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This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.

The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.

The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.

3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

Grey relational analysis (GRA) has already proved itself as an efficient tool for multi-objective optimization of many of the machining processes. In GRA, the distinguishing coefficient (ξ) plays an important role in identifying the optimal parametric combinations of the machining processes and almost all the past researchers have considered its value as 0.5. In this paper, based on past experimental data, the application of GRA is extended to dynamic GRA (DGRA) to optimize two electrochemical machining (ECM) processes.

Instead of a static distinguishing coefficient, this paper considers dynamic distinguishing coefficient for each of the responses for both the ECM processes under consideration. Based on these coefficients, the application of DGRA leads to determination of the dynamic grey relational grade (DGRG) and grey relational standard deviation (GRSD), helping in initial ranking of the alternative experimental trials. Considering the ranks obtained by DGRG and GRSD, a composite rank in terms of rank product score is obtained, aiding in final rankings of the experimental trials for both the ECM processes.

In the first example, the maximum material removal rate (MRR) would be obtained at an optimal combination of ECM parameters as electrolyte concentration = 2 mol/l, voltage = 16V and current = 4A, while another parametric intermix as electrolyte concentration = 2 mol/l, voltage = 14V and current = 2A would result in minimum radial overcut and delamination. For the second example, an optimal combination of ECM parameters as electrode temperature = 30°C, voltage = 12V, duty cycle = 90% and electrolyte concentration = 15 g/l would simultaneously maximize MRR and minimize surface roughness and conicity.

In this paper, two ECM operations are optimized using a newly developed but yet to be popular multi-objective optimization tool in the form of the DGRA technique. For both the examples, the derived rankings of the ECM experiments exactly match with those obtained by the past researchers. Thus, DGRA can be effectively adopted to solve parametric optimization problems in any of the machining processes.

Many firms have adopted different methodologies such as lean management to increase customer satisfaction. This is because they need to respond to customer demands for improved products and responsive service. This study aims to evaluate the influence of soft lean practices (SLP) on business performance in the service sector.

Out of 702 questionnaires distributed to various service companies in Zimbabwe, 260 valid responses were received. Structural equation modeling was used to assess the relationship among the factors of the proposed model.

The implementation of SLP leads to improvement in the business performance of the service companies. However, the impact of SLP on business performance is mainly indirect, mediated by customer satisfaction.

The research focused on the implementation of SLP in the service industry of a developing country; hence, the results obtained may require further investigations before generalization to other countries with different sociocultural contexts is made.

Most previous studies focused mainly on the implementation of the technical lean practices in the manufacturing industry without properly acknowledging the importance of SLP. This research investigates the importance of SLP in the service sector and further explores the mediatory role of customer satisfaction on business performance. The findings also validate the service-profit-chain theory.

The purpose of this paper is to investigate the research landscape of LSS 4.0 papers published in two well-known repositories, Scopus and Web of Science (WoS), in terms of publication trends, article distribution by author, journal, affiliations and country, and article clustering based on keywords, authors and countries. In addition, a literature review was carried out to build a conceptual framework of integrated Lean Six Sigma and Industry 4.0 (LSS 4.0) that encompasses operational, sustainability and human factors or ergonomics aspects.

The literature review of integrated Lean Six Sigma and I4.0 publications published in Scopus and WoS databases in the current decade was conducted for the present study. This study categorizes LSS, I4.0 and related research articles based on publication patterns, journals, authors and affiliations, country and continental-wise distribution and clustering the articles based on keywords and authors from the Scopus and WoS databases from 2011 to 2022 using the search strings “Lean”, “Six Sigma”, “Lean Six Sigma” and “Industry 4.0” in the Title, Abstract and Keywords using Biblioshiny, VOS viewer and Microsoft Excel.

In the recent three years, from 2020 to 2022, LSS 4.0 has been substantially increasing and is seen as an emerging and trending area. This research identifies the most influential authors, most relevant affiliations, most prolific countries and most productive journals and clusters based on keywords, authors and countries. Further, a conceptual framework was developed that includes the impact of operational, sustainability and ergonomic or human factors in LSS 4.0.

This article assists in comprehending the trends and patterns of LSS 4.0. Further, the conceptual framework helps professionals and researchers understand the significance and impact of integrating LSS and Industry 4.0 in the aspects of human factors/ergonomic, sustainability and operations. Also, the research induce professionals to incorporate all these factors while designing and implementing LSS 4.0 in their organization.

This conceptual framework and bibliometric analysis would aid in identifying potential areas of research and providing future directions in the domain of LSS 4.0. It will be beneficial for academicians, professionals and researchers who are planning to apply and integrate techniques of LSS and technologies of I4.0 in their organizations and research.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs) under dry sliding condition by using a robust statistical method.

In this research, the epoxy/bamboo and epoxy/flax composites filled with 0–8 Wt.% TiO₂ particles have been fabricated using simple hand layup techniques, and wear testing of the composite was done in accordance with the ASTM G99-05 standard. The Taguchi design of experiments (DOE) was used to conduct a statistical analysis of experimental wear results. An analysis of variance (ANOVA) was conducted to identify significant control factors affecting SWR under dry sliding conditions. Taguchi prediction model is also developed to verify the correlation between the test parameters and performance output.

The research study reveals that TiO₂ filler particles in the epoxy/bamboo and epoxy/flax composite will improve the tribological properties of the developed composites. Statistical analysis of SWR concludes that normal load is the most influencing factor, followed by sliding distance, Wt.% TiO2 filler and sliding velocity. ANOVA concludes that normal load has the maximum effect of 31.92% and 35.77% and Wt.% of TiO2 filler has the effect of 17.33% and 16.98%, respectively, on the SWR of bamboo and flax FRCs. A fairly good agreement between the Taguchi predictive model and experimental results is obtained.

This research paper attempts to include both TiO₂ filler and bamboo/flax fibers to develop a novel hybrid composite material. TiO₂ micro and nanoparticles are promising filler materials, it helps to enhance the mechanical and tribological properties of the epoxy composites. Taguchi DOE and ANOVA used for statistical analysis serve as guidelines for academicians and practitioners on how to best optimize the control variable with particular reference to natural FRCs.

The purpose of this study is to investigate the impact of titanium oxide (TiO₂) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a Taguchi approach. The study aims to enhance the abrasive wear resistance of these composites by introducing TiO₂ filler as a potential reinforcement, thus contributing to the development of sustainable and environmentally friendly materials.

This study focuses on the fabrication of epoxy/bamboo composites infused with TiO₂ particles within the Wt.% range of 0–8 Wt.% using hand layup techniques. The resulting composites were subjected to wear testing according to ASTM G99-05 standards. Statistical analysis of the wear results was carried out using the Taguchi design of experiments (DOE). Additionally, an analysis of variance (ANOVA) was used to determine the influential control factors impacting the specific wear rate (SWR) and coefficient of friction (COF).

The study illuminates how integrating TiO₂ filler enhances abrasive wear in epoxy/bamboo composites. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, Wt.% of TiO₂ and sliding distance. Analysis of the COF identifies normal load as the primary influential factor, followed by grit, Wt.% of TiO₂ and sliding distance. The Taguchi predictive model closely aligns with experimental results, validating its reliability. The morphological study revealed significant differences between the unfilled and TiO₂-filled composites. The inclusion of TiO₂ improved wear resistance, as evidenced by reduced surface damage and wear debris.

This research paper aims to integrate TiO₂ filler and bamboo fibers to create an innovative hybrid composite material. TiO₂ micro and nanoparticles show promise as filler materials, contributing to improved tribological properties of epoxy composites. The utilization of Taguchi’s DOE and ANOVA for statistical analysis provides valuable guidance for academic researchers and practitioners in optimizing control variables, especially in the context of natural fiber reinforced composites.

This study aims to investigate the impact of titanium oxide (TiO₂) filler on the coefficient of friction (COF) and specific wear rate (SWR) in flax fiber reinforced epoxy composites (FFRCs) under abrasive wear conditions utilizing the Taguchi approach. The primary objective is to enhance wear resistance and promote the development of sustainable materials for various applications.

Epoxy/flax composites with varying TiO₂ filler content (0–8 wt%) are fabricated through the hand layup method. Subsequently, wear testing is conducted following ASTM G99-05 standards. The Taguchi design of experiments (DOE) and analysis of variance (ANOVA) are utilized for statistical analysis.

Results indicate a significant improvement in abrasive wear properties with the incorporation of TiO₂ filler. The COF is found to be most influenced by the normal load (55.19%), followed by grit size, wt% TiO₂ filler and sliding distance. SWR is found to be most influenced by the grit size (42.92%), followed by wt% TiO₂, normal load and sliding distance. Notably, the Taguchi model aligns well with experimental results, demonstrating its efficacy in predicting the abrasive wear behavior of FFRCs.

This research introduces a novel hybrid composite that combines TiO₂ filler and flax fibers, showcasing their potential to enhance the tribological properties of epoxy composites. The study offers valuable insights into optimizing abrasive wear test variables in natural fiber-reinforced composites using Taguchi DOE and ANOVA, crucial for improving the performance of sustainable materials in engineering applications.

Globally, the landscape of corporate carbon disclosures (CCD) is continually evolving as societal, environmental and regulatory expectations change over time. The goal of this study is to examine the challenges faced by Indian firms’ corporate carbon reporting (CCR). The literature recognized the hurdles to reaching net zero emissions and decarbonization, which are equally applicable to carbon disclosure (CD).

The scope 3 emission disclosure barriers (S3EDBs) identified from the literature were ranked, and their relationships were discovered using the “Grey-based decision-making trial and evaluation laboratory” (Grey- DEMATEL) technique.

The key findings are the S3EDBs, the most prominent barriers, their interrelationships and important insights for managers of organizations in prioritizing the action area for scope 3 CD. Eight S3EDBs were categorized in terms of cause and effect, threshold value is calculated as 0.78. “Quality, and reliability of data,” “Government policies and statutory requirement on emission disclosure” and “Traceability and managing supply chain partners” are the most prominent S3EDBs.

The results will help industry people in countries with emerging economies that have significant scope 3 carbon footprints. The managers can plan to deal with top S3EDBs as a step towards decarbonization and ultimately fighting climate change (CC).

This study is one of the first to rank these barriers to CD so that industry practitioners can prioritize their actions. The core contribution of this research is to detect the most significant S3EDBs and their interdependencies.

The fertilizer industry (FI) is well known for its high energy needs, reliance on limited natural resources, and negative environmental impacts (EIs). The consumption of 14.2 billion tons (BT) of materials and the extraction of 1,580 tons of resources per acre are solely attributed to the FI. Because of FI's resource and energy-intensive nature, it becomes crucial for FI to adopt a Circular Economy (CE) to improve efficiency, energy, and resource reuse. However, FI needs to strengthen its progress toward CE adoption. The proposed study comprehends and examines the barriers that inhibit the adoption of CE in FI.

A total of 15 barriers obstructing the CE in FI are identified and categorized into seven different categories. The barriers were identified by performing a comprehensive literature review and expert input. The study employs the DEMATEL approach to analyze the barriers and establish a causal relationship between them.

The study reveals that the most significant challenge to implementing CE in FI is governmental restrictions, which are followed by a lack of awareness and understanding and a need for a steady supply of bulk materials. The results comprehensively comprehend the pivotal factors that jeopardize the CE in FI and furnish a robust foundation for the methodology and tactics to surmount the barriers to CE adoption.

The literature review encompasses the barriers to the transition to CE and offers management and policy perspectives that help the FI's policy and decision-makers surmount these barriers with future research endeavors.

Education 4.0 (E 4.0) represents a new paradigm in the field of education, which emphasizes a student-centric approach that allows learners to access education anytime, anywhere, tailored to their individual needs through modern-day technologies. The purpose of the study was to unearth the critical success factors (CSFs) essential for the successful implementation of E 4.0.

The CSFs were unearthed using a literature review and further the interrelationships were analysed using multi-criteria decision making (MCDM) approach.

The study unearthed 15 CSFs for the successful implementation of E 4.0. The most important factor for the successful implementation of E 4.0 was personalized learning which was found to be the casual factor. The other causal CSFs were clear vision and leadership for E 4.0, stakeholder involvement, data analytics in teaching and learning, inter-disciplinary learning and blended learning environments. The effect factors were digital citizenship-based education, teacher training and development for E 4.0, supportive environment, curriculum redesign for E 4.0, open educational resources, digital technologies, formative assessments, infrastructure for E 4.0 and sustainability in education.

This is the first study which unearthed the CSFs and found the interrelationships among them, thus contributing to the theory of technology organization environment.

This study represented a pioneering effort in understanding the CSFs underpinning the successful adoption of E 4.0, paving the way for a more personalized, tech-savvy and effective education system.