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The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

Laser fading, commonly used in the denim industry, is a computer-controlled, dry, ecological finishing method whereas conventional methods include high water, energy and time consumption. Resolution and pixel time are crucial parameters of laser source influencing the effect of laser treatment. The purpose of this study is to determine the optimum laser parameters of CO₂ laser followed by enzyme washing and to compare the tensile strength and color values of laser-treated denim fabric with that of conventional enzyme-faded.

Two different indigo-dyed, sulfur bottom-indigo-dyed and only indigo-dyed organic cotton denim fabrics with different unit weights, were lasetreated with different laser parameters and then subjected to 10 min enzyme washing. Tensile strength, abrasion resistance, and change in fabric unit weight were tested. CIE (L*a*b*, ΔE*, h°, C*) color values, color strength (K/S), yellowness and whiteness indexes were measured to identify the color differences. Color fastness tests including washing, rubbing, light, water and perspiration fastness were investigated.

Most effective laser fading in terms of good mechanical properties and color values was obtained at 40 dpi resolution and 300 µs pixel time.

Conventional enzyme fading of denim fabrics is a wet process and requires a long process time of 40–45 min and high temperatures, leading to high energy and water consumption. Laser fading, on the other hand, is a dry and ecological method, but causes a decrease in mechanical properties of the fabric, and an increase in yellowness. In this study, unlike the similar studies in the literature, denim fading was carried out by a combination of laser treatment followed by only 10 min enzyme washing in order to eliminate or minimize the drawbacks of the denim fading, such as high energy and water consumption for enzyme fading and decrease in mechanical properties of the fabric and increase in yellowness for laser fading. This method was applied to two different dyed denim fabrics, sulfur (bottom) and indigo (top) and laser process conditions were optimized to achieve the desired fading effects compared to conventional enzyme fading.

Tacit knowledge in frontline operations is primarily reflected in the holders’ intuition about dynamic systems. Despite the implicit nature of tacit knowledge, the understanding of complex systems it encapsulates can be displayed through formalization methods. This study seeks to develop a methodology for formalizing tacit knowledge in a dynamic delivery system.

This study employs a structured survey to gather experiential knowledge from dispatchers engaged in last-mile delivery operations. This knowledge is then formalized using a value function approximation approach, which transforms tacit insights into structured inputs for dynamic decision-making. We apply this methodology to optimize delivery operations in an online-to-offline pharmacy context.

The raw system feature data are not strongly correlated with the system’s development trends, making them ineffective for guiding dynamic decision-making. However, the system features obtained through preprocessing the raw data increase the predictiveness of dynamic decisions and improve the overall effectiveness of decision-making in delivery operations.

This research provides a foundational framework for studying sequential dynamic decision problems, highlighting the potential for improved decision quality and system optimization through the formalization and integration of tacit knowledge.

This approach proposed in this study offers a method to preserve and formalize critical operational expertise. By embedding tacit knowledge into decision-making systems, organizations can enhance real-time responsiveness and reduce operational costs.

This study presents a novel approach to integrating tacit knowledge into dynamic decision-making frameworks, demonstrated in a real-world last-mile delivery context. Unlike previous research that focuses primarily on explicit data-driven methods, our approach leverages the implicit, experience-based insights of operational staff, leading to more informed and effective decision-making strategies.

Prefabricated building (PB) has high technical requirements and is susceptible to safety accidents, and its construction occupational health and safety (OHS) problems should not be ignored. To promote the better development of PB, this study aims to measure their construction safety management level and propose corresponding countermeasures.

By systematically combing the relevant literature, this study extracts the influencing factors that appear frequently in several studies and categorizes them according to six dimensions: people, materials and components, technology, mechanical equipment, environment and system. Combining expert opinions, the measurement index system, including 6 primary indexes and 24 secondary indexes, is constructed. The structural entropy weight (SEW) method is applied to calculate the index weights. The cloud matter element (CME) model based on the weights is constructed to determine the level of construction occupational health and safety management (COHSM). A project case of a training building is used to verify it. The results obtained from the model are compared with those from other measurement models to verify the feasibility of the model in measuring the level of COHSM for PB.

The calculated weights show that technology is the most important for the COHSM of PB. The management level of the project in terms of people, materials and components, technology, machinery and equipment, environment and system is Level II good. The overall safety management level is also Level II, which is good. The model of this study is consistent with other model measurements. The methodology of this study yields reasonable and realistic results.

This study is the first to include occupational health dimensions in the research on the construction safety management of PB, which not only covers the key elements in traditional construction safety management but also considers the impact of the construction process, material use and technology of PB on safety management, making the measurement index system more scientific. Meanwhile, the introduction of the CME model based on the SEW method effectively solves the deficiencies of the traditional method in dealing with ambiguity and uncertainty and provides practitioners with more accurate and comprehensive measurement results. It helps practitioners formulate a more scientific management plan in combination with the actual situation and provides a guiding idea and practical path for the COHSM of similar projects.

The purpose of this study is to assess the effects of fused filament fabrication (FFF) printing parameters on the surface quality and dimensional accuracy of FFF-fabricated copper green parts using the appropriate filaments. The orthogonal experiments were implemented and the errors in length, width and height were measured and analyzed. The results of range analysis and variance analysis indicated the orders of effect factors. Dissolvent debinding combined with thermal debinding was adopted to remove the binders inside the green part by calculating debinding rate. The influence mechanism of sintering temperatures on the microstructure and shrinkage was elaborated.

The extrusion-based FFF in manufacturing copper parts can overcome shortcomings for high reflectivity and heat dissipation in laser powder bed fusion process at cost-saving and materials saving. This study makes an attempt to prepare copper/binder composite filaments through mixing, extrusion and flowability evaluation.

The results showed that the suitable composite filaments applied for FFF should balance rigidity and plasticity. The combination of printing speed and heating temperature impacts on the surface quality significantly, and the major factor in determining the dimensional accuracy is layer thickness. Two-stage debinding procedure was beneficial for binder removal and sintering process. The higher sintering temperature results in less voids, sizes shrinkage and densified microstructure, which is attributed to the occurrence of sintering neck among the fused copper powders.

The self-prepared copper/binder composite filaments were successfully manufactured using the FFF process. This study provides unique approach and print guidance for fabricating complex structures of pure copper components.

Small and medium-sized enterprises (SMEs) are critical for achieving a green economy. This study aims to empirically explore business associations’ impacts on SMEs’ green transformation in China.

This study uses a multiple-case study approach. The authors collect data from four Chinese business associations and their members.

This study finds that business associations promote SMEs’ green transformation by providing individual and collective services. SMEs conduct green transformation by developing a green mindset and adopting green operations. The results show that individual services directly enhance members’ green mindset and green operations. Collective services promote members’ green mindset and green operations both directly and indirectly by building relational capital.

This study contributes to the literature by revealing that business associations play critical roles in assisting SMEs’ green transformation. In addition, this study suggests that SMEs may adopt different practices compared with large companies. The findings enhance the current understanding of how SMEs conduct green transformation and how business associations assist SMEs.

This research paper aims to establish a comprehensive framework for the barriers to CER in the construction industry, assesses the barriers' relative degrees of hindrance and causal mechanisms.

Firstly, 26 carbon emission reduction (CER) barriers in the construction industry were identified based on a systematic literature review (SLR) and categorized into five dimensions: policy, economy, society, technology and organization (PEST + O model). Secondly, the Best–Worst Method (BWM) was used to clarify the degrees of hindrance of the CER barriers. Then, the Grey-Decision-Making Trial and Evaluation Laboratory (Grey-DEMATEL) was used to visualize the directional cause–result relationship network among prominent barriers. Finally, the Boston matrix model was used to propose differentiated strategies to address CER barriers in the construction industry.

The calculated centrality and causality of the prominent barriers indicated that the lack of relevant legal policies and normative guidelines, the poor binding force and enforcement of existing relevant policies, the lack of effective economic subsidies and incentives and the difficulty in the operation, transformation and upgrading of existing construction CER are the key barriers that CER needs to address first in the construction industry. Considering the order of priority and the optimal path, differentiated countermeasures are proposed to address key, driving, independent and effect barriers.

This study develops a BWM–Grey-DEMATEL integrated multi-criteria decision-making model. An innovative C-shaped strategic map for addressing CER barriers in the construction industry is proposed by integrating the dual dimensions of time and space. This will guide practitioners, policymakers and decision-makers in developing CER strategies.

Despite wide awareness of the importance of digital transformation (DT) for emerging market firms, we have limited understanding of the drivers, the process or the outcomes of DT in emerging market firms.

We conducted a qualitative study on 24 case companies in Thailand and embraced thematic analysis to generate our research findings.

The framework shows that the DT process in emerging market firms proceeds over three stages—market-opportunity sensing, digital technology acquisition and leading DT—which are driven by technological dynamism, business ties and institutional support. Once DT is successfully implemented, emerging market firms can improve their operational efficiency, customer relationship management, business model innovation and human resources management.

This study thus contributes to the DT literature by offering a three-stage model of DT and identifying important antecedents and consequences of DT, which together specify how emerging market firms transform themselves digitally.

As China is on an inevitable march into the digital era, firms have accumulated abundant digital assets, such as algorithms and data. Facing the possibility of using digital assets as a new type input, besides traditional inputs such as capital and labor, would powerful managers perform better? Would managerial power help managers increase the efficiency of how a firm combines traditional and digital inputs and converts them into outputs? Thus, the purpose of this study is to investigate whether powerful managers promotes corporate productivity by using digital assets as a new input.

Using data from listed Chinese firms between 2008 and 2020, the authors constructed panel regressions with three-way fixed effects to examine whether and how managerial power influences corporate productivity in the current digital context, particularly under market uncertainty.

The findings reveal no consistent relationship between managerial power and corporate productivity. The results explain this from two contrasting effects: while managerial power promotes technological change it hinders technical efficiency – two components of total productivity. Moreover, this study identifies market uncertainty as a significant external contingency. In uncertain markets, strong managerial power positively impacts corporate productivity.

The results extend extant theoretical insights in the literature on how managerial power might influence corporate productivity.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.