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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.

This paper aims to fulfil a need to identify assembly interfaces from existing products based on their Assembly Process Planning (APP). It proposes a tool to identify assembly interfaces responsible for reused components integration. It is integrated into a design for mixed model final assembly line approach by focusing on the identification of assembly interfaces as a generic tool. It aims to answer the problem of interfaces’ identification from the APP.

A tool is developed to identify assembly interfaces responsible for reused component integration. It is based on the use of a rule-based algorithm that analyses an APP and then submits the results to prohibition lists to check their relevance. The tool is then tested using a case study. Finally, the resulting list is subjected to a visual validation step to validate whether the identified interface is a real interface.

The results of this study are a tool named ICARRE which identify assembly interfaces using three steps. The tool has been validated by a case study from the helicopter industry.

As some interfaces are not contained in the same assembly operations and therefore, may not have been identified by the rule-based algorithm. More research should be done by testing and improving the algorithm with other case studies.

The paper includes implications for new product development teams to address the difficulties of integrating reused components into different products.

This paper presents a tool for identifying interfaces when sources of knowledge do not allow the use of current methods.

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.

This work presents the results of a case study aimed at revitalizing an agricultural equipment manufacturing consortium facing prolonged losses. The purpose of this paper is to enhance productivity and profitability by identifying and eliminating waste within the manufacturing processes. The study uses lean principles and tools to achieve this objective.

The study begins with the creation of a questionnaire, administered to the consortium to gather insights. The questionnaire responses serve as a foundation for pinpointing critical areas in need of immediate attention. To tackle the challenge of demand forecasting without customer data, a demand forecasting model is introduced. Value stream mapping (VSM) is used to identify and highlight process inefficiencies and waste. The findings are further analyzed using a Pareto chart to prioritize waste reduction efforts. Based on these insights, the study proposes alternative manufacturing methods and waste elimination strategies. A multiphase lean framework is developed as a step-by-step roadmap for implementing lean manufacturing.

The study identifies a broken process flow within the consortium’s manufacturing processes and highlights areas of waste through VSM. The Pareto chart analysis reveals the most significant waste areas requiring immediate intervention. Recommendations for process improvements and waste reduction strategies are provided to the consortium.

This study contributes to the field by applying lean principles and tools to address the unique challenges faced by an agricultural equipment manufacturing consortium. The integration of a demand forecasting model and the development of a multiphase lean framework offer innovative approaches to enhancing productivity and profitability in this context.

This study aims to explore psychological safety as a potential moderating mechanism for the relation between functional diversity and individual perceptions of learning, and functional diversity and team performance in self-assembled teams.

To test these relationships, the authors conducted a cross-level, time-lagged, quasi-experiment, using a sample of 143 self-assembled teams. In one condition, participants formed into functionally diverse teams, and in another condition, participants formed functionally homogeneous teams.

Results suggest that functional diversity and psychological safety have an interactive effect on both individual learning and self-assembled team performance, albeit in different directions. Specifically, low psychological safety was more deleterious for individuals on functionally diverse teams than functionally homogeneous teams when it came to perceptions of learning, but the opposite was true when it came to team performance.

The results of this study indicate that it is critical to train team members on developing psychological safety, both in traditional and functionally diverse contexts.

Organizations are continually improving their practices to improve operational performance. They already employ Lean Manufacturing techniques (LM) to reduce unnecessary waste. Industry 4.0 techniques enhance operational performance in association with LM. Despite the proven benefits of LM principles and the advancements offered by Industry 4.0 technologies, many organizations struggle to integrate these approaches effectively. This research paper explores how LM principles can be combined with Industry 4.0 technologies to provide valuable guidance for businesses looking to adopt lean automation strategies.

A systematic literature review on LM and Industry 4.0 was done to investigate the possible technical integration of both methods. Ninety-two articles are extracted systematically from the Scopus and Web of Science databases. This study states a systematic literature review, including quantitative analysis of bibliographic networks and cluster analysis, to identify emergent ideas and their further implementation.

The research findings highlight the positive impact of integrating lean production with Industry 4.0 techniques, benefiting organizations in achieving their goals. A lean automation integration framework is proposed based on the literature review and the findings.

This study provides industry administrators and practitioners valuable guidance for enhancing organizational productivity. These implications can provide businesses with competitive advantages, enhance customer satisfaction, and enable them to adapt to the dynamic demands of the contemporary business environment.

This literature review study has substantially contributed to the technological integration of lean and Industry 4.0. The work has also identified potential emerging areas that warrant further research.

This study aims to address the aspects of product and process innovation strategies and their determining factors to understand their characteristics in clothing manufacturing and contribution for a successful and competitive clothing industry.

This general review is based on literature data of previous studies on innovation that transcend and cover the aspects of innovation applicable in the clothing industry. Although the scope of discussion is theoretically broad, it focusses on the context of innovation strategies in clothing manufacturing and the determinant factors indicating the acquisition and implementation of product and process-related innovation activities, simultaneously exploring and linking their implications for adopting, managing and integrating enterprise activities to the values of desired innovation novel models.

Based on theoretical background and pragmatic generalizations, product and process innovation strategies in clothing manufacturing firms tend to incline more towards computer-integrated technologies and concepts meant to promote product development, process optimization and organizational integration. Industry, technological and R&D factors tend to significantly determine innovation capability of a clothing firm.

This review generates integrated conceptual frameworks for product and process innovation strategies applicable in clothing firms and their determinant factors as prelude to empirical validation.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

Analyst team forecasts are the most frequent form of earnings expectations available to investors, with teams issuing more than 70% of research reports in 2016. Prior research provides differing evidence on whether analyst teams issue higher or lower quality forecasts than individual analysts.

I use a sample of more than 17,000 hand-collected analyst reports representing 7,586 forecasts from 89 companies in three industries from 1994–2005.

I document that analyst teams benefit from an assembly bonus, and issue more accurate forecasts than individual analysts only in time periods when teams would be expected to benefit from an assembly bonus.

I outline multiple factors within the control of brokerage houses that impact teams’ relative forecast quality, such as the number of members in the team, how long the team has worked as a unit and the costliness of integrating information when forming a forecast.

Given the preponderance of analyst teams and the strength of market reaction to their forecasts, it is valuable to document factors both in the past and present likely to affect analyst teams’ relative forecast accuracy.

Offsite construction (OSC) is a modern method of construction (MMC) that involves manufacturing and assembly of buildings. Industry 4.0-driven technological advancements have accelerated OSC uptake causing many skilled construction workers requiring re-skilling or up-skilling. Quantity surveyors currently play an important role throughout a construction project life cycle, and hence, their knowledge on OSC projects is vital for the continuity of quantity surveying (QS) profession. The purpose of this paper is to critically review QS education in an OSC perspective and to propose strategies for up-skilling.

This research includes a detailed literature review of QS and OSC to evaluate how OSC affects the QS profession. Publicly available information on the university websites on accredited QS and construction management degrees was reviewed to identify OSC and MMC references, specifically the content listing and learning outcomes.

Out of the 315 degrees offered by 157 universities, only 69 degrees in 32 universities refer to OSC or MMC. OSC concept is explicitly referred in subjects related to construction technology, production management, lean construction, digital construction and MMC. As such, the research suggests including lean production principles, OSC techniques and design philosophies such as design for manufacture and assembly be added to the QS curricula.

This research is a comprehensive desktop study that does not involve empirical data-based evaluation. Research findings inform strategies to improve QS education and professional competencies.

To the best of the authors’ knowledge, this paper is the first of its kind that evaluates accredited QS degrees and learning requirements in an OSC perspective.