Assembly Of Parts Research Articles

Enzyme Biosensor Based on 3D-Printed Flow-Through Reactor Modified with Thiacalixarene-Functionalized Oligo (Lactic Acids)

Electrochemical enzyme biosensors are extensively utilized in clinical analysis and environmental monitoring, yet achieving effective enzyme immobilization while maintaining high activity remains a challenge. In this work, we developed a flow-through enzyme biosensor system using a 3D-printed flow-through electrochemical cell fabricated from commercially available poly (lactic acid). After modification with thiacalixarene-functionalized oligo (lactic acids) (OLAs), the material enabled efficient immobilization of uricase on the inner surface of a replaceable reactor of the cell. Swelling and hydrolytic stability of OLAs in cone, partial cone, and 1,3-alternate conformations were studied, with 1,3-alernate conformation demonstrating superior stability and enzyme immobilization performance. The use of OLAs enhanced immobilization efficiency by over 30% and protected the reactor from swelling, hydrolytic degradation, and enzyme loss. The biosensor was validated for amperometric uric acid determination, with a screen-printed carbon electrode modified with carbon black and Prussian Blue. This modification reduced the cathodic potential for uric acid detection to –0.05 V. The biosensor exhibited a linear detection range of 10 nM to 30 μM with a detection limit of 7 nM, and it performed effectively in artificial urine and synthetic blood plasma. The novel cell design, featuring easy assembly and low-cost replaceable parts, makes this biosensor a promising candidate for routine clinical analysis and other practical applications.

Assembly strategy for inclined-holes based on vision and force

Abstract In robotic part assembly, the unpredictability of part orientations, uncontrollable contact forces, and challenges in maintaining horizontal assembly holes constrain the practical application of robotic assemblies. These factors frequently lead to misalignment, resulting in assembly failures or excessive contact forces that can damage workpieces or robots, thereby increasing economic costs. Consequently, this paper investigates compliant assembly using machine vision, focusing on cylindrical hole assembly. It examines posture estimation for inclined holes. A hole positioning strategy is formulated, comprising a visual initial location stage and a force feedback searching stage, which achieves low-cost and precise positioning of hole parts. To overcome the jamming issues associated with traditional impedance control strategies during the hole insertion stage, a variable compliance center impedance control strategy is introduced. Four experimental setups involving different-sized pegs and holes for inclined holes were developed, the assembly success rate is 94% and 92% when the inclinations of the hole is 30° and 45°, and the peg-in-hole gap is 0.2 mm. The findings confirm that this strategy can accurately complete robotic peg-in-hole automated assembly, address the challenge of inclined hole angles, and improve the precision and efficiency of fully automated robotic assembly with reduced environmental constraints, supporting cost-effective, precise assembly in industrial applications.

Participatory research towards the control of snakebite envenoming and other illnesses in a riverine community of the Western Brazilian Amazon.

Riverine communities face various health problems, which involve geographical and cultural barriers to accessing care, in addition to a lack of financial investments in services aimed at these communities, resulting in a process of invisibility for the population living in these regions. In this scenario, the significant burden of snakebite envenoming (SBE) highlights the need for participatory research to address ways to minimize this situation. Thus, this study aimed to describe the priority health problems identified by this population and the ranking of SBEs in that context, mapping solutions according to the local reality. This study was conducted in Limeira, a riverine community located in Tabatinga, in the extreme Western Brazilian Amazonia, on the borders with Peru and Colombia. The research lasted approximately one year, from 2021 to 2022.It is a participatory study that followed three steps: baseline assessment of the community, community assembly, and final data analysis. The study included a total of 42 participants in the sociodemographic survey, which served as the basis for the subsequent stages of data collection. Of these 42 individuals, 32 participated in the qualitative interviews, and 20 took part in the community assembly. Participants emphasized snakebite envenoming as a significant health issue, though not the only one, and reported frequent encounters with snakes, underscoring its severity as a concern. The qualitative analysis identified three main themes: Snakebites in the Community, which focused on personal experiences with snakes; Common Health Problems, which addressed other health issues faced by community members; and Community Defining Solutions, which discussed strategies and solutions proposed by the community to address these challenges. Improvements in health care delivery to populations living in Amazonian communities are possible with the judicious use of tested integrated interventions, particularly when the community identifies various concurrent health problems. SBE control programs in remote areas of the Brazilian Amazon should be planned with a multidisciplinary and intercultural approach, preferably integrated with broader interventions that address the population's needs for a range of health issues.

Construction and Characterization of MoClo-Compatible Vectors for Modular Protein Expression in E. coli.

Cloning methods are fundamental to synthetic biology research. The capability to generate custom DNA constructs exhibiting predictable protein expression levels is crucial to the engineering of biology. Golden Gate cloning, a modular cloning (MoClo) technique, enables rapid and reliable one-pot assembly of genetic parts. In this study, we expand on the existing MoClo toolkits by constructing and characterizing compatible low- (p15A) and medium-copy (pBR322) destination vectors. Together with existing high-copy vectors, these backbones enable a protein expression range covering a 500-fold difference in normalized fluorescence output. We further characterize the expression- and burden profiles of each vector and demonstrate their use for the optimization of growth-coupled enzyme expression. The optimal expression of adhE (encoding alcohol dehydrogenase) for ethanol-dependent growth of Escherichia coli is determined using randomized Golden Gate Assembly, creating a diverse library of constructs with varying expression strengths and plasmid copy numbers. Through selective growth experiments, we show that relatively low expression levels of adhE facilitated optimal growth using ethanol as the sole carbon source, demonstrating the importance of adding low-copy vectors to the MoClo vector repertoire. This study emphasizes the importance of varying vector copy numbers in selection experiments to balance expression levels and burden, ensuring accurate identification of optimal conditions for growth. The vectors developed in this work are publicly available via Addgene (catalog #217582-217609).

Design and Development of Point Focus Integrated Linear Fresnel Solar Collector for 1kW Thermal Power Generation

A linear Fresnel collector is a type of line focus solar collector that uses flat reflectors to concentrate sunlight upon a fixed receiver. Although the existing constraints of linear Fresnel collector make its adoption in industries difficult, this technology offers a number of major advantages over conventional collectors. The objective of this study is to improve the performance of linear Fresnel collectors by integrating line and point focus technologies. A prototype for combined focus technology that is capable of producing up to 1 kW of thermal power has been designed and developed. A mathematical model has been developed to aid in optical design. The model is simulated in MATLAB, and the model’s output serves as the basis for geometric specification. The results of the model are validated with the existing work that shows a good agreement. SolidWorks software is used to design the collector’s assembly and component parts. The line focus reflector has a width of 0.15 m and a length of 2 m. The point focus reflector has 0.13 m length and 0.13 m width. The diameter of the line focus receiver is calculated to be 0.10 m.

Advancements in Golden Gate Cloning: A Comprehensive Review.

Researchers have dedicated efforts to refining genetic part assembly techniques, responding to the demand for complex DNA constructs. The optimization efforts, targeting enhanced efficiency, fidelity, and modularity, have yielded streamlined protocols. Among these, Golden Gate cloning has gained prominence, offering a modular and hierarchical approach for constructing complex DNA fragments. This method is instrumental in establishing a repository of reusable parts, effectively reducing the costs and proving highly valuable for high-throughput DNA assembly projects. In this review, we delve into the main protocol of Golden Gate cloning, providing refined insights to enhance protocols and address potential challenges. Additionally, we perform a thorough evaluation of the primary modular cloning toolkits adopted by the scientific community. The discussion includes an exploration of recent advances and challenges in the field, providing a comprehensive overview of the current state of Golden Gate cloning.

МОДЕЛЬ УПРАВЛІННЯ ЯКІСТЮ ТЕХНІЧНОЇ ПІДГОТОВКИ ВИГОТОВЛЕННЯ КОРПУСІВ БПЛА

Nowadays, the production of unmanned aerial vehicles (UAVs) outside of serial production is an urgent task. There is a need for the manufacture of such devices of various schemes, sizes and intended purpose. Aircraft-type UAVs (monoplanes and biplanes) are more difficult to manufacture. As a rule, they are single-use devices. Therefore, such devices should have a simple design, be light enough, have minimal radio visibility and a number of other properties that are necessary for conventional flying apparatus. Composites (glass and carbon plastics) are materials that largely meet the specified requirements. But for these materials, it is necessary to use special structural and manufacturing solutions (SMS) during the technical preparation of their production (TPP) (production of aerodynamic surfaces, assembly and joining of parts, aggregates into a structure). Quite a lot of such solutions are known, but their optimal selection and combination of individual SMS in the UAV depends significantly on the purpose of the UAV, the volume of production, production conditions and, in general, on the production goals.The purpose of this work is to develop a mathematical model for quality management of technical preparation for the production of UAVs. It is the quality of the technical preparation of the production that determines the quality of the manufactured products and the effect of the production itself.The work gives a general diagram of UAV manufacturing processes. The inextricable connection between the design and technological parts of technical training was noted. The multi-iteration process of developing optimal production processes, which depend on its various goals, is shown. The systematicity, compactness, and reasonableness of the solutions are determined by taking into account many properties of technical processes and the conditions of their implementation. The consideration of possibilities in computer-aided design is sufficiently substantiated. For its implementation, a complex model of quality management of technical training is required. The qualitative approach to model synthesis meets the conditions of complexity and reasonableness.As an example of multi-variability, the SMS of the manufacture of the wing (bearing surfaces) and their connection with the fuselage is considered.The original feature of the proposed mat model is the use as a control parameter of the weighting factor of one or another property of the SMS, which depends on the set goal.The mechanism of distortion of given aerodynamic shapes of bearing surfaces, which are made of polymer composites, when they are supported by various structural elements (spars, stringer frames, fittings) is described. Such residual deformations lead to deterioration of aerodynamic quality and strength.The scientific novelty of the work consists in the synthesis of a mat model of quality management of technical preparation for the manufacture of UAVs. The practical value of the work is determined by the given number of examples of SMS variants of different levels.

Monocular Vision-Based 3D Reconstruction Method for Parts in Opto-Mechanical Assembly

Abstract In automated assembly, accurately capturing multi-dimensional parameters of parts is essential. Traditional methods include teaching methods or laser-based approaches, with the former relying on experience for pre-calibration and the latter being costly. This paper proposes a low-cost, monocular vision-based method for 3D reconstruction in opto-mechanical assembly. Taking the laser crystal component, including the laser gain medium and its heat sink, in a solid-state laser as an example, a monocular camera is firstly mounted on a robot and captures images of parts from specific angles. Due to the reflective properties of metal surfaces, some image information is lost. To address this problem, a 2D gamma function-based adaptive illumination correction algorithm is then proposed. Next, the improved SIFT algorithm is used to extract and match feature points from the collected images. Subsequently, the SFM approach generates a sparse point cloud, which is then densified using the CMVS/PMVS algorithm. Finally, the Poisson surface reconstruction algorithm is used to complete the 3D reconstruction of the laser crystal heat sinks. Experimental results indicate that the adaptive correction algorithm for non-uniform illumination images can extract more feature points and improve the number of matches. The reconstructed 3D model has high accuracy, providing important technical support for the next step in achieving automated opto-mechanical assembly.

Study Explores Interaction Between Bit Whirl and High-Frequency Torsional Oscillations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23989, “The Nature of the Interaction Between Bit Whirl and High-Frequency Torsional Oscillations,” by Andreas Hohl, SPE, and Armin Kueck, Baker Hughes, and Vincent Kulke, TU Braunschweig, et al. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference. _ Understanding the interaction of high-frequency torsional oscillations (HFTO) with other vibrational phenomena is essential to develop effective HFTO mitigation strategies. While coupling with axial vibrations and stick/slip already have been studied extensively, the interaction of HFTO with lateral vibrations has received less attention. The complete paper analyzes this interaction based on a bit/rock interaction model that accounts for the superimposed movement of whirl and HFTO at the bit. The new bit model provides a physics-based explanation of why bit backward whirl and torsional vibrations cannot be observed simultaneously. Introduction The torsional movement of HFTO is localized to the lower part of the bottomhole assembly (BHA). The amplitudes along the distance from the bit are scaled by complex mode shapes with a comparably small wavelength. This deflection of the BHA leads to high tangential acceleration and dynamic torsional torque loads. The self-excitation mechanism of HFTO can be modeled as a drilling torque that decreases when the bit rotary speed increases. The nonlinear drilling torque characteristic can be identified from field data. Because both stick/slip and HFTO correspond to a torsional movement of the drilling system, and both are excited by the rock-cutting process, they interact strongly. The mitigation strategies and optimal operational parameters to manage HFTO are, thus, strongly linked to the understanding of this interaction. In this work, the interaction of HFTO and bit backward whirl was analyzed and modeled to further interpret this observation. Bit backward whirl is a complex lateral movement of the bit. The understanding of the interaction between HFTO and backward whirl drives improvements of vibration-mitigation strategies and bit design to mitigate HFTO or bit backward whirl. Modeling of the Cutting Forces To study the interaction of whirl and HFTO in more detail, a simplified bit cutting-force model is needed. For a common polycrystalline-diamond-compact (PDC) bit, the cutting torque of the bit results from the rock-cutting process. The cutting process results in cutting forces on each individual cutter. For simpler modeling, the cutting forces are assumed to be proportional to the normal force on each cutter. The proportionality factor between the normal force and the cutting force at a cutter is called cutter aggressiveness and is dependent on the velocity of the cutter. The radial distance between the geometric center of the bit and a cutter, multiplied by the cutting force, results in the portion of the cutting torques of each cutter. These add up to the overall bit torque. The three central variables in the torque calculations are the normal force, cutting speed, and cutter aggressiveness.

Surface Nanocrystallization and Improvement of the Mechanical and Tribological Properties of AISI 304 Steel Using Multi-Pass Nanostructuring Burnishing.

Owing to their high producibility and resistance to corrosion, austenitic chromium-nickel steels are widely used in the chemical, petroleum, and food industries. However, their significant disadvantage lies in their poor structural performance, which cannot be improved by heat treatment. This significantly limits the usability of these steels in parts of machines that operate under friction loads. Hardening can be achieved by decreasing the size of grains and applying deformation-induced martensitic transformation. Nanostructuring burnishing (NSB) may be one of the technologies suited for producing parts of tribological assemblies with enhanced operating characteristics. Nanostructuring burnishing using a sliding indenter is being developed as a method of industrial surface nanocrystallization through severe plastic deformation used in the mechanical machining of various types of parts. This article investigates the possibility of enhancing the mechanical and tribological properties of nanocrystallized surfaces of austenitic steels, which are formed through nanostructuring burnishing using a tool with a natural diamond spherical indenter and a change in sliding speed from 40 to 280 m/min with one, three, and five passes. Increasing the tool sliding speed makes surface nanostructuring machining of big parts highly effective. This paper aims to establish the influence exerted by the sliding speed and number of indenter passes on the formation of a nanocrystalline structure, as well as on the modification of microhardness and residual stresses, texture, and tribological properties of the surface layer in the nanostructuring burnishing of AISI 304 steel. Transmission microscopy and microdurometry, 3D-profilometry, and tribological tests of surfaces nanocrystallized with the "ball-on-disk" scheme with dry and lubricated friction established the optimal values of speed and number of passes for a spherical indenter in nanostructuring burnishing.

PhysFiT: Physical-aware 3D Shape Understanding for Finishing Incomplete Assembly

Understanding the part composition and structure of 3D shapes is crucial for a wide range of 3D applications, including 3D part assembly and 3D assembly completion. Compared to 3D part assembly, 3D assembly completion is more complicated, which involves repairing broken or incomplete furniture that miss several parts with a toolkit. Given an incomplete assembly, 3D assembly completion seeks to identify its missing parts from multiple candidates, determine their poses, and produce complete assembly that is well-connected, structurally stable, and aesthetically pleasing. This task necessitates not only specialized knowledge of part composition but, more importantly, an awareness of physical constraints, i.e., connectivity, stability, and symmetry. Neglecting these constraints often results in assemblies that, although visually plausible, are impractical. To address this challenge, we propose PhysFiT, a physical-aware 3D shape understanding framework. This framework is built upon attention-based part relation modeling and incorporates connection modeling, simulation-free stability optimization and symmetric transformation consistency. We evaluate its efficacy on 3D part assembly and 3D assembly completion, a novel assembly task presented in this work. Extensive experiments demonstrate the effectiveness of PhysFiT in constructing geometrically sound and physically compliant assemblies.

The Product and Process Quality Level Analysis in the Automotive Elements Production Management

Abstract The article presents the results of research carried out in the chosen company producing components for automotive companies. The main objective of the research was to analyze the quality level in the production of assembly parts for the automotive industry with regard to customer requirements, carried out by using the product FMEA and process FMEA methods. The research were conducted based on results of the company quality control and calculations have been conducted with applying Excel program. Results of data analysis presented in the paper indicate the main areas connected with occurrence of nonconformities that affected the final quality of the product. Analysis of research results using the FMEA method allowed to find not only the source of quality problems in the production process. It also allowed us to indicate recommendations for system improvements. The recommended actions not only allow for streamlining the production process, but also affect the quality level of the product at every stage of its production, which can be successfully found in other companies in the automotive industry.

Specific inhibition and disinhibition in the higher-order structure of a cortical connectome.

Neurons are thought to act as parts of assemblies with strong internal excitatory connectivity. Conversely, inhibition is often reduced to blanket inhibition with no targeting specificity. We analyzed the structure of excitation and inhibition in the MICrONS $mm^{3}$ dataset, an electron microscopic reconstruction of a piece of cortical tissue. We found that excitation was structured around a feed-forward flow in large non-random neuron motifs with a structure of information flow from a small number of sources to a larger number of potential targets. Inhibitory neurons connected with neurons in specific sequential positions of these motifs, implementing targeted and symmetrical competition between them. None of these trends are detectable in only pairwise connectivity, demonstrating that inhibition is structured by these large motifs. While descriptions of inhibition in cortical circuits range from non-specific blanket-inhibition to targeted, our results describe a form of targeting specificity existing in the higher-order structure of the connectome. These findings have important implications for the role of inhibition in learning and synaptic plasticity.

Autonomous ecologies of construction: Collaborative modular robotic material eco-systems with deep multi-agent reinforcement learning

To address the unprecedented challenges of construction pressurized by the global climate crisis, housing shortage, and growing shortage of skilled labor, this research presents a radical shift in the construction lifecycle of buildings, from linear processes that produce static continuous buildings to interrelational processes linking adaptative eco-systems of collaborative robots and reconfigurable building parts. Inspired by natural builders, the interdisciplinary field of collective robotic construction (CRC) offers the potential for scalable, adaptive, and resilient construction with simple robots. We establish a design framework for autonomous collaborative robotic construction (ACRC) through modular robotic material eco-systems (MRMES) trained with deep multi-agent reinforcement learning (DMARL). This involves the integration of three core aspects: (1) modular robotic material eco-systems (2) cyber-physical simulation and control with bidirectional feedback (3) adaptive intelligence through deep multi-agent reinforcement learning. The framework is implemented through three comparable case studies for collaborative modular robotic assembly of reconfigurable building parts.

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.

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.

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.

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.