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This paper presents a study of the development of surface roughness model when turning the mild steel hardened up to 484 HV with mixed alumina ceramic (KY1615) and coated alumina ceramic cutting tools (KY4400). The model was developed in terms of main cutting parameters such as cutting speed, feed rate and depth of cut, using response surface methodology. The established equation indicated that the feed rate affected the surface roughness the most, but other parametres remined stable for arithmetic average height parametre (Ra). However, it decreased with increasing the cutting speed, and with the starting and finishing point of cut for ten point height parametre (Rz). The cutting speed and the depth of cut had a slight effect on surface roughness values of Ra, Rz when using KY4400 cutting tools. Furthermore, the average surface roughness value of Ra was about 0.926 um, 1.089 um for KY1615, KY4400 cutting tools, respectively. The predicted surface roughness was found to be very close to experimentally observed ones at 95% confidence level. Moreover, analysis of variance indicated that squares terms were significant but interaction terms were insignificant for both cutting tools.

The goal of this study is to carry out multi‐objective optimization by considering minimization of surface roughness (Ra) and build time (T) in selective laser sintering (SLS) process, which are functions of “build orientation”. Evolutionary algorithms are applied for this purpose. The performance comparison of the optimizers is done based on statistical measures. In order to find truly optimal solutions, local search is proposed. An important task of decision making, i.e. the selection of one solution in the presence of multiple trade‐off solutions, is also addressed. Analysis of optimal solutions is done to gain insight into the problem behavior.

The minimization of Ra and T is done using two popular optimizers – multi‐objective genetic algorithm (non‐dominated sorting genetic algorithm (NSGA‐II)) and multi‐objective particle swarm optimizers (MOPSO). Standard measures from evolutionary computation – “hypervolume measure” and “attainment surface approximator” have been borrowed to compare the optimizers. Decision‐making schemes are proposed in this paper based on decision theory.

The objects are categorized into groups, which bear similarity in optimal solutions. NSGA‐II outperforms MOPSO. The similarity of spread and convergence patterns of NSGA‐II and MOPSO ensures that obtained solutions are (or are close to) Pareto‐optimal set. This is validated by local search. Based on the analysis of obtained solutions, general trends for optimal orientations (depending on the geometrical features) are found.

A novel and systematic way to address multi‐objective optimization decision‐making post‐optimal analysis is shown. Simulations utilize experimentally derived models for roughness and build time. A further step could be the experimental verification of findings provided in this study.

This study provides a thorough methodology to find optimal build orientations in SLS process. A route to decipher valuable problem information through post‐optimal analysis is shown. The principles adopted in this study are general and can be extended to other rapid prototyping (RP) processes and expected to find wide applicability.

This paper is a distinct departure from past studies in RP and demonstrates the concepts of multi‐objective optimization, decision‐making and related issues.

This paper aims to examine the performance of the machining parameters used in the hard-turning process of DIN 1.2738 mold steel and identify the optimum machining conditions.

Experiments were carried out via the Taguchi L18 orthogonal array. The evaluation of the experimental results was based on the signal/noise ratio. The effect levels of the control factors on the surface roughness and flank wear were specified with analysis of variance performed. Two different multiple regression analyses (linear and quadratic) were conducted for the experimental results. A higher correlation coefficient (R²) was obtained with the quadratic regression model, which showed values of 0.97 and 0.95 for Ra and Vb, respectively.

The experimental results indicated that generally better results were obtained with the TiAlN-coated tools, in respect to both surface roughness and flank wear. The Taguchi analysis found the optimum results for surface roughness to be with the cutting tools of coated carbide using physical vapor deposition (PVD), a cutting speed of 160 m/min and a feed rate of 0.1 mm/rev, and for flank wear, with cutting tools of coated carbide using PVD, a cutting speed of 80 m/min and a feed rate of 0.1 mm/rev. The results of calculations and confirmation tests for Ra were 0.595 and 0.570 µm, respectively, and for the Vb, 0.0244 and 0.0256 mm, respectively. Developed quadratic regression models demonstrated a very good relationship.

Optimal parameters for both Ra and Vb were obtained with the TiAlN-coated tool using PVD. Finally, confirmation tests were performed and showed that the optimization had been successfully implemented.

This paper aims to analyze changes in the surface topography of the work rolls during skin passing. Cold rolled steel sheets are additionally subject to skin pass rolling to form an appropriate surface topography. This operation should facilitate the process of further metal forming of steel sheets, such as deep drawing, painting, etc. The surface topography of steel sheets is determined by the surface topography of the work rolls as well as the skin pass rolling parameters (rolling speed, elongation, roll force, etc.). Suitable preparation and selection of roll surface topography influences the degree of rolls wear and the surface topography of steel sheets as well.

Two-dimensional (2D) and three-dimensional (3D) roughness measurements of work roll surface before, during and after finishing of skin pass rolling of steel sheets are presented in the paper. The measurements were performed on four sets of work rolls with different surface topography.

The appearance of the surface of rolls obtained from the analysis of 3D roughness, the values of selected parameters of the 3D roughness and relative changes of the roughness parameter Ra/Sa depending on the length of the skin passed steel sheets are presented.

The wear of rolls is different depending on work surface topography.

The aim of this paper is to analyze changes in the surface topography of the work rolls during skin passing. It was expected that the surface of work rolls with more summits at similar average roughness Ra will change much faster than the surface with fewer summits. For this purpose, preliminary tests were performed in an industrial environment on four pairs of work rolls, including two pairs of rolls that were hard chromium-plated.

The purpose of this paper is to provide a theoretical foundation for improving the selective laser melting (SLM) surface roughness. To improve the part’s surface quality during SLM process, the upper surface roughness of SLM parts was theoretically studied and the influencing factors were analyzed through experiments.

The characteristics of single track were first investigated, and based on the analysis of single track, theoretical value of the upper surface roughness would be calculated. Two groups of cubic sample were fabricated to validate SLM parts’ surface roughness, the Ra and relative density of all the cubic parts was measured, and the difference between theoretical calculation and experiment results was studied. Then, the effect of laser energy density on surface roughness was studied. At last, the SLM part’s surface was improved by laser re-melting method. At the end of this paper, the curved surface roughness was discussed briefly.

The SLM upper surface roughness is affected by the width of track, scan space and the thickness of powder layer. Measured surface roughness Ra value was about 50 per cent greater than the theoretical value. The laser energy density has a great influence on the SLM fabrication quality. Different laser energy density corresponds to different fabricating characteristics. This study divided the SLM fabrication into not completely melting zone, balling zone in low energy density, successfully fabricating zone and excessive melting zone. The laser surface re-melting (LSR) process can improve the surface roughness of SLM parts greatly without considering the fabricating time and stress accumulation.

The upper surface roughness of SLM parts was theoretically studied, and the influencing factors were analyzed together; also, the LSR process was proven to be effective to improve the surface quality. This study provides a theoretical foundation to improve the surface quality of SLM parts to promote the popularization and application of metal additive manufacturing technology.

Selective laser melting (SLM) process is an additive manufacturing method that uses computer-aided design to fabricate complex components layer-by-layer. Surface roughness is one of the primary drawbacks of SLM process; hence, the purpose of this paper is to present a parametric study and optimisation of fundamental parameters, including scan power, speed, inclined angle and layer thickness on surface roughness during selective laser melting of Hastelloy X.

Parametric significance on surface finish was analysed using analysis of variance and response surface methodology. General agreement between predicted and measured values was achieved. Surface characteristics of both up-skin and down-skin with various angles were covered within the investigated range.

Both experimental and statistical analysis showed that surface roughness of up-skin was primarily influenced by scan power, inclined angle and layer thickness while down-skin was more affected by the former two factors. Melt pool shape and staircase size were found to determine the up-skin surface, whereas attached particles were responsible for down-skin surface roughness.

As per our understanding, this manuscript provides valuable insight into the surface quality problem of SLM, which is a very critical issue for up-grading the process for manufacturing real components. This manuscript helps promote improved knowledge and understanding of the attributes and capabilities of this rapidly evolving 3D printing technology. Moreover, it establishes usable processing window and helps obtain optimal conditions, thus offering useful information to professionals working in this field. By combining experiments with statistical analysis, both practice and theory relevant to SLM process are further developed.

Polyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process.

PETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles.

PLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one.

The combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding.

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.

This study aims to apply an electrochemical grinding (ECG) technology to improve the material removal rate (MRR) under the premise of certain surface roughness in machining U71Mn alloy.

The effects of machining parameters (electrolyte type, grinding wheel granularity, applied voltage, grinding wheel speed and machining time) on the MRR and surface roughness are investigated with experiments.

The experiment results show that an electroplated diamond grinding wheel of 46# and 15 Wt.% NaNO₃ + 10 Wt.% NaCl electrolyte is more suitable to be applied in U71Mn ECG. And the MRR and surface roughness are affected by machining parameters such as applied voltage, grinding wheel speed and machining time. In addition, the maximum MRR of 0.194 g/min is obtained with the 15 Wt.% NaCl electrolyte, 17 V applied voltage, 1,500 rpm grinding wheel speed and 60 s machining time. The minimum surface roughness of Ra 0.312 µm is obtained by the 15 Wt.% NaNO₃ + 10 Wt.% NaCl electrolyte, 13 V applied voltage, 2,000 rpm grinding wheel speed and 60 s machining time.

Under the electrolyte scouring effect, the products and the heat generated in the machining can be better discharged. ECG has the potential to improve MRR and reduce surface roughness in machining U71Mn.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0341/

In this study, experimental studies were carried out using different process parameters of the soft pneumatic gripper (SPG) fabricated by the fused deposition modeling method. In the experimental studies, the surface quality of the gripper was examined by determining four different levels and factors. The experiment was designed to estimate the surface roughness of the SPG.

The methodology consists of an experimental phase in which the SPG is fabricated and the surface roughness is measured. Thermoplastic polyurethane (TPU) flex filament material was used in the fabrication of SPG. The control factors used in the Taguchi L16 vertical array experimental design and their level values were determined. Analysis of variance (ANOVA) was performed to observe the effect of printing parameters on the surface quality. Finally, regression analysis was applied to mathematically model the surface roughness values obtained from the experimental measurements.

Based on the Taguchi signal-to-noise ratio and ANOVA, layer height is the most influential parameter for surface roughness. The best surface quality value was obtained with a surface roughness value of 18.752 µm using the combination of 100 µm layer height, 2 mm wall thickness, 200 °C nozzle temperature and 120 mm/s printing speed. The developed model predicted the surface roughness of SPG with 95% confidence intervals.

It is essential to examine the surface quality of parts fabricated in additive manufacturing using different variables. In the literature, surface roughness has been examined using different factors and levels. However, the surface roughness of a soft gripper fabricated with TPU material has not been examined previously. The surface quality of parts fabricated using flexible materials is very important.