturing tasks) methodology, a structured approach based on Axiomatic Design principles to integrate Human Factors evaluation early in the operations design phase and redesign manufacturing tasks to improve operators well-being. The primary objective is to mitigate discomfort and safety risks that often lead to musculoskeletal disorders, absenteeism, and production delays. SAGE provides a comprehensive framework for assessing ergonomic aspects of manufacturing tasks and identifying the need for redesign. It offers a detailed set of Functional Requirements (FRs) for reference, assesses FR satisfaction, evaluates task complexity using the Independence Axiom, and examines the intensity of FR satisfaction through the Information Axiom. The methodology includes specific implementation guidelines, ensuring its applicability across diverse manufacturing contexts. Its effectiveness is demonstrated through a large-scale parts assembly case study inspired by the bus and coach industrial sector, where a production engineer evaluated a windows assembly task and identified ergonomic design interventions. A comparative analysis with other relevant methods is finally presented, highlighting the approach's effectiveness.

Numerical investigation on machining of additively manufactured CFRP composite with different build orientations and layer widths

Additive manufacturing (AM) is now a widely researched manufacturing technology in the past two decades to adopt in industry for its added advantage of customization and adopting complex geometries with comparatively low buy-to-fly ratio. Carbon fiber reinforced polymer (CFRP) composite has found applications in different high-performance industries for its greatest benefit of high strength-to-weight ratio. Additive manufacturing of CFRP composite (AM-CFRP) opens up the possibility of enhancing mechanical strength using different printing orientations and enables producing complex shapes maintaining sustainable manufacturing perspective. However, Limitation of AM parts of having surface irregularities and questionable dimensional accuracies. To use AM-CFRP parts in high precision assembly or industrial applications, post processing machining is often required to meet the customers’ specification of geometrical tolerances and acceptable surface finish. In this study, the influence of AM parameters on machinability of AM-CFRP composite has been evaluated using finite element analysis (FEA) based numerical simulation. The slot milling operation was simulated with a tungsten carbide end milling tool and AM-CFRP workpiece with four different printing directions, i.e., 0°-90°, 45°-135°, 0°-90°-45°-135°, two different layer widths, i.e., 50 µm and 100 µm, and two in-fill patterns, i.e., solid and perforated structures. The machinability of the 3D printed CFRP has been analyzed based on cutting forces, stress at first contact and maximum stress generation, and temperature increases at the interface during slot milling of AM-CFRP under different AM parameters. Evolution of failure mechanisms of AM-CFRPs under various machining conditions, such as, delamination, matrix rupture etc., have been discussed and chip and burr formation mechanisms have been analyzed. Finite Element analysis (FEA) package ABAQUS/Explicit was used to model 3D micro slot milling operation of AM-CFRP workpiece with appropriate damage and constitutive models, such as, damage initiation, progression and cohesion in adjacent passes and layers.

An investigation of mixed-model assembly line balancing problem with uncertain assembly time in remanufacturing

In recent decades, remanufacturing has emerged as an effective way to address resource crises and environmental pollution issues. Unlike traditional manufacturing, remanufacturing production is filled with various variable factors, especially in the assembly phase. Due to changes in part types, quality conditions, and assembly methods, the assembly time becomes highly uncertain. Assembly line balancing is a key challenge to achieve the stable operation of remanufacturing system. This study proposes an evaluation method for remanufacturing assembly time and establishes a multi-objective mathematical model for balancing remanufacturing mixed-model assembly (RMMA) line. The evaluation method utilizes the Fuzzy Graphical Evaluation Review Technique (FGERT) network to predict expected assembly time for each operation. The balancing model aims to optimize remanufacturing takt time and comprehensive balance rate (CBR). To effectively solve this model, an adaptive double-layer genetic algorithm (ADGA) is designed, where layer I ensures production efficiency and layer II optimizes assembly line balance. Finally, an assemble example of high-pressure common rail fuel pumps (HCRFP) is used to validate the effectiveness of the proposed method. The results demonstrate notable improvements compared to traditional single-product assembly (TSPA) line in scenarios with workstation numbers 4, 5, 6, and 7. Specifically, the production takt time is reduced by 4.19% to 9.56%, and CBR is enhanced by approximately 50%. Further comparison with three other classic algorithms confirms the superiority of ADGA. Additionally, it is observed that remanufacturability (proportion of remanufactured parts) has a significant impact on assembly performance. As remanufacturability increases, both takt time and CBR increase, reaching their maximum values when remanufacturability is around 0.5.

A multi-stage approach for desired part grasping under complex backgrounds in human-robot collaborative assembly

Human-robot collaborative assembly can leverage the unique capabilities of humans and robots to provide a flexible and efficient way for complex tasks. In this context, robots are expected to be endowed with the perception ability to collaborate with humans. For flexible processes, robotic grasping for a desired assembly part from variable multi-parts is essential but remains challenging, especially in situations of complex backgrounds including disordered placement, occlusion, similarity, and large-scale differences in size. To improve the perception and decision-making abilities of robots in collaborative assembly, firstly, a multi-stage approach integrating 2D part recognition and 6D pose estimation with perspective-focusing is proposed for desired part grasping under complex backgrounds. Secondly, to efficiently and accurately recognize a desired part from variable multi-parts under complex backgrounds, based on RGB data, an enhanced detector is adopted, which lays the foundation for the subsequent pose estimation. Thirdly, based on point cloud data, a pose estimation method based on perspective-focusing is proposed to enhance the success rate and efficiency of pose acquisition. Additionally, a modular autonomous robotic grasping system is designed. Finally, taking decelerator assembly as a case, the proposed approaches are comprehensively validated and compared on a real collaborative robot platform. Under complex backgrounds with occlusion, our approach shows stable performance with an average grasping success rate of 90.0 % and an average cycle time of 6.4175 s. Our work is expected to improve robots’ perception and decision-making capabilities while further reducing the cycle time of collaborative assembly tasks.

Optimized Selective Assembly using Hungarian Algorithm

Assembly of discrete parts guided by hardware design specification constitutes the final phase in product manufacturing. In the course of mass production of components, mating parts with geometric or dimensional deviation from their intended design can be made acceptable by the identification of suitable pairs after analysing design fit and tolerance limits. By transforming this application-specific problem into a unified mathematical model, an optimal solution can be achieved that minimizes the rejection of non-conforming fabricated parts. Regardless of the type and range of a design fit, the problem can be mapped into a matrix using a ranking function defined by the user. The ranking function is modifiable as per the user requirements and may vary based on the selection criteria for an assembly. Based on the type of ranking function used, the tabulated matrix is solved using the Hungarian minimization/maximization algorithm, which is a powerful combinatorial optimization algorithm that solves the classical assignment problem in mathematics. This approach ensures maximum number of suiting pairs as well as nominal suiting of parts with each other resulting in high-quality products and maximum utilization of fabricated resources.

NP-Collabs: Investigating Counterion-Mediated Bridges in the Multiply Phosphorylated Tau-R2 Repeat.

Tau is an intrinsically disordered (IDP) microtubule-associated protein (MAP) that plays a key part in microtubule assembly and organization. The function of tau can be regulated by multiple phosphorylation sites. These post-translational modifications are known to decrease the binding affinity of tau for microtubules, and abnormal tau phosphorylation patterns are involved in Alzheimer's disease. Using all-atom molecular dynamics simulations, we compared the conformational landscapes explored by the tau R2 repeat domain (which comprises a strong tubulin binding site) in its native state and with multiple phosphorylations on the S285, S289, and S293 residues, with four different standard force field (FF)/water model combinations. We find that the different parameters used for the phosphate groups (which can be more or less flexible) in these FFs and the specific interactions between bulk cations and water lead to the formation of a specific type of counterion bridge, termed nP-collab (for nphosphate collaboration, with n being an integer), where counterions form stable structures binding with two or three phosphate groups simultaneously. The resulting effect of nP-collabs on the tau-R2 conformational space differs when using sodium or potassium cations and is likely to impact the peptide overall dynamics and how this MAP interacts with tubulins. We also investigated the effect of phosphoresidue spacing and ionic concentration by modeling polyalanine peptides containing two phosphoserines located one-six residues apart. Three new metrics specifically tailored for IDPs (proteic Menger curvature, local curvature, and local flexibility) were introduced, which allow us to fully characterize the impact of nP-collabs on the dynamics of disordered peptides at the residue level.

Design and Prototyping of a Collaborative Station for Machine Parts Assembly

Collaboration between humans and machines is the core of the Industry 5.0 paradigm, and collaborative robotics is one the most impactful enabling technologies for small and medium Enterprises (SMEs). In fact, small batch production and high levels of product customization make parts assembly one of the most challenging operations to be automated, and it often still depends on the versatility of human labor. Collaborative robots, for their part, can be easily integrated in this productive paradigm, as they have been specifically developed for coexistence with human beings. This work investigates the performance of collaborative robots in machine parts assembly. Design and research activities were carried out as a case study of industrial relevance at the i-Labs industry laboratory, a pole of innovation that is briefly introduced at the beginning of the paper. A fully-functional prototype of the cobotized station was realized at the end of the project, and several experimental tests were performed to validate the robustness of the assembly process as well as the collaborative nature of the application.

Detection of Piston Ring Deficiency in The Assembly of Automotive Ball Joint and Tie Rod End Parts

In today's intensely competitive environment, businesses strive to optimize production efficiency to reduce costs, increase profitability, and ensure customer satisfaction. This focus on efficiency and quality enables businesses to operate more effectively, gain a competitive advantage in the market, and move towards sustainable growth. This study uses image processing techniques to detect missing segments in the assembly of ball joints automatically. In the automotive industry, performing quality control of critical components before assembly and detecting and classifying the defective ones is essential. Many quality control methods can be applied with existing technologies. This paper proposes an automatic real-time control based on image processing techniques to detect ball joint missing segments, a common defect in the automotive industry. In the company, operators perform defect detection by visual inspection. In this system, production continues in cases where the operator cannot detect the defect. This system aims to detect the errors made by the operator during the assembly operations and provide instant feedback. The developed system uses OpenCV library algorithms that are highly accurate in detecting defects in manual assembly processes so that missing components are removed from the production chain, and production quality is significantly improved. Accuracy is over 94% when identifying missing segments, about 30% better than traditional methods. In tests, 1200 ball joints were run through the system, resulting in 1150 defects being correctly identified and removed from the production line. Accuracy is high thanks to the application of various image processing techniques such as grayscale conversion, edge detection, and shape recognition. This also provides real-time feedback to the operator so the system can reduce detection and response time from 15 seconds to 5 seconds. This increases production speed and reduces the error rate in manual assembly processes by 20%. This paper also highlights the potential of image processing technology in manufacturing. It will contribute to improved quality control mechanisms to increase the reliability and efficiency of production lines in the automotive industry.

La dissection en kinésithérapie : le lien entre l’anatomie iconographique et le mouvement

Dissection is at the origin of knowledge of the organs of the human body. It requires specific places and conditions, which are incompatible with the pedagogical principles essential to initial discovery. Anatomy makes up for this by dematerializing the analytical presentation of organs through text and drawing. These standardized presentations have become the sole source of knowledge for most professionals. Their pedagogical objective is to focus on the characteristics deemed necessary and sufficient to gain knowledge of the isolated organ. They are readily available, and feature caricatures or diagrams to reinforce the illusion of concrete reality. The graphic assembly of bone parts is the basis of the proposed reconstruction. It reverses the order of appearance of anatomical parts during dissection. Physiological knowledge studies the functioning of the organs revealed by dissection, according to analytical principles. These conditions of acquisition of anatomical and physiological knowledge are satisfactory for illustrating, in functional anatomy or biomechanics, the caricatured principles of the functioning of this intellectually reconstructed anatomy. The physiotherapist's view, focused on putting anatomical elements into motion, differs from that of the surgeon, for whom structural norms take precedence. Real knowledge of human movement, which forms the basis of rehabilitation practice, requires the inclusion in the reconstruction of gliding planes that can only be perceived through dissection. Knowledge of the actual course of movement requires mechanical knowledge of these interstitial tissues, which is currently inaccessible.

Similar assembly state discriminator for reinforcement learning-based robotic connector assembly

In practice, the process of robot assembly in an unstructured environment faces difficulties due to the presence of unpredictable environmental errors related to vision and pose. Therefore, to minimize the uncertain environmental errors during the robotic assembly process in an unstructured environment, several studies have considered a reinforcement learning (RL)-based approach. However, if assembly parts are changed, it becomes difficult to apply RL-based methods to assemble various parts because additional learning may be required. Especially in the case of connector assembly, fine-tuning is essential because the shape changes depending on the type of connector. In this study, we propose a similar assembly state discriminator that transforms the state information (force, velocity, and RGB image) of reinforcement learning into generalized features to respond various types of connector assembly tasks. This method processes the data to include essential features for assembly regardless of connector type. By learning the RL model with the processed data using this method, the RL model trained for a specific connector can be efficiently applied to other types of connectors without fine-tuning. The assembly success rate for the 7 types of connectors (Harting, HDMI, USB, power, air jack, banana plug and PCIE) using the proposed method was demonstrated to be over 96 %.

On the application of buoyancy correction in the cup test measurement

The water vapour permeability of building materials is often measured using the cup method. One of usual corrections applied to the measured data is correction for the ambient air buoyant force acting on the cup assembly during weighing, the so-called ‘buoyancy correction’. Although the mathematical formula for the correction is well known, there are two distinct ways for its application to the cup test, which depend on the air permeability and flexibility of the tested sample. The magnitude of the correction can be twice different for the two approaches. This study, at first theoretically describes the use of buoyancy correction for cups with different samples. Then, presents results of the measurement of the air pressure inside cups with different samples which support the theoretical assumptions. Finally, based on numerical simulation, were established limits of use of the calculated buoyancy correction. Many samples of building materials are enough air permeable (air permeance higher than 1 × 10−10 kg/(m2sPa)) or flexible to maintain the air pressure and density in the cup close to ambient conditions. Cups with these samples can be corrected by applying the buoyancy correction to the volume of the air-impermeable parts of the cup assembly. If the air permeance of the sample is lower, the possible residual error after the application of correction can be ±3–7 mg/dm2 of the cup mouth area. The application of buoyancy correction can reduce the measurement time, especially for samples with a higher vapour resistance.

IMPROVING THE QUALITY OF SMALL PIECES MADE BY RAPID PROTOTYPING

Rapid Prototyping is the rapid fabrication of a part, model, or physical assembly that was designed using any design software (Autodesk Inventor Professional in this case), which is a CAD (Computer-Aided Design) application that helps in the creation of a digital prototype. The creation of the part, model, or assembly is usually completed using the manufacture of additives or more commonly known as 3D printing. The paper presents a 3D design, 3D printing, and verifying the actual dimensions of the printed „washer”. The purpose of this paper is to verify that printing errors are controllable and can be reduced. The paper contains and explains all the mandatory steps required to do this experiment and the laboratory equipment that was used to do this.

Microbial Leakage through Three Different Implant-Abutment Interfaces on Morse Taper Implants In Vitro.

The objective of this study was to evaluate microbial leakage by means of genome counts, through the implant-abutment interface in dental implants with different Morse taper abutments. Fifty-six samples were prepared and divided in four groups: CMC TB (14 Cylindrical Implants-14 TiBase Abutments), CMX TB (14 Conical Implants-14 TiBase Abutments), CMX PU (14 Conical Implants-14 Universal Abutment) and CMX U (14 Tapered Implants-14 UCLA Abutments). Assemblies had their interface submerged in saliva as the contaminant. Samples were subjected either to thermomechanical cycling (2 × 106 mechanical cycles with frequency of 5 Hz and load of 120 N simultaneously with thermal cycles of 5-55 °C) or thermal cycling (5-55 °C). After cycling, the contents from the inner parts of assemblies were collected and analyzed using the Checkerboard DNA-DNA hybridization technique. Significant differences in the total genome counts were found after both thermomechanical or thermal cycling: CMX U > CMX PU > CMX TB > CMC TB. There were also significant differences in individual bacterial counts in each of the groups (p < 0.05). Irrespective of mechanical cycling, the type of abutment seems to influence not only the total microbial leakage through the interface, but also seems to significantly reflect differences considering individual target species.

What if fractography does not tell us anything? – Failure of a side seal retainer

Abstract A fractured transition side seal retainer, together with an intact piece for reference, was received from the client on December of 2023. The subject part fractured next to the fastening hole, liberating a long ligament that was never found but reportedly caused some damage to hot gas path components downstream of a large gas turbine engine. It is speculated that the subject side seal retainer fractured due to high-stress, not fully reversed bending high cycle fatigue failure. It is recommended to make the design sturdier by considering dampening the long free length of the part, and by specifying a larger fillet radius in the initiation area. Also, over-torquing upon assembly of the subject part should be avoided.

A 3D-printed, dynamic, patient-specific knee simulator

Purpose3D-printed devices proved their efficacy across different clinical applications, helping personalize medical treatments. This paper aims to present the procedure for the design and production of patient-specific dynamic simulators of the human knee. The scope of these simulators is to improve surgical outcomes, investigate the motion and load response of the human knee and standardize in-vitro experiments for testing orthopedic devices through a personalized physical representation of the patient’s joint.Design/methodology/approachThis paper tested the approach on three volunteers. For each, a patient-specific mathematical joint model was defined from an magnetic resonance imaging (MRI) of the knee. The model guided the CAD design of the simulators, which was then realized through stereolithography printing. Manufacturing accuracy was tested by quantifying the differences between 3D-printed and CAD geometry. To assess the simulator functionality, its motion was measured through a stereophotogrammetric system and compared with the natural tibio-femoral motion of the volunteers, measured as a sequence of static MRI.FindingsThe 3D-printing accuracy was very high, with average differences between ideal and printed parts below ± 0.1 mm. However, the assembly of different 3D-printed parts resulted in a higher average error of 0.97 mm and peak values of 2.33 mm. Despite that, the rotational and translational accuracy of the simulator was about 5° and 4 mm, respectively.Originality/valueAlthough improvements in the production process are needed, the proposed simulators successfully replicated the individual articular behavior. The proposed approach is general and thus extendible to other articulations.

Dimensional Quality Assessment for Assembly Part of Prefabricated Steel Structures Using a Stereo Vision Sensor

Dimensional Quality Assessment for Assembly Part of Prefabricated Steel Structures Using a Stereo Vision Sensor

How to improve efficiency for prefabricated component production scheduling with worker allocation and order outsourcing?

Prefabricated building is an advanced building mode that is actively promoted in China owing to its high-efficiency, energy-saving and labour-saving characteristics. However, because of the limited production capacity and production resources of the final assembly parts production plant, it is usually impossible for a factory to accept all orders. Therefore, the factory will consider outsourcing part of the orders to reduce delay penalties. In addition, the allocation of workers in the production process will significantly affect the processing time of orders. Regarding those two issues, a production scheduling integrated optimization model, considering worker allocation and order outsourcing, is established to maximize net profit. To address the complexity of problem solving caused by the NP-hard nature of this problem, a hybrid improved genetic–accelerated iterative greedy search algorithm is developed. Finally, a numerical example is delivered to verify that the proposed algorithm has improved solution quality and convergence.