Journal of Micromanufacturing

Improving cutting tool life is very crucial in manufacturing industry. The quality of the finished product is equally important. Investigations are being done to identify different methods to minimize tool wear and improve surface integrity. Micro-texturing of tools is one of the possible methods to improve life of the cutting tool. This work investigates the influence of type of tool: micro-textured, untextured tool; cooling conditions: dry, flood machining; process parameters like cutting speed: low and high, feed: low and high and depth of cut: low and high on surface integrity of machined low alloy steel. Micro-textured tools showed better performance over untextured tools by decreasing the tool crater wear area, roundness error and surface roughness of the finished part. Type of tool, cutting condition, feed and depth of cut influenced roundness error the most. Cutting condition influenced crater wear and feed influenced surface roughness the most.

In manufacturing industries like shipbuilding, automotive, microelectronics, and aerospace, laser forming is a potential new technology. Laser forming is an intricate thermo-mechanical process in which the process mechanisms are determined by the temperature profile across the sheet thickness, which is influenced by laser power, scan speed, number of passes, laser beam diameter, and workpiece geometry. During the process, the material deforms because of compressive stress generated in the underneath region due to the thermal expansion of the irradiated region. This study focuses on the multiple bending and the effect of different process parameters on the laser bending of the sheet. Experiments were performed on 4 mm and 8 mm thick sheets of mild steel sheet of grade IS 2062 E250 using a 2-kW fiber laser with various influencing parameters such as power, scan speed, and laser spot diameter. Hardness and grain size measurements of laser-formed surfaces at different combinations of influencing parameters were performed and the features formed due to laser irradiation were analyzed. An artificial neural network (ANN) model is proposed to predict the bend angle value.

Manufacturing technologies, particularly additive manufacturing (AM), have ushered in innovative practices for complex part production, prominently exemplified by fused filament fabrication (FFF) for the production of complex parts. Significant efforts have been ongoing with a focus on improving performance measures of biodegradable bioplastics, notably polylactic acid (PLA), within the eco-conscious packaging industry. Findings in this field have given rise to FFF technologies for enhancing critical performance measures such as surface roughness (Ra) of food packaging by investigating optimal process parameters such as layer thickness, raster width, raster angle, printing speed, air gap and bed temperature, to name but a few. Additional performance measures affecting geometric part quality, such as thickness variations along horizontal and vertical geometric planes, have been analysed in previous studies to derive solutions for enhancing the correlation between process parameter input and dimensional accuracy. Despite these advancements, research on 3D-printed specimens has typically focused on manufacturing standards with thickness greater than 1 mm, as opposed to the thinner requirements of some plastic packaging products. This practice has left a gap of uncertainty regarding the potential mechanical performance of thin-film 3D-printed products for current and future applications. The necessity to induce fewer infill layers during the FFF process has sparked interest in evaluating the efficacy of process parameters in achieving reliable mechanical performance in surface quality and Z-axial dimensional accuracy compared to conventionally manufactured plastic products. This article, therefore, sought to address this uncertainty using a design of experiments (DoE) approach followed by multi-objective optimisation through the application of genetic algorithm (GA) techniques. From these efforts, optimal process parameters of layer thickness (0.151 mm), raster width (0.514 mm) and raster angle (82.75^o) for the minimisation of Ra and Delta-Z (∇Z) variation in PLA-based FFF specimens. Further data analysis noted variability in the accuracy of Ra predicted R² values depending on the raster angle and void defects at the surface layer, while additional observations indicated an increase in Z-axial variation when a layer thickness of 50% of the overall specimen thickness was utilised. This study concluded by underscoring the necessity for future research in integrating multi-objective optimisation further, with the aim of not only improving the predictability of process parameter optimisation but also the detection, analysis and minimisation of defects using machine learning tools and techniques.

This study explores the fabrication of bone scaffolds using a composite ink of poly-ε-caprolactone (PCL), polyhydroxybutyrate (PHB) and synthesized fluorapatite (FHAp) via response surface methodology optimization to achieve optimal Vickers hardness number (VHN). Characterization with X-ray diffraction confirms FHAp presence and increased crystallinity post-sintering, while Fourier-transform infrared spectroscopy reveals fundamental material interactions. Results show PCL’s softening effect at higher concentrations, PHB’s contribution to decreasing hardness and FHAp’s significant role in reinforcing the composite. Contour plots demonstrate peak hardness at lower PCL and PHB concentrations (<11% wt/v) with 18% wt/v FHAp. The optimum hardness values were found at PCL, PHB and FHAp of 9.754% wt/v, 9.473% wt/v and 24.608% wt/v, respectively, yielding 185.34 VHN. These findings offer insights into optimizing composite concentrations for tailored mechanical properties crucial in bone scaffold design, advancing regenerative medicine and tissue engineering.

Micro products play a critical role in various fields, ranging from consumer electronics like watches and cell phones to advanced medical devices. These intricate parts cannot be manufactured using conventional methods, making microforming a viable solution for producing such components. The goal of the current study is to examine how various forming die conditions affect the production of microparts, namely through the metal microextrusion method. Three distinct die conditions were evaluated: dry die condition, lubricated die condition, and diamond-like carbon (DLC) coated die condition. To demonstrate the practical aspects of microforming, two different metals were employed in the study, with a micropin chosen as the representative micropart for investigation. During the microextrusion tests, various parameters were recorded, including force-displacement response, hardness, and surface roughness of the microextrudates. In comparison to uncoated and lubricated dies, the results show that using a DLC-coated die greatly decreased the extrusion force and enhanced the micropins’ surface polish. Additionally, the greatest pin length produced by the DLC-coated die was 7.9 and 12.7 mm for Cu and Al billets, while the uncoated die yielded a shorter pin length of 5.2 and 7.6 mm. However, the hardness of the microextrudates was found to be higher with the uncoated die, showing a trade-off between surface finish and material hardness.

Since the field’s founding, understanding metal additive manufacturing (AM) procedures and the composition and characteristics of the produced metallic components has come a long way. We are optimizing the microstructure and reducing defect development achieved by fine-tuning essential process parameters like laser power and scan speed, which are crucial initial steps in improving the quality and dependability of AM components. This research aims to achieve complete dense samples by effectively controlling the energy density input during the printing process, which depends entirely on the chosen printing parameters, balancing between insufficient melting and excessive melting. This study adjusted the laser power, scanning speed, and hatch spacing to create components that were as close to fully dense as possible. Using parameters derived from the Rosenthal calculation, we printed samples using 17-4 precipitation hardening (17-4PH) stainless steel. Subsequently, the printed samples were examined using an optical microscope to detect imperfections and correlate them with the specific conditions applied in each case.

This study investigates the impact of plasma flushing efficiency (PFE) on solidified white layer thickness (WT) and heat-affected zone (HAZ) depth during micro-hole fabrication using micro-electrical discharge machining (µEDM) of Inconel-718. Temperature distribution was simulated using axisymmetric three-dimensional finite element models. Full factorial designed experiments (four pulse-on time (Ti) and five pulse-current (I) levels) were conducted to validate the simulation results. The results reveal that extending the pulse-on duration increases WT and HAZ depth owing to longer temperature exposure during discharge. In contrast, higher pulse current causes a minor reduction in both WT and HAZ depth, implying a link between plasma behaviour and heat distribution. For constant discharge energy, higher pulse current coupled with shorter pulse-on time leads to a reduction in both surface damage characteristics, potentially due to improved PFE. The finite element model demonstrates good predictive accuracy with average deviations of 9.65% and 12.65%, compared to experimental data for WT and HAZ depth, respectively. This study highlights the effectiveness of FEM modelling in predicting surface integrity in µEDM and provides insights into optimizing process parameters for Inconel-718 machining.

In this article, we explore the first application of fuzzy cognitive maps (FCMs) for analyzing the variables of small-scale clay 3D printing processes, addressing an evident gap in methodologies within additive manufacturing (AM). While clay, as a sustainable and recyclable material, presents unique opportunities for customization in manufacturing, its inherent fragility poses significant challenges, including volumetric changes and structural instability. The proposed FCM methodology integrates advanced modeling techniques that extend beyond traditional statistical approaches, enabling a dynamic analysis of interrelated process variables. By systematically characterizing these factors, the study identifies critical parameters influencing print quality, contributing to the development of standardized best practices for clay AM. The findings underscore the potential of FCMs to enhance decision-making in process management and lay the groundwork for future research into optimizing 3D printing technologies with ceramic materials. This research ultimately aims to facilitate sustainable and efficient production methods in the context of circular economy principles. Furthermore, it may support the creation of guidelines, protocols, and standards that define optimal parameters and procedures for 3D printing with clay, thereby facilitating reproducibility and consistent quality across diverse production environments.

Micromilling of the biomaterial SS316L is a precision manufacturing technique employed to fabricate micro-scale features. This study utilized a hybrid TOPSIS-AHP (Technique for Order Preference by Similarity to Ideal Solution-Analytical Hierarchy Process) optimization method to simultaneously evaluate and optimize multiple response variables during the micromilling of SS316L stainless steel. The impact of critical process parameters—spindle speed (SS), feed rate (FR), and depth of cut (DC)—on material subtraction rate (MSR) and surface roughness (SR) was investigated using a Taguchi L27 orthogonal array design. Statistical analyses, including analysis of variance (ANOVA), F-tests, and significance testing at a 95% confidence level, unveiled that FR was the most influential factor affecting MSR, facilitating 63.02% to its variation, whereas SS predominantly influenced SR, accounting for 84.45% of its variation. Regression models were developed to predict MSR and SR, exhibiting strong predictive accuracy with R² values of 91.28% (MSR) and 89.51% (SR), alongside low average prediction errors of 8.72% and 10.49%, respectively. The optimal parameter settings—SS at 4500 rpm, FR at 0.05 mm/rev, and DC at 0.5 mm—achieved the highest MSR and the lowest SR, thereby validating the efficacy of the proposed multi-response optimization strategy.

The scale and value of surface roughness affect the wetting, fatigue, damping, emissivity, heat transfer, and aesthetic characteristics leading to applications in food, chemical, medical, and traditional mechanical industries. Starting with the definitions of roughness parameters, in this work, we presented a comprehensive review of various aspects required to characterize surface roughness. Subsequently, a well-arranged literature survey on the use of several roughness-sensing methods for various machining processes was presented. In tandem, the theoretical aspects of sensing and their limits on resolution, sensitivity, range, and accuracy have been described. Thereafter, mathematical models to reconstruct surface profiles were outlined. We also presented the ways for online measurements of roughness and integration of the former with the standards of Industry 4.0. With details on all aspects of roughness, this work is expected to assist the researchers and engineers in the selection of appropriate roughness measurement techniques that lead to application in various industries.

The subject of surface texturing has been one of the most sought and rigorously researched domains of interest for the research community in the manufacturing field for a few decades. The reason for this is the potential applications of surface texturing techniques in various sectors of manufacturing and services. One of the foremost applications where surface texturing has proven to be highly beneficial is cutting tool manufacturing. There are several other applications of surface texturing like fabrication of aerospace and marine structures where the research is still not well established. Unlike these applications, surface texturing in metal cutting has been established and documented by researchers to a level to get the process implemented in practical industry use cases. The present work critically assesses the work related to surface texturing in metal cutting for tools (cutting tools, drills, milling tools, etc.) that have highlighted the influence of surface texturing on performance improvement. Various manufacturing techniques used to create surface textures and their underlying mechanisms for performance improvement have been discussed. The synergistic effects coupled with the benefits of employing texturing with coatings/ lubrication/ cooling have also been critically analyzed over a wider range of process parameters and work material combinations. Furthermore, the existing finite element (FE) models for textured surfaces have been discussed to identify the ease/complexity of the simulation to achieve the advantages of texturing and their practical validity.