Optimization of Fused Deposition Modeling Parameters for Improved PLA and ABS 3D Printed Structures

The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

/pdf/a-review-of-indirect-tool-condition-monitoring-systems-and-4xqg1e60db.pdf

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.

Use of Castor Oil as Cutting Fluid in Machining of Hardened Stainless Steel with Minimum Quantity of Lubricant

In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece mate...

https://journals.sagepub.com/doi/pdf/10.1177/0954406219873932

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

The paper addresses the assessment, modeling, and optimization study of surface roughness in finish dry hard turning (FDHT) of AISI 4340 steel with coated ceramic tool by considering the cutting speed, axial feed, depth of cut, and nose radius as machining parameters. Thirty sets of longitudinal turning trials based on central composite (CCD) design of experiments (DOEs) are performed, and response surface methodology (RSM), particle swarm optimization (PSO), and finally, Gilbert’s approach are subsequently applied for mathematical modeling, response optimization, tool life estimation, and economic analysis. Additionally, various diagnostic tests have been executed to check the statistical significance and validity, adequacy, effectiveness, and fitness of data of the proposed model using analysis of variance (ANOVA) and Anderson-Darling normal probability test. Results indicated that nose radius and feed are the most significant controlled as well as dominant factors for hard turning operation if the minimization of the machined surface roughness is considered. The RSM model combined with PSO technique leads to minimum surface roughness value similar to cylindrical grinding of 0.2021 μm, corresponding to optimum process parameters: 220 m/min of cutting speed, 0.05 mm/rev of feed, 0.193 mm of depth of cut, and 1.6 mm of tool nose radius. Finally, under pre-cited optimum machining conditions, tool life was evaluated to perform cost analysis for the economical justification and feasibility of coated ceramic tools in hard turning. The total machining cost per piece is ensued to be lower ($0.34) as a consequence of higher tool life (44 min), reduction in downtime, and enhancement in savings, which finds economical benefits in hard turning. The novelty aspects of the present work are that (i) it demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing coated ceramic tool in hard turning processes considering technological, economical, and ecological aspects, which are helpful and efficient from industrial point of view and (ii) it contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45–55 HRC.

Surface Roughness Analysis for Economical Feasibility Study of Coated Ceramic Tool in Hard Turning Operation

This study reports an optimization of finish turning of hardened AISI D6 (60 HRC) cold work tool steel with ceramic and cubic boron nitride cutting tools using grey relational analysis. Optimizatio...

Application of grey relational analysis in high-speed machining of hardened AISI D6 steel:

Coating Graphene Nanoplatelets onto Carbon Fabric with Controlled Thickness for Improved Mechanical Performance and EMI Shielding Effectiveness of Carbon/Epoxy Composites

In this study, various nano and micro particles with different properties, including density, surface area, purity and particle morphology were used as reinforcement particles for the production of polymer composite filaments to be used for 3D printing. Acrylonitrile Butadiene Styrene (ABS) was matrix material and Multi wall carbon nanotubes (MWCNTs), SiO2, ZrB2, and, Al particles were reinforcements. Production of the composite filaments was carried out by using a twin screw extruder. Produced composite filaments were characterized via Differential Scanning Calorimeter (DSC), Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), tensile test and surface roughness tests. Results showed that addition of micro/nano particles into ABS matrix improved the Ultimate Tensile Strength (UTS) of the composites by around 16% compared to non-reinforced one. As a result of reinforcing with micro particles, ZrB2 and Al, the tensile strain of neat-ABS filament increased by 17.8% and 40%, respectively

/pdf/production-and-characterization-of-composite-filaments-for-32llot3rex.pdf

Production and Characterization of Composite Filaments for 3D Printing

Three dimensional (3D) glass fiber epoxy composites reinforced with nano-SiO2 particles at ratios of 0.5 wt.%, 1 wt.%, 2 wt.% and 3 wt.%, were manufactured using the vacuum infusion method to improve the mechanical properties. Differential scanning calorimetry, thermo-gravimetric analysis, particle dispersion detection by color measurement, scanning electron microscopy, energy dispersive X-ray spectroscopy mapping, three point bending and tensile testing were performed to characterize the nano-particle-reinforced 3D composites. Mapping and color measurement revealed good dispersion of the nano-particles. The mechanical tests showed improvement rates of 52 % and 64 % respectively on warp and weft directions compared with the non-reinforced 3D composite. The tensile strength of the nano-SiO2 reinforced 3D woven composites also increased to 6 % and 10 % according to the warp and weft directions compared with the non-reinforced 3D composite.

Mechanical properties of nano-SiO2 reinforced 3D glass fiber/epoxy composites

High Speed Hard Turning of AISI S1 (60WCrV8) Cold Work Tool Steel

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