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Selection of the design concepts is one of the significant activities in product development process. Most of the products are usually failed due to inappropriate decision during the selection of the design concepts at the early stage of product development process. The determination of the greatest selection of design concepts at the conceptual design stage is a crucial decision due to a poor design concept which can never be compensated for by a good detailed design and it will implicate great expense of redesign cost. Analytical Hierarchy Process (AHP) is one available method in forming a systematic approach for a single decision maker or a group decision maker, is employed to solve such problem. In this paper, 7 design concepts of wheelchair designs were considered as a case study. The AHP through utilizing Expert Choice software was implemented to determine the most suitable design concept of wheelchair design at the conceptual design stage. The sensitivity analysis was performed to test the stability of the priority ranking and to increase the confidence in the selection of design concepts.

The purpose of this paper is to examine the use of fused deposition modelling (FDM) models and finite element analysis (FEA) related to dysplastic hip orthopaedic surgery.

The study involved the use of Mimics and Abaqus softwares. Mimics was used to process the CT scan patient data to STL format before producing FDM models which were for before and after surgery. FEA was done to study the two different type of implant biomaterials used in dysplastic hip surgery.

The use of FDM pre models for preplanning of dysplastic hip surgery by orthopaedic surgeons and viewing of the surgery outcome via FDM post models. Different implant biomaterials used gave different results in reduction of stresses that were achieved.

This is original work involving patients in hospital, which got ethical approval and was funded by a university grant. The paper describes a new kind of research in the university.

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries.

Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. A reference dataset contains comprehensive information regarding mechanical behavior of the lignocellulosic fibers was compiled from previous experimental investigations on mechanical properties for eight different fiber materials. Data encompass three key factors: Density of 0.9–1.6 g/cm³, Diameter of 5.9–1,000 µm, and Microfibrillar angle of 2–49 deg were utilized. Initially, fuzzy clustering technique was utilized for the data. For validating proposed model, ultimate tensile strength and elongation at break were predicted and then examined against unseen new data that had not been used during model development.

The output results demonstrated remarkably accurate and highly acceptable predictions results. The error analysis for the proposed method was discussed by using statistical criteria. The stacked model proved to be effective in significantly reducing level of uncertainty in predicting the mechanical properties, thereby enhancing model’s reliability and precision. The study demonstrates the robustness and efficacy of the stacked method in accurately estimating mechanical properties of lignocellulosic fibers, making it a valuable tool for material scientists and engineers in various applications.

Cellulosic fibers are essential for biomaterials to enhance developing green sustainable bio-products. However, such fibers have diverse characteristics according to their types, chemical composition and structure causing inconsistent mechanical performance. This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers.

Military and unmanned aerial vehicles (UAV) applications like rocket motor casings, missile covers and ship hulls use components that are made of maraging steel. Maraging steel has properties that are superior to other metals, making it more suitable for the fabrication of such components. A grey relational analysis (GRA) that is based on the Taguchi method has been utilised in the current study to optimise a laser beam welding (LBW) process. Further aspects such as GRA's optimum ranges and percentage contributions were also estimated.

A Taguchi L16 orthogonal array is utilised to design and conduct the experiments. Laser power (LP), welding speed (WS) and focal position (FP) are the three parameters are chosen for the process of welding. The output responses are the upper width of the heat-affected zone (HAZup), the upper width of the fusion zone (FZup) and the depth of penetration (DOP). The effect of the above key parameters on the responses was examined using an analysis of variance (ANOVA).

The results of ANOVA reveal that the parameter that has the most influence on the overall grey relational grade (GRG) is the FP. Finally, metallographic characterisation and a microstructural analysis are conducted on the weld bead geometry to demarcate the zone of HAZ and fusion zone (FZ).

As the most important criteria for LBW of maraging steels is the provision of higher DOP, higher FZ width and lower heat-affected zone, the study intended to prove the applicability of GRA technique in solving multi-objective optimisation problems in applications like defence and unmanned systems.

The purpose of this study is to reveal the friction reduction performance and mechanism of granular flow lubrication during the milling of difficult-to-machining materials and provide a high-performance lubrication method for the precision cutting of nickel-based alloys.

The milling tests for Inconel 718 superalloy under dry cutting, flood lubrication and granular flow lubrication were carried out, and the milling force and machined surface quality were used to evaluate their friction reduction effect. Furthermore, based on the energy dispersive spectrometer (EDS) spectrums and the topographical features of machined surface, the lubrication mechanism of different granular mediums was explored during granular flow lubrication.

Compared with flood lubrication, the granular flow lubrication had a significant force reduction effect, and the maximum milling force was reduced by about 30%. At the same time, the granular flow lubrication was more conducive to reducing the tool trace size, repressing surface damage and thus achieving better surface quality. The soft particles had better friction reduction performance than the hard particles with the same particle size, and the friction reduction performance of nanoscale hard particles was superior to that of microscale hard particles. The friction reduction mechanism of MoS₂ and WS₂ soft particles is the mending effect and adsorption film effect, whereas that of SiO₂ and Al₂O₃ hard particles is mainly manifested as the rolling and polishing effect.

Granular flow lubrication was applied in the precision milling of Inconel 718 superalloy, and a comparative study was conducted on the friction reduction performance of soft particles (MoS₂, WS₂) and hard particles (SiO₂, Al₂O₃). Based on the EDS spectrums and topographical features of machined surface, the friction reduction mechanism of soft and hard particles was explored.

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.

In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.

Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.

This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.

Because many cutting fluids contain hazardous chemical constituents, industries and researchers are looking for alternative methods to reduce the consumption of cutting fluids in machining operations due to growing awareness of ecological and health issues, government strict environmental regulations and economic pressures. Therefore, the purpose of this study is to raise awareness of the minimum quantity lubrication (MQL) technique as a potential substitute for environmental restricted wet (flooded) machining situations.

The methodology adopted for conducting a review in this study includes four sections: establishment of MQL technique and review of MQL machining performance comparison with dry and wet (flooded) environments; analysis of the past literature to examine MQL turning performance under mono nanofluids (M-NF); MQL turning performance evaluation under hybrid nanofluids (H-NF); and MQL milling, drilling and grinding performance assessment under M-NF and H-NF.

From the extensive review, it has been found that MQL results in lower cutting zone temperature, reduction in cutting forces, enhanced tool life and better machined surface quality compared to dry and wet cutting conditions. Also, MQL under H-NF discloses notably improved tribo-performance due to the synergistic effect caused by the physical encapsulation of spherical nanoparticles between the nanosheets of lamellar structured nanoparticles when compared with M-NF. The findings of this study recommend that MQL with nanofluids can replace dry and flood lubrication conditions for superior machining performance.

Machining under the MQL regime provides a dry, clean, healthy and pollution-free working area, thereby resulting the machining of materials green and environmentally friendly.

This paper describes the suitability of MQL for different machining operations using M-NF and H-NF.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0131/

This study aims to provide an overview of rapid prototyping (RP) and shows the potential of this technology in the field of medicine as reported in various journals and proceedings. This review article also reports three case studies from open literature where RP and associated technology have been successfully implemented in the medical field.

Key publications from the past two decades have been reviewed.

This study concludes that use of RP-built medical model facilitates the three-dimensional visualization of anatomical part, improves the quality of preoperative planning and assists in the selection of optimal surgical approach and prosthetic implants. Additionally, this technology makes the previously manual operations much faster, accurate and cheaper. The outcome based on literature review and three case studies strongly suggests that RP technology might become part of a standard protocol in the medical sector in the near future.

The article is beneficial to study the influence of RP and associated technology in the field of medicine.

The purpose of this paper is to determine the suitability of adopting hybridized multicriteria decision-making models as a decision tool in engineering design. This decision tool will assist design engineers and manufacturers to determine a robust design concept before simulation and manufacturing while all the design features and sub features would have been identified during the decision-making process.

Fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) are hybridized and applied to obtain optimal design of a reconfigurable assembly fixture (RAF) from a set of alternative design concepts. Design features and sub features associated with the RAF are identified and compared using fuzzified pairwise comparison matrices to obtain weights of their relative importance in the optimal design. The FAHP obtained the fuzzy synthetic extent (FSE) values of the design features and sub features. The FSE values are used as weights of the design features and sub features in generating the decision matrix. FTOPSIS and FTOPSIS based on left and right scores were adopted to predict effects of the weights. Results were obtained for normalized and unnormalized weights of the design features and its effects on the relative closeness coefficients of the design alternatives.

The improved performance of the FTOPSIS based on left and right scores is due to the involvement of the left and right scores of weights of the design features in the computation of distances from positive and negative ideal solutions. Embedding the weights of the design features in the normalized decision matrix before estimating the distances of the design concepts from ideal solutions reduces the dependency of the closeness coefficients on the weights of the design features. This also decreases the difference in the final values of the design concepts. In essence, the weights of the design features have an impact in the closeness coefficient. There is reduction in the closeness coefficients of the design concepts due to normalization of the weights of the design features. However, normalizing the weights of the design features did not affect the variations in the final values of the design concept. As the final value of the design concepts can be influenced by the normalized weights of the design features, it can be implied that normalization of weights of the sub features will also affect the decision matrix. The study has been able to proof that hybridizing FAHP and FTOPSIS can produce effective results for decisions on optimal design by the application of FTOPSIS based on left and right scores rather than the general FTOPSIS.

This research develops a hybridized multicriteria decision-making model for decision-making in engineering design. It presents a detailed extension of hybridized FAHP and FTOPSIS based on left and right scores as a useful tool for considering the relative importance of design features and sub features in optimal design selection.