The present study is aimed at investigating microhardness and density of aluminium alloy chips when subjected to various settings of preheating temperature and preheating time. Three values of preheating temperature were taken as 450 °C, 500 °C, and 550 °C. On the other hand, three values of preheating time were chosen (1, 2, 3) hours. The influences of the process parameters (preheating temperature and time) were analyzed using Design of Experiments (DOE) approach whereby full factorial design with center point analysis was adopted. The total runs were 11 and they comprise of two factors of full factorial design with 3 center points. The responses were microhardness and density. The results showed that the density and microhardness increased with decreasing the preheating temperature. The results also found that the preheating temperature is more important to be controlled rather than the preheating time in microhardness analysis while both the preheating temperature and preheating time are important in density analysis. It can be concluded that setting temperature at 450 °C for 1 hour resulted in the optimum responses.

This study optimized the performance of plastic extrusion process of drip irrigation pipes using fuzzy goal programming. Two main responses were of main interest; roll thickness and hardness. Four main process factors were studied. The L18 array was then used for experimental design. The individual-moving range control charts were used to assess the stability of the process, while the process capability index was used to assess process performance. Confirmation experiments were conducted at the obtained combination of optimal factor setting by fuzzy goal programming. The results revealed that process capability was improved significantly from -1.129 to 0.8148 for roll thickness and from 0.0965 to 0.714 and hardness. Such improvement results in considerable savings in production and quality costs.

In the present study an attempt has been made to re-engineer traditional wadi into wholesome ready-to-use cereal-pulse-based chunks rich in protein quality and fibre content. Chunks were made using extrusion-dehydration combination. Two formulations i.e., whole green gram dhal with instant oats and washed green gram dhal with whole oats were formulated. These chunks are versatile in nature as they can be easily incorporated in day-to-day home-made preparations such as pulao, potato curry and kadhi. Cereal-pulse ratio was calculated using NDpCal%. Limiting amino acids such as lysine, tryptophan, methionine, cysteine and threonine were calculated for maximum amino acid profile in cereal-pulse combination. Time-temperature combination for extrusion at 130oC and dehydration at 65oC for 7 hours and 15 minutes were standardized to obtain maximum protein and fibre content. Proximate analysis such as moisture, fat and ash content were analyzed. Protein content of formulation was 62.10% and 68.50% respectively. Fibre content of formulations was 2.99% and 2.45%, respectively. Using a 5-point hedonic scale, consumer preference trials of 102 consumers were conducted and analyzed. Evaluation of chunks prepared in potato curry, kadi and pulao showed preferences for colour 82%, 87%, 86%, texture and consistency 80%, 81%, 88%, flavour and aroma 74%, 82%, 86%, after taste 70%, 75%, 86% and overall acceptability 77%, 75%, 88% respectively. High temperature inactivates antinutritional compounds such as trypsin inhibitors, lectins, saponins etc. Hence, availability of protein content was increased. Developed products were palatable and easy to prepare.

Even though legumes possess high nutritional value and have a rather high protein content for plant origin products, they are underutilized mostly due to their lengthy cooking time. To increase the presence of legume-based products in human diet, new extruded products were made of cow peas (Vigna unguiculata (L.) Walp.). But as it is known, adding different moisture content to flour before extrusion can change the physical properties of the extruded product. Experiments were carried out to estimate the optimal moisture content for cow pea extrusion. After extrusion, the pH level had dropped from 6.7 to 6.5 and the lowest hardness rate was observed in the samples with additional 9 g 100g-1 of moisture - 28±4N, but the volume mass of the samples with additional 9 g100g-1 of water was 263±3 g L-1; all samples were approximately 7±1mm long.

The experiments have been conducted to study the mechanical properties of commercially pure copper processing at room temperature by severe plastic deformation using equal channel angular extrusion (ECAE) through a die of 90oangle up to 3 passes by route BC i.e. rotating the sample in the same direction by 90o after each pass. ECAE is used to produce from existing coarse grains to ultra-fine, equiaxed grains structure with high angle grain boundaries in submicron level by introducing a large amount of shear strain in the presence of hydrostatic pressure into the material without changing billet shape or dimension. Mechanical testing plays an important role in evaluating fundamental properties of engineering materials as well as in developing new materials and in controlling the quality of materials for use in design and construction. Yield stress, ultimate tensile stress and ductility are structure sensitive properties and vary with the structure of the material. Microhardness and tensile tests were carried out to evaluate the hardness, strength and ductility of the ECAE processed materials. The results reveal that the strength and hardness of commercially pure copper samples improved significantly without losing much ductility after each pass.

Plastic extrusion has been an important process of plastic production since 19th century. Meanwhile, in plastic extrusion process, wide variation in temperature along the extrudate usually leads to scraps formation on the side of finished products. To avoid this situation, there is a need to deeply understand temperature distribution along the extrudate in plastic extrusion process. This work developed an analytical model that predicts the temperature distribution over the billet (the polymers melt) along the extrudate during extrusion process with the limitation that the polymer in question does not cover biopolymer such as DNA. The model was solved and simulated. Results for two different plastic materials (polyvinylchloride and polycarbonate) using self-developed MATLAB code and a commercially developed software (ANSYS) were generated and ultimately compared. It was observed that there is a thermodynamic heat transfer from the entry level of the billet into the die down to the end of it. The graph plots indicate a natural exponential decay of temperature with time and along the die length, with the temperature being 413 K and 474 K for polyvinylchloride and polycarbonate respectively at the entry level and 299.3 K and 328.8 K at the exit when the temperature of the surrounding was 298 K. The extrusion model was validated by comparison of MATLAB code simulation with a commercially available ANSYS simulation and the results favourably agree. This work concludes that the developed mathematical model and the self-generated MATLAB code are reliable tools in predicting temperature distribution along the extrudate in plastic extrusion process.

Mustard leaves are rich in folates, vitamin A, K and B-complex. Mustard greens are low in calories and fats and rich in dietary fiber. They are rich in potassium, manganese, iron, copper, calcium, magnesium and low in sodium. It is very rich in antioxidants and Phytonutrients. For the optimization of process variables (moisture content and mustard leave powder), the experiments were conducted according to central composite Face Centered Composite design of RSM. The mustard leaves powder was replaced with composite flour (a combination of rice, chickpea and corn in the ratio of 70:15:15). The extrudate was extruded in a twin screw extruder at a barrel temperature of 120°C. The independent variables were mustard leaves powder (2-10 %) and moisture content (12-20 %). Responses analyzed were bulk density, water solubility index, water absorption index, lateral expansion, antioxidant activity, total phenolic content, and overall acceptability. The optimum conditions obtained were 7.19 g mustard leaves powder in 100g premix having 16.8% moisture content (w.b).

The composite flour blend consisting of corn, pearl millet, black gram and wheat bran in the ratio of 80:5:10:5 was taken to prepare the extruded product and their effect on physical properties of extrudate was studied. The extrusion process was conducted in laboratory by using twin screw extruder. The physical characteristics evaluated include lateral expansion, bulk density, water absorption index, water solubility index, and rehydration ratio and moisture retention. The Central Composite Rotatable Design (CCRD) was used to decide the level of processing variables i.e. feed moisture content (%), screw speed (rpm), and barrel temperature (oC) for the experiment. The data obtained after extrusion process were analyzed by using response surface methodology. A second order polynomial model for the dependent variables was established to fit the experimental data. The numerical optimization studies resulted in 127°C of barrel temperature, 246 rpm of screw speed, and 14.5% of feed moisture as optimum variables to produce acceptable extruded product. The responses predicted by the software for the optimum process condition resulted in lateral expansion 126%, bulk density 0.28 g/cm3, water absorption index 4.10 g/g, water solubility index 39.90%, rehydration ratio 544% and moisture retention 11.90% with 75% desirability.

Aluminum/Copper clad sheet has been fabricated using asymmetric extrusion method, which caused severe shear deformation between Al and Cu plate to easily bond to each other. Interfacial microstructure and mechanical properties of Al/Cu clad were studied by scanning electron microscope equipped with energy dispersive X-ray detector, micro-hardness, and tension tests. The asymmetric extrusion bonding was very effective to provide a good interface for atoms diffusion during subsequent annealing. The strength of bonding was higher with the increasing extrusion ratio.

This current research focused on development of degradable starch based packaging film with enhanced mechanical properties. A series of low density polyethylene (LDPE)/tapioca starch compounds with various tapioca starch contents were prepared by twin screw extrusion with the addition of maleic anhydride grafted polyethylene as compatibilizer. Palm cooking oil was used as processing aid to ease the blown film process, thus, degradable film can be processed via conventional blown film machine. Studies on their characteristics, mechanical properties and biodegradation were carried out by Fourier Transform Infrared (FTIR) spectroscopy and optical properties, tensile test and exposure to fungi environment respectively. The presence of high starch contents had an adverse effect on the tensile properties of LDPE/tapioca starch blends. However, the addition of compatibilizer to the blends improved the interfacial adhesion between the two materials, hence, improved the tensile properties of the films. High content of starch amount also was found to increase the rate of biodegradability of LDPE/tapioca starch films. It can be proved by exposure of the film to fungi environment. A growth of microbes colony can be seen on the surface of LDPE/tapioca starch film indicates that the granular starch present on the surface of the polymer film is attacked by microorganisms, until most of it is assimilated as a carbon source.

The influence of extrusion parameters on surface quality and properties of AA6061+x% vol. SiC (x = 0; 2,5; 5; 7,5;10) composites was discussed in this paper. The averages size of AA6061 and SiC particles were 10.6 μm and 0.42 μm, respectively. Two series of composites (I - compacts were preheated at extrusion temperature through 0.5 h and cooled by water directly after process; II - compacts were preheated through 3 hours and were not cooled) were consolidated via powder metallurgy processing and extruded by KoBo method. High values of density for both series of composites were achieved. Better surface quality was observed for II series of composites. Moreover, for these composites lower (compared to I series) but more uniform strength properties over the cross-section of the bar were noticed. Microstructure and Young-s modulus investigations were made.

The plastic flow of metal in the extrusion process is an important factor in controlling the mechanical properties of the extruded products. It is, however, difficult to predict the metal flow in three dimensional extrusions of sections due to the involvement of re-entrant corners. The present study is to find an upper bound solution for the extrusion of triangular sectioned through taper dies from round sectioned billet. A discontinuous kinematically admissible velocity field (KAVF) is proposed. From the proposed KAVF, the upper bound solution on non-dimensional extrusion pressure is determined with respect to the chosen process parameters. The theoretical results are compared with experimental results to check the validity of the proposed velocity field. An extrusion setup is designed and fabricated for the said purpose, and all extrusions are carried out using circular billets. Experiments are carried out with commercially available lead at room temperature.

In the process of polyethylene extrusion polymer material similar to powder or granule is under compression, melting and transmission operation and on base of special form, extrudate has been produced. Twin-screw extruders are applicable in industries because of their high capacity. The powder mixing with chemical additives and melting with thermal and mechanical energy in three zones (feed, compression and metering zone) and because of gear pump and screw's pressure, converting to final product in latest plate. Extruders with twin-screw and short distance between screws are better than other types because of their high capacity and good thermal and mechanical stress. In this paper, process of polyethylene extrusion and various tapes of extruders are studied. It is necessary to have an exact control on process to producing high quality products with safe operation and optimum energy consumption. The granule size is depending on granulator motor speed. Results show at constant feed rate a decrease in granule size was found whit Increase in motor speed. Relationships between HDPE feed rate and speed of granulator motor, main motor and gear pump are calculated following as: x = HDPE feed flow rate, yM = Main motor speed yM = (-3.6076e-3) x^4+ (0.24597) x^3+ (-5.49003) x^2+ (64.22092) x+61.66786 (1) x = HDPE feed flow rate, yG = Gear pump speed yG = (-2.4996e-3) x^4+ (0.18018) x^3+ (-4.22794) x^2+ (48.45536) x+18.78880 (2) x = HDPE feed flow rate, y = Granulator motor speed 10th Degree Polynomial Fit: y = a+bx+cx^2+dx^3... (3) a = 1.2751, b = 282.4655, c = -165.2098, d = 48.3106, e = -8.18715, f = 0.84997 g = -0.056094, h = 0.002358, i = -6.11816e-5 j = 8.919726e-7, k = -5.59050e-9

The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.