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Snap fits are crucial in automotive applications for rapid assembly and disassembly of mating components, eliminating the need for fasteners. This study aims to focus on designing and analyzing serviceable cantilever fit snap connections used in automobile plastic components. Snap fits are classified into permanent and semi-permanent fittings, with permanent fittings having a snap clipping angle between 0° and 5° and semi-permanent fittings having a clipping angle between 15° and 45°. Polypropylene random copolymer is chosen for its exceptional fatigue resistance and elasticity.

The design process includes determining dimensions, computing assembly, disassembly pressures and creating three-dimensional computer-aided design models. Finite element analysis (FEA) is used to evaluate the snap-fit mechanism’s stress, deformation and general functionality in operational scenarios.

The study develops a modified snap-fit mechanism with decreased bending stress and enhanced mating force optimization. The maximum bending stress during assembly is 16.80 MPa, requiring a mating force of 7.58 N, while during disassembly, it is 37.3 MPa, requiring a mating force of 16.85 N. The optimized parameters significantly improve the performance and dependability of the snap-fit mechanism. The results emphasize the need of taking into account both the assembly and disassembly processes in snap-fit design, because the research demonstrates greater forces during disassembly. The approach developed integrates FEA and design for assembly (DFA) concepts to provide a solution for improving the efficiency and reliability of snap-fit connectors in automotive applications.

The research paper’s distinctiveness comes from the fact that it presents a thorough and realistic viewpoint on snap-fit design, emphasizes material selection, incorporates DFA principles and emphasizes the specific requirements of both assembly and disassembly operations. These discoveries may enhance the efficiency, reliability and sustainability of snap-fit connections in plastic automobile parts and beyond. In conclusion, the idea that disassembly needs to be done with a lot more force than installation in a snap-fit design can have a good effect on buzz, squeak and rattle and noise, vibration and harshness characteristics in automobiles.

In most developing countries, the delivery of construction project is still characterised by inefficiencies resulting from the use of outdated methods and techniques, which retards project performance. Hence, the call for the implementation of innovative technologies such as humanoids in the execution of construction projects as it has been proven to be very effective in other sectors while improving productivity and quality of work. Consequently, this study looks at how humanoids can be used in the construction industry and what benefits they can bring.

The study employed a quantitative approach underpinned in post-positivist philosophical view using questionnaire as the instrument for data collection. The target respondents were construction professionals, and purposive sampling was used, while a response rate of 62.5% was gotten. The methods of data analysis were mean item score, standard deviation and one-sample t-test.

The findings revealed that humanoids can be used in progress tracking, auto-documentation and inspection and surveillance of tasks in construction activities. Also, the most important benefits of using humanoids in construction work were found to be shorter delivery times, fewer injuries and more accurate work.

The outcome of the study gives professionals and relevant stakeholders in construction and other interested parties' information about the areas where humanoids can be used and their benefits in construction.

The novelty of this study is that it is a pioneering study in South Africa on humanoids' usage in the construction industry. Also, it expands the existing borderline of the conservation of construction digitalisation for enhanced project execution.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

The assembly simulation in tolerance analysis is one of the most important steps for the tolerance design of mechanical products. However, most assembly simulation methods are based on the rigid body assumption, and those assembly simulation methods considering deformation have a poor efficiency. This paper aims to propose a novel efficient and precise tolerance analysis method based on stable contact to improve the efficiency and reliability of assembly deformation simulation.

The proposed method comprehensively considers the initial rigid assembly state, the assembly deformation and the stability examination of assembly simulation to improve the reliability of tolerance analysis results. The assembly deformation of mating surfaces was first calculated based on the boundary element method with optimal initial assembly state, then the stability of assembly simulation results was assessed by the density-based spatial clustering of applications with noise algorithm to improve the reliability of tolerance analysis. Finally, combining the small displacement torsor theory, the tolerance scheme was statistically analyzed based on sufficient samples.

A case study of a guide rail model demonstrated the efficiency and effectiveness of the proposed method.

The present study only considered the form error when generating the skin model shape, and the waviness and the roughness of the matching surface were not considered.

To the best of the authors’ knowledge, the proposed method is original in the assembly simulation considering stable contact, which can effectively ensure the reliability of the assembly simulation while taking into account the computational efficiency.

In the context of fixed-wing aircraft wing assembly, there is a need for a rapid and precise measurement technique to determine the center distance between two double-hole components. This paper aims to propose an optical-based spatial point distance measurement technique using the spatial triangulation method. The purpose of this paper is to design a specialized measurement system, specifically a spherically mounted retroreflector nest (SMR nest), equipped with two laser displacement sensors and a rotary encoder as the core to achieve accurate distance measurements between the double holes.

To develop an efficient and accurate measurement system, the paper uses a combination of laser displacement sensors and a rotary encoder within the SMR nest. The system is designed, implemented and tested to meet the requirements of precise distance measurement. Software and hardware components have been developed and integrated for validation.

The optical-based distance measurement system achieves high precision at 0.04 mm and repeatability at 0.02 mm within a range of 412.084 mm to 1,590.591 mm. These results validate its suitability for efficient assembly processes, eliminating repetitive errors in aircraft wing assembly.

This paper proposes an optical-based spatial point distance measurement technique, as well as a unique design of a SMR nest and the introduction of two novel calibration techniques, all of which are validated by the developed software and hardware platform.

This paper uses dialectical inquiry to explore tensions that arise when adopting Industry 4.0 technologies in a lean production system and their reconciliation mechanisms.

We conducted an in-depth qualitative case study over a 3-year period on an Italian division of an international electrotechnical organisation that produces electrical switches. This organisation successfully adopted Industry 4.0 technologies in a lean production system. The study is based on primary data such as observations and semi-structured interviews, along with secondary data.

We identify four empirically validated dialectic tensions arising across different Industry 4.0 adoption stages due to managers’ and workers’ contrasting interpretations of technologies. Consequently, we define the related reconciliation mechanisms that allow the effective adoption of various Industry 4.0 technologies to support a lean production system.

This is the first empirical investigation of tensions in the adoption of Industry 4.0 technologies in a lean production system. Furthermore, the paper presents four theoretical propositions and a conceptual model describing which tensions arise during the adoption of Industry 4.0 technologies in a lean production system and the reconciliation mechanisms that prevent lean production system deterioration.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

The postponement principle concerns defining when and where value is added, usually referring to hardware components for physical products. However, in modern supply chains, software’s importance is increasing, impacting the timing and location of value-adding operations. Lacking insights into software-driven implications for postponement, we aim at elaborating on the postponement principle by contextualizing its evolution when integrating different objects (i.e. hardware and software).

We adopted an abductive approach to elaborate on the existing knowledge with original empirical insights. A single-case study with four subcases allowed us to explore postponement dimensions in the context of a global high-tech enterprise offering products that integrate hardware and software objects. As global supply chains involve multiple jurisdictions with heterogeneous regulations, we also analyzed in depth the emerging fiscal and legal implications.

Besides where and when value is added, the study illustrates that deciding who (i.e. what legal entity) is carrying out what operation on what kind of object is highly important. Moreover, fiscal and legal implications for the various legal entities strongly depend on what operations are executed and in which jurisdiction (where). The study identifies critical interrelationships among postponement dimensions when integrating hardware and software objects, highlighting the importance of understanding and managing their reciprocity with the emerging fiscal and legal risks.

We elaborate on the postponement principle by contextualizing its applications when integrating hardware and software objects in global supply chains, which include multiple jurisdictions. By formalizing the impact of the who dimension, the study contributes to developing the interorganizational perspective for postponement. Moreover, it extends the traditional cost perspective for postponement beyond the trade-off between responsiveness and cost-efficiency, suggesting that firms applying global postponement should extend their focus to also examine fiscal and legal risks for all the legal entities involved.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.

Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.