Optimal Setting Of Process Parameters Research Articles

Experimental investigation into the fabrication of green body developed by micro‐extrusion‐based 3D printing process

AbstractIn the present work, a micro‐extrusion‐based three‐dimensional (3D) printing process has been used to fabricate a metal‐polymer‐based green body. The fabricated parts consisted of carbonyl iron particles with a binder (polylactic acid) and a solvent mixture. From the pilot experiments, it was found that the process parameters, namely, Fe loading, layer thickness,and infill density, affect the green density, shrinkage, and surface roughness of the fabricated part. Moreover, to develop a statistical model with significant factors, experiments were performed based on the design of the experiment using a central composite design method. The experimental results revealed that green density and surface roughness of 3D printed parts increased with the increase in Fe loading and infill density. On the contrary, the shrinkage in the fabricated part decreased with an increase in Fe loading and increased with the rise in infill density. Further, with an increase in layer thickness, the green density decreased while shrinkage and roughness were observed to increase. To verify the accuracy of the developed model, confirmation experiments were also performed at the optimum set of process parameters obtained by the genetic algorithm optimization technique.

Drag reduction studies in water using polymers and their combinations

Enhancing the flow rate by reducing the drag in turbulent flows of fluids is a highly significant phenomenon with reference to many industries viz., oil, marine, irrigation, biomedical etc to reduce power consumption. This drag reduction could be mostly achieved by adding polymeric additives, also referred to as drag reducing agents (DRAs) to the fluid in trace amounts. In this study, pressure driven set-up with pipes of different diameters and roughness levels has been employed to study drag reduction by polymers like PEO, PAM, PAA, HPMC in water systems. The objective of the work was to identify the optimum set of process parameters in terms of polymer nature and its concentration, pipe diameter and its roughness, methodology adopted to maximize maximum drag reduction. The results achieved were then validated with well proven concepts and reported studies.

Optimisation of turning parameters for minimising specific cutting energy with use of different cooling/lubricating techniques

The objective of this paper is optimisation of the machining parameters using the Taguchi method to obtain minimum specific cutting energy. Considering the machining parameters such as speed, depth of cut and feed rate, a series of turning experiments were conducted to measure the main cutting force required to calculate specific cutting energy. The experiments were performed on C45E steel bars using coated carbide inserts under different cooling/lubrication conditions, namely, flooded cooling, minimal quantity lubrication and high pressure cooling. The concept of signal-to-noise ratio was employed to find the optimal levels of the process parameters so as to get the lowest value of specific cutting energy. Analysis of variance was applied to estimate the significance of control factors affecting specific cutting energy. Finally, the results of confirmation experiments with optimal process parameters settings showed that the Taguchi method can be successfully applied in the optimisation of process parameters for minimum specific cutting energy.

IIoT Framework for SME level Injection Molding Industry in the Context of Industry 4.0

The Internet of Things (IoT) is a hype topic for nearly a decade now. Broadly growing, millions of devices get direct access to the Internet provides plenty of applications such as smart homes or mobile health management. This trend can also be found in the industry where IoT components hardened for these environments are introduced, called Industrial IoT (IIoT) devices which can be either sensors or actors, as well as mobile equipment such as smartphones, tablets, and smart glasses. Consequently, mobile communication becomes universal in smart factories. IIoT devices provide massive data on temperature, pressure, machine states, etc. But still, most of the SME level industries in the Asian region are new to these technological advancements. They still operate their facilities ith conventional setups without absorbing the new opportunities which are presented by IoT. In the plastic injection molding industry, process parameters perform a significant role in the quality of the output product. During the manufacturing process, these process parameters have to deal with various factors such as quality and type of materials, requirement tolerance levels of the output product, Environmental conditions like temperature and humidity, etc. Injection molding has been a challenging process for many SME level manufacturers to produce products while meeting the quality requirements at the lowest cost. Most of them are unable to reach the global market in the injection molding industry due to the non-availability of the proper methods to determine the process parameters for injection molding. During production, quality characteristics may differ due to drifting or shifting of processing conditions caused by machine wear, environmental change, or operator fatigue. By determining the optimal process parameter settings productivity and quality will increase while reducing the cost of production. In this paper, we suggest an Industrial IoT framework that can develop for small- and medium-sized enterprises (SMEs) level industries to optimize their production facility. With the presented framework SME level industries can start to inherit IoT devices into their production floor to manage and monitor production parameters in real-time while improving the quality of the production.

Optimization of Process Parameters of EDM of Inconel 617 by Taguchi Based PCA and GRA Technique and Effect of Recast Layer Formation

Inconel materials are manufactured in various series which have differences in their chemical composition and hence in its physical and chemical properties. These alloy materials are known for their high strength, corrosion resistance and oxidation resistance. They are not easily machine-able through conventional machining, due to rapid work hardening tendency, high toughness and hardness, tendency to form built up edges, hence for their machining, non conventional machining equipments like EDM are used. A number of experiments has been performed to optimize process parameters of EDM on different series of Inconel material like Inconel 625, Inconel 718, Inconel 601, Inconel hastelloy C-276, Inconel 690 etc. by different optimization technique Taguchi, PCA, GRA etc however there is a lack of data available for optimization of process parameters of EDM machining for Inconel 617. So in this paper optimization of process parameters like Pulse on time, peak current, gap voltage have been done by taking Material Removal Rate and Tool Wear Rate as response variables. Other process parameters have been kept constant during EDM process. It is found that for different values of process parameters pattern of variation of MRR and TWR is different. Also the effect of recast layer formation on MRR and TWR has been analyzed and it is found that at high Peak Current and high Pulse on Time, tendency of formation of recast layer is high. Formation of recast layer adversely affects MRR and TWR i.e. it reduces MRR and TWR. Also Results obtained from PCA and GRA has been compared and it is found that both methods give same optimum set of process parameters but they have differences in pattern of variation of MRR and TWR.

Grey-WPCA Based Parametric-Optimization and Modelling of Chromium Powder Mixed Surface-Electro Discharge Diamond Grinding of Inconel 600 Using RSM

Abstract The PMS-EDDG is an effective hybrid process to beat the problems identified in machining of Inconel 600 through the conventional machining process. This hybrid-machining-process is a combination of SDG and Powder Mixed-EDM process. Modeling, comparative investigation, determination of optimal setting of process factors, and various surface developments in S-EDDG of Inconel 600 with chromium and absence of chromium-powder blended dielectric fluid (DF) have been narrated in this research work. One set of 31 experiments with chromium-powder and another set of 31 experiments without chromium-powder mixed DF was conducted on this set-up in accordance with the C-C-R-D plan of experimentation. The developed models through RSM support to investigate the behavior of input process-factors over the responses. The input factors in this research work are ampere-current (I), pulse-on-time (Ton), wheel speed (WS), and duty cycle (DC). The MRR and Ra are the output-responses in this machining process. The optimum setting of process-parameters is computed through the integrated Grey-Taguchi based WPCA-approach. The confirmation experiment is conducted on this set-up at the optimum-condition and its results display the agreeable matching among the actual and predicted values. The WMPI is improved by 0.414. The SEM investigation has been conducted at the optimum-condition on the produced machined-surfaces and on the produced white recast-layer thickness.

Influence of drinking water and graphite powder concentration on electrical discharge machining of Ti-6Al-4V alloy

In the present investigation, the influence of drinking water as dielectric fluid and graphite powder concentration on electric discharge machining of Ti-6Al-4V alloy is studied. Taguchi parameter design approach was used to get an optimal parametric setting of EDM process parameters namely, discharge current, pulse on time, pulse off time and graphite powder concentration that give up optimal process performance characteristics such as material removal rate, surface roughness and recast layer thickness. Also individual effect of process Parameters on performance characteristics was studied. Experiments were conducted on experimental setup with modified dielectric fluid circulating system. To identify the significance of parameters on measured response, the analysis of variance (ANOVA) has been done. Further empirical models were developed by performing nonlinear regression analysis to predict the process performance characteristics. Confirmation tests were conducted at their respective optimum parametric settings to verify the predicted optimal values of performance characteristics.

Evaluation of Oxalobacter formigenes DSM 4420 biodegradation activity for high oxalate media content: An in vitro model.

Oxalate is a common component of many foods typically present as a salt of oxalic acid, which will be excreted in the urine. Hyperoxaluria is known to be a considerable risk factor for urolithiasis, and formation of oxalate kidney stone. Oxalate degradation by the probiotic anaerobic bacterium Oxalobacter formigenes DSM 4420 has high yield and efficiency both in the human colon helping to prevent hyperoxaluria and disorders such as the development of kidney stones and as a novel approach in reducing the high concentration of foodstuff oxalate content such as tea, coffee, and nuts. For determining the effective factors to enhance high concentration oxalate biodegradation activity of Oxalobacter formigenes DSM 4420 Plackett-Burman screening design was applied to evaluate the impact of 10 process variables. After determining the main factors by screening design, a response surface methodology was used to find suitable treatment combination for oxalate biodegradation by this probiotic. A second-order quadratic model estimated that the highest biodegradation of 60.2% was achieved in presence of 1.35 (g/L) inulin, 36.56 (g/L) glucose, 26 (mmol/L) ammonium oxalate, and pH 6. In other word, the optimum point showed that in the above condition the high concentration of ammonium oxalate content of 26 mmoL/L will reach to 9.95 mmoL/L. Reconfirmation experiment showed the validity of predicted optimum conditions. A surface model using the RSM and optimizing this model using the GA technique, resulted in a useful method of finding an optimal set of process parameters.

Joining of tubular steel–steel by unconventional magnetic pulse force: environmentally friendly technology

Electromagnetic welding uses environment-friendly, unconventional non-contacting magnetic pulse force for joining of two metals. The present paper focuses on the welding of tubular mild steel to two different steel bars, ferrite-pearlite 1018 carbon steel and austenite 304 stainless steel using a 40 kJ electromagnetic instrument. A qualitative metallurgical bonding was obtained for a selected set of optimum process parameters. The bonded region did not show localized melting for a mild steel–carbon steel joint and was found to be homogeneous liquid state bonding for a mild steel–stainless steel joint within a restricted distance of \(3~\upmu \hbox {m}\) from the interface. Both the joints indicated good peel strength and leak tightness. Simulation studies were validated using experimental parameters such as voltage, current, impact velocity, magnetic flux and displacement.

STUDY ON FORMING FORCES OF PARTS PROCESSED BY SINGLE POINT INCREMENTAL FORMING WITH DUMMY SHEET

The present paper describes the experimental investigation on influence of process parameters on maximum forming force in Single Point Incremental Forming (SPIF) process using dummy sheet. Process parameters namely dummy sheet thickness, tool size, step size, wall angle and feed rate are selected. Taguchi L18 orthogonal array is used to design the experiments. From the analysis of variance (ANOVA) dummy sheet thickness, tool size, step size and wall angle are significant process parameters while feed rate is insignificant. It is found that as dummy sheet thickness, tool size, step size and wall angle increase magnitude of peak forming force increases while there is marginal decrease in forming force as feed rate increases. Predictive model is also developed for forming force. Validation tests are performed in order to check the accuracy of developed model. Optimum set of process parameters is also determined to minimize forming force. Experimental results are in good agreement with results predicted by the developed mathematical model.

Complex taper profile machining of WC-Co composite using wire electric discharge process: analysis of geometrical accuracy, cutting rate, and surface quality

Complex profiles, comprising curved features and inclined surfaces, are an inherent attribute of tungsten carbide (WC-Co) tooling applications like dies, molds, and cutting tools. Such profiles are difficult to machine by conventional processes due to high hardness of WC-Co. Wire electric discharge machining (WEDM) is a frequently employed non-traditional machining process for producing intricate shapes and complex geometries in difficult-to-cut materials. However, this process offers its own challenges regarding machining accuracy of arced profiles such as undercutting or overcutting of material. Machining task becomes more complicated if arced profiles are to be machined at a certain taper angle. Keeping in view the stringent requirements of productivity and geometrical accuracy for WC-Co tooling, current research is focused on comprehensively evaluating the performance of WEDM process for producing complex tapered profiles in WC-Co composite. Furthermore, instead of using simple brass wire, a zinc-coated wire electrode is employed to enhance cutting rate. The impact of WEDM control variables namely, pulse-on/off time, servo voltage, and wire tension, in conjunction with workpiece taper, is studied on geometrical accuracy, cutting rate, and surface quality of complex tapered profile. Results of experimentation are analyzed using various statistical techniques. Scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) analysis are carried out for in-depth evaluation of machining results. Experimental results show that a complex tapered profile can be generated with as small as 0.16° angular error and 6-μm radial error using optimal parameter settings. Optimal settings of process parameters result into 33.3% reduction in angular error, 14.3% reduction in radial error, 12% increase in cutting speed, and 14.4% reduction in surface roughness of workpiece.

Influence of Process Parameters on Chlorinated Biocide Decontamination by li-CO2 on Artificially Contaminated Model Materials

ABSTRACTPreviously, heavy use of biocides for the treatment of objects made of organic materials in museum collections (e.g. ethnographic, historical collections) was very common. Now suitable decontamination methods/technologies are being sought. A decontamination treatment by li-CO2 was optimized that considered the specific requirements of museum objects. The treatment was tested on model materials artificially contaminated with biocide solutions containing dichlorodiphenyltrichloroethane (DDT), pentachlorophenol (PCP), lindane, and permethrin or cypermethrin. High decontamination was achieved for DDT, lindane, PCP, and permethrin on artificially biocide loaded wool and wood model materials. Optimal process parameter settings for li-CO2 decontamination include a single 30 min cycle for woolen materials and three sequential 30 min cycles each for wood. These methods allow a reduction of at least 90% of all biocides for wool and between 70% and 85% for wood. Decontamination of the latter was more effective for less polar biocides as DDT and permethrin. Despite a significant improvement in decontamination for wood using co-solvents such as acetone, ethanol, and methyl tertiary-butyl ether (MTBE), their use is not recommended due to the increased risk of damage to the objects, in particular if a surface coating is present.

Effect of Process Parameters on Mechanical Strength of Fabricated Parts using the Fused Deposition Modelling Method

This study investigated the effects of process parameters on mechanical properties of fabricated parts of the Polylactic acid (PLA) materials using fused deposition modeling (FDM) in 3D printing Technology. First, Taguchi method in the design of experiment (DOE) approach was applied to generate a design matrix of three process parameters namely; printing speed, extrusion temperature and layer thickness. A L9 array with 9 specimens was used for fabrication under various process parameters by the Builder 3D printer. Tensile test was implemented and recorded in accordance with ASTM D368 standard. Achieved data were analyzed using the Minitab software to show the effect of each process parameter on mechanical properties. Secondly, a regression model was developed to predict the trend of response in case of change in setting of parameters and estimating the optimal set of process parameters which creates the strongest FDM parts. The achieved optimum parameters were used to validate the fabricated samples for tensile testing. According to the results, the best mechanical strength of fabricated parts was achieved with printing speed of 48 mm/s, extrusion temperature of 220 degree of celsius (C) and the layer thickness of 0.15 mm. Also, the extrusion temperature was the most influencing factor on ultimate tensile stress.

Method for Attaining Dimensionally Accurate Conditions for High-Resolution Three-Dimensional Printing Ceramic Composite Structures Using MicroCLIP Process.

Continuous liquid interface production (CLIP) utilizes projection ultraviolet (UV) light and oxygen inhibition to transform the sequential layered three-dimensional (3D) manufacturing into a continuous fabrication flow with tremendous improved fabrication speed and structure integrity. Incorporating ceramic particles to the photo-curable polymers allows for additive manufacturing of ceramic parts featuring sophisticated geometries, mitigating the difficulties associated with traditional manufacturing processes. The presence of ceramic particles within the ink, however, strongly scatters the incident UV light. In the high-resolution CLIP (microCLIP) process, the scattering effect can significantly alter the process characteristics, resulting in broadening of lateral feature dimensions alongside curing depth reduction. Varying exposure conditions to accommodate scattering additionally affects the oxygen deadzone thickness (DZ), which is dependent on power of incident light. This introduces a systematic defocusing error for large deadzone thickness to further complicate process control, such as the unwanted narrowing of part features. In this work, we developed a systematic framework for process optimization by balancing those effects via experimental characterization. We showed that the reported method can provide a set of optimal process parameters (UV power and stage speed) for high-resolution 3D fabrication in accommodating the distinct characteristics of given photo-curable ceramic ink. The method to optimize process parameter was validated experimentally via fabricating a gradient index Luneburg lens comprising densely packed woodpile building-blocks with a strut width of 100 μm and a layer thickness of 60 μm using microCLIP at dimensionally accurate exposure conditions.

Solid State Additive Manufacturing of Acrylonitrile Butadiene Styrene with Silica Augmentative: Application of Friction Stir Processing

The additive manufacturing processes are increasing their importance in fabrication of composite like structures. Friction stir processing is classified as a solid state additive manufacturing technique that is used for lamination or powder deposition in a matrix substrate. In the present work an attempt was made to add the silica as secondary phase into the acrylonitrile butadiene styrene (ABS) surface for enhancing the mechanical and surface properties of fabricated composite. The parameters such as amount of silica fraction, pass number, tool rotational speed and linear speed were considered to be involved in the study. The responses surface method (RSM) was used here to assess relationship between aforementioned inputs and outputs vs bending strength and deflection before fracture. Optimal setting of process parameters was determined by maximizing bending strength and deflection. The obtained optimal results showed that maximum strength and deflection can be achieved by selecting of 100% silica volume fraction, 1 pass number, 1400 rpm tool rotation and 80 mm/min travel speed. The optimum results were further verified through confirmatory experiment and the result showed that the values of prediction error for bending strength and deflection are 6.1% and 8.5%, respectively. This proves the validity of the proposed approach for empirical modeling and optimization of friction stir additive manufacturing process.

Multi-Optimization of Process parameters for Inconel 718 While Die-Sink EDM Using Multi-Criterion Decision Making Methods

The objective of the paper is to apply Multi-criterion Decision Making methods in manufacturing specially in machining using EDM and to obtain an optimal setting of process parameters resulting in an optimal value of the material removal rate and tool wear rate while EDM for Inconel 718 using copper cadmium as electrode Taguchi L9 array has been used to design the experiments, the results are further analyzed using Multi-criterion Decision Making techniques named Technique for order preference by similarity to ideal solution TOPSIS and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) to investigate the multi-optimization of response characteristics for Inconel 718.

Optimization of Fractal Dimension of Turned AISI 1040 Steel Surface Considering Different Cutting Conditions

The present work examines the effect of turning process parameters, namely depth of cut and feed rate on the fractal dimension of AISI 1040 steel. Machined surfaces have been characterized using fractal dimensions. Apart from the aforesaid conventional turning parameters, cutting condition has been also considered as a design variable. Three cutting conditions have been considered, e.g. dry, water lubricated, and commercially available water-soluble emulsion lubricated condition. The depth of cut and feed rate has been also been varied at three levels. Experiments were performed following Taguchi's L9 orthogonal array. The optimal setting of process parameters has been achieved through the use of Taguchi's quality loss function represented by a signal-to-noise ratio. The optimal condition predicted from Taguchi's analysis is a 0.4 mm depth of cut, a 0.07 mm/rev feed rate and a water-based emulsion cutting environment. The results obtained for fractal dimensions has been also compared with the more conventional roughness parameter centre line average roughness which is dependent on instrument resolution.

Experimental Study on Material Removal Rate of Al6061 Machined with Magnetic Field Assisted Powder Mixed Electrical Discharge Machining

This paper describes the research work involved in experimental study on magnetic field-assisted powder mixed electrical discharge machining of Al6061 alloy. In this study, five process parameters namely, peak current, powder concentration, pulse on time, magnetic field and pulse off time are considered. Box-Behnken design approach based on response surface methodology was used for performing the experiments and for investigation of influence of process parameters on material removal rate (MRR). Based on analysis quadratic model was developed to predict the MRR. Optimization was performed using desirability function approach for determining the optimum set of process parameters for maximum MRR. The experimental validation was performed at optimum process parameters.

An Intelligent Optimization System for PIM Process

This study proposes an intelligent optimization system based on the Taguchi method, back-propagation neural network (BPNN), multilayer perceptron (MLP) and modified PSO-GA to find optimal process parameters in plastic injection molding (PIM). Firstly, the Taguchi method is used to determine the initial combination of parameter settings by calculating the signal-to-noise (S/N) ratios from the experimental data. Significant factors are determined using analysis of variance (ANOVA). The S/N ratio predictors (BPNNS/N) and quality predictors (BPNNQ) are constructed using BPNN with the experimental data. In addition, a modified PSO-GA algorithm in conjunction with MLP is used to find initial weights of BPNN and to reduce the training time of BPNN. In the first stage optimization, the S/N ratio predictors are coupled with GA to reduce the variations of the manufacturing process. In the second stage optimization, The combination of S/N ratio predictors and quality predictors with modified PSO-GA is empoyed to search for the optimal parameters. Finally, three confirmation experiments are performed to assess the effectiveness of these approaches. The experimental results show that the proposed system can create the best performance, and optimal process parameter settings which not only enhance the stability in the whole injection molding process but also effectively improve the PIM product quality. Furthermore, experiences of the novel hybrid optimization system can be transferred into the intelligent PIM machines for the coming up internet of things (IoT) and big data environment.

Optimization of mechanical oil extraction process from Camellia oleifera seeds regarding oil yield and energy consumption

AbstractMechanical pressing is widely used for producing high‐quality vegetable oil in industry. However, mechanical pressing process faces a challenge from meeting the industrial demand for high yield with low energy consumption. Hence, the objective of this study was to optimize mechanical oil extraction process from Camellia oleifera seeds by maximizing the yield and minimizing the energy consumption. A Box–Behnken design of response surface methodology is adopted for the experiments of mechanical oil extraction from C. oleifera seeds. The effects of experimental factors namely moisture content (0–9% w.b.), applied pressure (10–45 MPa), pressing temperature (40–100°C) and extraction time (10–40 min) on yield and energy consumption were investigated by modeling the oil extraction process. An analysis of variance (ANOVA) revealed that moisture content, applied pressure and extraction time significantly effect on yield while applied pressure, pressing temperature, and extraction time have an obvious influence on energy consumption. In addition, the oil extracted under the processing conditions investigated was validated to be of acceptable quality. The Pareto fronts based on multiobjective genetic algorithm provides a set of optimal process parameters to obtain sustainable end products.Practical ApplicationMoisture content, applied pressure, pressing time, and pressing temperature are primary processing conditions affecting oil extraction from C. oleifera seeds during the mechanical process. The use of inappropriate processing parameters may lead to low yield and high‐energy consumption. Understanding the mechanisms of oil extraction process thoroughly while taking important parameters into consideration is vital for the process to be efficient. This can be obtained by modeling oil extraction process using both experimental and theoretical methods. In this article, how these factors affect the yield and energy consumption of the system were investigated by modeling the oil extraction process. Processing conditions were optimized by multiobjective optimization technique based on NSGA‐II. The oil extracted under the processing conditions investigated was of acceptable quality. The data collected during this study can be utilized to design the equipment and to obtain sustainable end products.