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This study aims to improve the mechanical properties of an object produced by fused deposition modelling with high-grade polymer.

The study uses an ensembled surrogate-assisted evolutionary algorithm (SAEA) to optimize the process parameters for example, layer height, print speed, print direction and nozzle temperature for enhancing the mechanical properties of temperature-sensitive high-grade polymer poly-ether-ether-ketone (PEEK) in fused deposition modelling (FDM) 3D printing while considering print time as one of the important parameter. These models are integrated with an evolutionary algorithm to efficiently explore parameter space. The optimized parameters from the SAEA approach are compared with those obtained using the Gray Relational Analysis (GRA) Taguchi method serving as a benchmark. Later, the study also highlights the significant role of print direction in optimizing the mechanical properties of FDM 3D printed PEEK.

With the use of ensemble learning-based SAEA, one can successfully maximize the ultimate stress and percentage elongation with minimum print time. SAEA-based solution has 28.86% higher ultimate stress, 66.95% lower percentage of elongation and 7.14% lower print time in comparison to the benchmark result (GRA Taguchi method). Also, the results from the experimental investigation indicate that the print direction has a greater role in deciding the optimum value of mechanical properties for FDM 3D printed high-grade thermoplastic PEEK polymer.

This study is valid for the parameter ranges, which are defined to conduct the experimentation.

This study has been conducted on the basis of taking only a few important process parameters as per the literatures and available scope of the study; however, there are many other parameters, e.g. wall thickness, road width, print orientation, fill pattern, roller speed, retraction, etc. which can be included to make a more comprehensive investigation and accuracy of the results for practical implementation.

This study deploys a novel meta-model-based optimization approach for enhancing the mechanical properties of high-grade thermoplastic polymers, which is rarely available in the published literature in the research domain.

The investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.

The study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.

The work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.

The novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

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.

This paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.

First, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.

The research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.

The newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.

This study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

This study aims to determine the key indicators affecting the resilience of the construction supply chain to flooding and calculate the resilience of the urban construction supply chain in three cases city.

This study combines expert opinions and literature review to determine key indicators and establish a fuzzy EWM-GRA-TOPSIS evaluation model. The index weight was calculated using the entropy weight method, and GRA-TOPSIS was used for comprehensive evaluation.

The results of the study show that the three cities are ranked from the high to low in order of Hangzhou, Hefei and Zhengzhou.

The innovative method adopted in this study comprising EWM-GRA-TOPSIS reduced the influence of subjectivity, fully extracted and utilized data, in a way that respects objective reality. Further, this approach enabled the absolute and relative level of urban construction supply chain resilience to be identified, allowing improvements in the comprehensiveness of decision-making. The method is relatively simple, reasonable, understandable, and computationally efficient. Within the approach, the entropy weight method was used to assign different index weights, and the GRA-TOPSIS was used to rank the resilience of the construction supply chain in three urban cities. The development of resilience provides a robust decision-making basis and theoretical reference, further enriching research methods, and having strong practical value. The study serves to improve risk awareness and resilience, which in turn helps to reduce losses. It also provides enhanced awareness regarding the future enhancement of supply chain resilience for urban construction.

In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem.

In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores.

Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A₂ and A₁₁ occupy the top two positions having almost the same performance with respect to the considered financial metrics.

Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

The study aims to identify Green Lean Six Sigma (GLSS) barriers in the context of Higher Education Institutions (HEIs) and prioritize them for executing the GLSS approach.

A systematic literature review (SLR) was used to identify a total of 14 barriers, which were then verified for greater relevance by the professional judgments of industrial personnel. Moreover, many removal measures strategies are also recommended in this study. Furthermore, this work also utilizes Gray Relational Analysis (GRA) to prioritize the identified GLSS barriers.

The study reveals that training and education, continuous assessment of SDG, organizational culture, resources and skills to facilitate implementation, and assessment of satisfaction and welfare of the employee are the most significant barriers to implementing this approach.

The present study provides an impetus for practitioners and managers to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers. In this case, the outcomes of this research, and in particular the GRA technique presented by this work, can be used by managers and professionals to rank the GLSS barriers and take appropriate action to eliminate them.

The ranking of GLSS barriers gives top officials of HEIs a very clear view to effectively and efficiently implementing GLSS initiatives. The outcomes also show training and education, sustainable development goals and organizational culture as critical barriers. The findings of this study provide an impetus for managers, policymakers and consultants to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers.

The GLSS barriers in HEIs may significantly affect the society. HEIs can lessen their environmental effect by using GLSS practices, which can support sustainability initiatives and foster social responsibility. Taking steps to reduce environmental effect can benefit society as a whole. GLSS techniques in HEIs can also result in increased operational effectiveness and cost savings, which can free up resources to be employed in other areas, like boosting student services and improving educational programs. However, failing to implement GLSS procedures in HEIs could have societal repercussions as well. As a result, it is critical for HEIs to identify and remove GLSS barriers in order to advance sustainability, social responsibility and operational effectiveness.

GLSS is a comprehensive methodology that facilitates the optimum utilization of resources, reduces waste and provides the pathway for sustainable development so, the novelty of this study stands in the inclusion of its barriers and HEIs to prioritize them for effective implementation.

The purpose of this paper is to explore a new grey relational analysis model to measure the coupling relationship between the indicators for the water environment status assessment. Meanwhile, the model deals with the problem that the changing of indicator order may result in the changing of the degree of grey relation.

The binary index submatrix of the sample matrix is given first. Then the product of the matrix and its own transpose is used to measure the characteristics of the index and the coupling relationship between the indicators. Thirdly, the grey relational coefficient is defined based on the matrix norm, and a grey coupling relational analysis model is proposed.

The paper provides a novel grey relational analysis model based on the norm of matrix. The properties, normalization, symmetry, relational order invariance to the multiplicative, are studied. The paper also shows that the model performs very well on the water environment status assessment in the eight cities along the Yangtze River.

The model in this paper has supplemented and improved the grey relational analysis theory for panel data.

This paper compares traditional and digital banks in nine categories of complaints and provides insights to improve complaint management performance.

A sample of the major Brazilian banks was defined, with four traditional and four digital banks. The grey relational analysis (GRA) method was applied as an analytical tool to compare the most frequent complaints of traditional and digital banks. The most critical complaints identified were considered to discuss potential improvements in complaint management using quality and service management system concepts.

The GRA method enabled the development of a ranking of nine complaint categories, considering the uncertainty involved in the data and differentiating between traditional and digital banks. The most critical complaint categories, regardless of business model, were “unauthorized charges” and “poor service,” which were ranked first and second in the frequency rankings. Traditional and digital banks differed the most in the complaint category “unfair charge,” ranking third and eighth in the rankings, respectively.

Managers from traditional and digital banks can improve complaint management performance by applying ISO 9001 and ISO 20000 concepts such as incident, problem, change, service level, availability, capacity, information technology service continuity and financial management.

The study's findings can help bank managers improve service levels in the face of technological competition. Improving these organizations is an important factor for developing countries such as Brazil.

This paper reveals the differences between two business models regarding complaint management. It also considers a methodological approach to include the uncertainty related to customers' perception and subjectivity inherent to complaints.

This study aims to investigate the micro mechanism of macro rheological characteristics for composite modified asphalt.Grey relational analysis (GRA) was used to analyze the correlation between macro rheological indexes and micro infrared spectroscopy indexes.

First, a dynamic shear rheometer and a bending beam rheometer were used to obtain the evaluation indexes of high- and low-temperature rheological characteristics for asphalt (virgin, SBS/styrene butadiene rubber [SBR], SBS/rubber and SBR/rubber) respectively, and its variation rules were analyzed. Subsequently, the infrared spectroscopy test was used to obtain the micro rheological characteristics of asphalt, which were qualitatively and quantitatively analyzed, and its variation rules were analyzed. Finally, with the help of GRA, the macro-micro evaluation indexes were correlated, and the improvement efficiency of composite modifiers on asphalt was explored from rheological characteristics.

It was found that the deformation resistance and aging resistance of SBS/rubber composite modified asphalt are relatively good, and the modification effect of composite modifier and virgin asphalt is realized through physical combination, and the rheological characteristics change with the accumulation of functional groups. The correlation between macro rutting factor and micro functional group index is high, and the relationship between macro Burgers model parameters and micro functional group index is also close.

Results reveal the basic principle of inherent-improved synergistic effect for composite modifiers on asphalt and provide a theoretical basis for improving the composite modified asphalt.