Application of MEREC in multi-criteria selection of optimal spray-painting robot

Robots are being increasingly utilized for various operations in industrial and household applications. One such application is for spray painting, wherein atomized paint particles are sprayed on a surface to coat the surface with paint. As there are different models of robots available for the job, it becomes crucial to select the best among them. Multi-criteria decision-making (MCDM) techniques are widely used in various fields to tackle selection problems where there are many conflicting criteria and several alternatives. This work focuses on selecting the best robot among twelve alternatives based on seven criteria, among which payload, speed, and reach are beneficial criteria while mechanical weight, repeatability, cost, and power consumption are cost criteria. Five MCDM techniques, namely combination distance-based assessment (CODAS), complex proportional assessment (COPRAS), combined compromise solution (CoCoSo), multi-attributive border approximation area comparison (MABAC), and visekriterijumsko kompromisno rangiranje (VIKOR) were used for the selection while a weight calculation was performed using an objective weight calculation technique called MEREC. HY1010A-143 was found to be the most suitable robot for spray-painting applications by four of the five techniques used. Correlation studies showed a significant level of correlation among all the MCDM techniques.Web of Science106art. no. 117

Optimization of variable stiffness joint in robot manipulator using a novel NSWOA-MARCOS approach

Robots and robotic systems have become an inevitable part of modern industrial settings. Robotics systems are being introduced for various household services as well. As the interactions between the workspace of robots and humans increases, there is an increased likelihood of unintended harm being caused by the robots to humans due to collisions or abrupt contact. To mitigate this, active and passive compliant mechanisms must be introduced in these systems. In this study, a design optimization case study is carried out for the optimization of a passive compliance mechanism achieved with variable stiffness joints realized by the use of permanent magnets. Three design parameters of the systems, namely, inner stator width, outer stator width, and magnet height, are considered. The objective is to minimize the weight and maximize the maximum torque. A nature-inspired metaheuristic hybridized with a multi-criteria decision-making method is introduced to achieve this. The Non-dominated Sorting Whale Optimization Algorithm (NSWOA) is used for Pareto optimal front generation and MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) is applied to extract the best feasible solution from the Pareto front. We observed 1.8% and 41% improvements as compared to the previous known best design and original design, respectively.Web of Science106art. no. 107

Novel fuzzy measurement alternatives and ranking according to the compromise solution-based green machining optimization

Due to the increase in the impact of different manufacturing processes on the environment, green manufacturing processes are the prime focus of many current pieces of research. In the current article, a green machining process for stainless steel and SS304 and AISI1045 steel has been optimized using newly developed Fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (F-MARCOS) method in the form of two case studies. In the first case study, nose radius, cutting speed, depth of cut, and feed rate are selected as the process parameters whereas surface roughness, consumption of electrical energy, and power factor are the outputs. In the second case study width of cut, depth of cut, feed rate, and cutting speed were the process parameters and material removal rate (MRR), active energy consumption (ACE), and surface roughness (Ra) are the response variables. The MARCOS method ranks the alternatives based on the ideal and anti-ideal solutions for the different criteria. The inclusion of fuzzy logic adds worth to the model by using a linguistic scale to make the method more practical and flexible. Based on the detailed analysis, it ranked the best alternative in case study one which results in a power factor of 0.862, 26.68 kJ of electrical energy consumption, and surface roughness of 0.36 mu m. In the second case study, the best alternative selected by this method gave an MRR of 2400 mm3/min and Ra of 2.29 mu m and utilizes 53.988 kJ ACE.Web of Science1012art. no. 264

Effect of deposition temperature on the tribo-mechanical properties of nitrogen doped DLC thin film

The tribomechanical characteristics of diamond-like carbon (DLC) coatings are notably superior to other hard coatings, making them highly desirable for industrial applications. This study focuses on the synthesis of nitrogen-doped DLC (N-DLC) films through chemical vapor deposition (CVD) methods, with an emphasis on varying the deposition temperature. Comprehensive characterization techniques such as atomic force microscopy (AFM), scanning electron microscopy (SEM), and nanoindentation were employed to investigate the morphological and mechanical attributes of these coatings. The thickness of the films, measured using a Dektak profilometer, demonstrated an increase from 1.9 to 2.8 µm as the deposition temperature rose. Nanoindentation testing revealed that the film deposited at 900°C exhibited the highest hardness (H) and modulus of elasticity (E), measuring 21.95 and 208.3 GPa, respectively. Conversely, the film deposited at 1,000°C showed the lowest values, with H and E at 14.23a and 141.9 GPa, respectively. The H/E ratio of the coatings initially rose from 0.096 to 0.106 as the deposition temperature increased from 800°C to 900°C. However, for deposition temperatures exceeding 900°C the H/E ratio began to decline

A comparative study of linear, random forest and AdaBoost regressions for modeling non-traditional machining

Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.Web of Science911art. no. 201

Investigation of mechanical properties of silver-doped diamond-like carbon coating by varying deposition temperature

The present work shows the influence of deposition temperatures on the mechanical properties of silver (Ag)-doped diamond-like carbon (DLC) coating synthesized by the thermal chemical vapor deposition (CVD) technique. The deposited film showed excellent mechanical and tribological behavior with respect to the lower deposition temperatures. From the EDS analysis, it was confirmed that the percentage of Ag decreased from 9.8% to 8.4% as the deposition temperature increased. The nanoindentation tests at different loads were extensively carried out to observe the mechanical properties of the coating with respect to various deposition temperatures. The coating hardness (H) and Young’s modulus (E) decreased with the rise in furnace temperature, and the Hmax. and Emax. were observed as 29.71 and 251.19 GPa, respectively, for the Ag-DLC coating grown at 800°C at a load of 20 mN. In comparison to other Ag-DLC thin films made using different deposition techniques, the residual stress (σ) was significantly reduced, reaching 0.45 GPa, which is extremely low

Enhancing efficiency in photo chemical machining: a multivariate decision-making approach

Non-Traditional Machining (NTM) outperforms traditional processes by offering superior geometric and dimensional accuracy, along with a better surface finish. Photo Chemical Machining (PCM) represents one such NTM process, using chemical etching for material removal. PCM finds substantial application in the creation of microchannels in pharmaceutical, chemical and energy industries. Several input parameters—such as etchant concentration, etching time and etchant temperature—profoundly influence the machining’s quality and efficiency. Therefore, the optimization of these parameters is crucial. This study presents a comparative analysis of five Multiple Criteria Decision Making (MCDM) techniques—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), Additive Ratio Assessment (ARAS), Weighted aggregated sum product assessment method (WASPAS) and Multi-Attributive Border Approximation Area Comparison Method (MABAC)—for the optimization of the PCM process. Key performance metrics considered are Material Removal Rate (MRR), Surface Roughness (SR), Undercut (Uc) and etch factor (EF). The weights of these criteria were calculated using the Criterion-Induced Aggregation Technique (CRITIC) and was compared with other popular methods like MEREC, Entropy and equal weights. MRR and EF are seen as beneficial criteria, while SR and Uc are perceived as cost criteria. Optimum process parameters were identified as 850 g/L etchant concentration, 40 min etching time and 70°C etchant temperature. Two of the three employed MCDM techniques agreed on these optimal parameters, reinforcing the findings. Furthermore, a strong correlation was observed amongst the employed MCDM techniques, further validating the results

Fabrication and analysis of mechanical properties of PVC/Glass fiber/graphene nano composite pipes

The aim of this work is to examine the conventional moulding method for manufacturing the PVC/Glass fiber/graphene nano composites. Uniform graphene dispersion is observed with the matrix for better bonding. The Mechanical properties of the manufactured nano composites have been done in this work. Three important standard tests were evaluated for the performance of Nano-Composites developed. The composites were developed as flat specimen for pipe applications. The three standards test which includes axial tension, compression and transverse compression is studied. The graphene nano composites were varied 0.5%, 1%, 1.5% and 2 percentages. Based on the results it can be concluded that the increase in the percentages of graphene made a uniform dispersion, which leads to increase in the compressive strength of the Nanocomposite. Increase in the axial compressive strength and stiffness was observed and the increase in the trend value is mainly observed in 1.5 wt% and 2 wt% respectively. The Graphene dispersion and fractured surface morphology of nano composites were examined using scanning electronic microscopy (SEM).It can also be used as an alternative for metal pipes in industries

A temperature-based synthesis and characterization study of aluminum-incorporated diamond-like carbon thin films

The present work deals with the study of various properties of aluminum (Al)-incorporated diamond-like carbon (DLC) thin films synthesized using the atmospheric pressure chemical vapor deposition (APCVD) technique by varying the deposition temperature (Td) and keeping the N2 flow rate constant. Surface morphology analysis, resistance to corrosion, nanohardness (H), and Young’s modulus (E) of the coatings were carried out using atomic force microscopy (AFM), corrosion test, scanning electron microscopy (SEM), and nanoindentation test, respectively. SEM results showed a smoother surface morphology of the coatings grown at different process temperatures. With an increase in process temperature, the coating roughness (Ra) lies in the range of 20–36 µm. The corrosion resistance of the coating was found to be reduced with a consecutive increase in the deposition temperature from 800℃ to 880℃. However, above 880℃, the resistance increases further, and it may be due to the presence of more Al weight percentage in the coating. The nanoindentation result revealed that H and E of the coating increase with an increase in the CVD process temperature. The elastic–plastic property indicated by H/E and H3/E2, which are also indicators of the wear properties of the coating, were studied using the nanoindentation technique. The residual stresses (σ) calculated using Stoney’s equation revealed a reduction in residual stress with an increase in the process temperature

Influence of Filler Material on the Microstructural and Mechanical Properties of 430 Ferritic Stainless Steel Weld Joints

Tungsten Inert Gas (TIG) welding is a commonly used welding technique for ferritic stainless steel, due to its ability to produce high-quality, clean, and precise welds. This welding method provides excellent control over the heat input, making it suitable for thin-walled, high-alloy materials such as ferritic stainless steel. The purpose of this study was to investigate the effect of using two different filler materials, 310 (austenitic) and 410 (ferritic), on the microstructural and mechanical properties of Tungsten Inert Gas (TIG) weld butt joints of 430 ferritic stainless steel (FSS). The results showed that the choice of filler material significantly impacted the dilution percentage, the chromium-nickel equivalent ratio, microstructure, microhardness, and tensile characteristics of the welded joint. The use of 310 filler resulted in a columnar microstructure, whereas the use of 410 filler resulted in a ferritic (acicular ferrite) microstructure with the presence of martensite and austenite. The sample welded with 410 filler demonstrated superior mechanical properties compared to the sample welded with 310 filler. These findings emphasize the importance of selecting the appropriate filler material in order to achieve the desired microstructural and mechanical properties in 430 FSS welded joints