Evaluation of Substrates of Al-Mg and Aluminized Steel Coated With Non-Stick Fluoropolymers after the Removal of the Coating

Many trays and pieces of Al-Mg and aluminized steel are used in the food industry. Sometimes these elements have non-stick coatings to solve problems related to the adhesion of masses and food products. With use, the coatings deteriorate and lose efficiency and must be removed to apply a new coating. The thermal cycles suffered by these alloys during the removal process of the deteriorated coating (500 _C) and the polymerization of a new coating (400 _C) can affect the durability and efficiency of the metallic substrates. The evolution of the mechanical and microstructural properties of the Al-Mg and aluminized steel substrates after two thermal cycles was studied in this work. The following parameters were analyzed: tensile strength, elongation (%), hardness, ASTM grain size, and the nature and distribution of the constituent particles. The report concluded that the removal of the coating, after each cycle, produced a decrease in the mechanical properties of the substrates. The hardness and tensile strength in Al-Mg decreases between 20–27% and in aluminized steel between 10–11%. In both cases, the process does not compromise the reuse of the substrate for the application of a new coating layer. The final blasting stage does not affect the Al-Mg alloys but may affect the aluminized steel Al-Si protective layer if special precautions are not taken

Instructional Design Methodological Proposal for the Training of Online Content Tutors

With the challenge of change that a teacher faces, the use of educational technologies requires knowledge of instructional design. The teacher plays a prominent role in this process, so it must create educational content, following the principles of an instructional design model. The problem is that not all methodologies include a practical approach where the tutor takes ownership of pedagogical and practical aspects. Therefore, this study proposes an instructional design methodology for online Tutors combining the ADDIE, ASSURE, and PACIE models. In addition, to validate the proposed methodology, an evaluation process is carried out, taking into account the appropriation of the role of tutor of online content and level of use of the LMS tools.     Keywords: instructional design, LMS, online course, E-learnin

Analysis, Validation and Optimization of the Multi-Stage Sequential Wiredrawing Process of EN AW-1370 Aluminium

For the wiredrawing of aluminium, the initial wire rod is obtained by continuous inverted casting. The raw geometry is industrially processed in a linear multi-step wiredrawing sequence to obtain a wire that is commonly used for the manufacture of electrical conductors. In the present work a complete study of the material has been made. The experimental procedure consisted in the realization of a sequence of section reduction stages in the laboratory, a sequence designed following the technological criteria recommended by the manufacturer of the drawing machine in which the industrial process will be implemented. From the specimens corresponding to each reduction step, it has been possible to know the evolution of the main mechanical properties when this pure aluminium is processed by wiredrawing. This information has led to establish the hardening law by which it is possible characterize the plastic behaviour of this pure metal when it is transformed by this specific sequential process of cold forming. The strain hardening law has been implemented in a numerical simulation software application and the experimental setup has been simulated for its validation. Finally, the classic analytical solution founded in the “slab method” has been applied for the design of a proposal for the optimization of the industrial wiredrawing process

Use of the support vector machine (SVM) algorithm to predict geometrical accuracy in the manufacture of molds via single point incremental forming (SPIF) using aluminized steel sheets

In the present work, the use of the support vector machine (SVM) algorithm is proposed to generate models that allow predicting the geometrical accuracy of molds manufactured via single point incremental forming (SPIF) using aluminized steel sheets DX51D AS120 B CO. For this purpose, 27 molds were manufactured, using the dummy technique, and employing different process parameters (tool diameter, spindle speed, feed rate, step size) and toolpath strategies (contour-parallel, spiral, radial). The molds manufactured were geometrically characterized by means of a coordinate measuring machine: the transverse profile of each mold was measured and compared with the expected theoretical profile. Three geometrical values were extracted from this comparison: the area between the two profiles, the moment of inertia of this area with respect to the Y-axis and the difference in height between the two profiles at the mid-point of the mold. The geometrical accuracy of the mold increases if these values decrease. The model that achieved the best results is the one associated with the area between the theoretical and real profiles (correctly classified instances = 90%; kappa statistic = 0.8). This model was generated using the LibSVM (linear kernel) algorithm and evaluating only three of the five parameters (strategy, tool diameter and step size). In addition, process maps were drawn up to show briefly which values generate higher geometrical accuracy in the molds: contour-parallel strategy, tool diameter equal to 12 mm and small step size values

NIRS potential use for the determination of natural resources quality from dehesa (acorn and grass) in Montanera system for Iberian pigs.

NIRS technology has been used as an alternative to conventional methods to determinate the content of nutrients of acorns and grass from dehesa ecosystem. Dry matter (DM), crude fat (CF), crude protein (CP), starch, total phenolic compounds (TP), α-tocopherol, γ-tocopherol, fatty acids, neutral detergent fiber (NDF), total antioxidant activity (TAA) and total energy (TE) were determined by conventional methods for later development of NIRS predictive equations. The NIR spectrum of each sample was collected and for all studied parameters, a predictive model was obtained and external validated. Good prediction equations were obtained for moisture, crude fat, crude protein, total energy and γ-tocopherol in acorns samples, with high coefficients of correlation (1-VR) and low standard error of prediction (SEP) (1-VR=0.81, SEP=2.62; 1-VR=0.92, SEP=0.54; 1-VR=0.86, SEP=0.47; 1-VR=0.84, SEP=0.2; 1-VR=0.88, SEP=5.4, respectively) and crude protein, NDF, α-tocopherol and linolenic acid content in grass samples (1-VR=0.9, SEP=1.99; 1-VR=0.87, SEP=4.13; 1-VR=0.76, SEP=10.9; 1-VR=0.82, SEP=0.6, respectively). Therefore, these prediction models could be used to determinate the nutritional composition of Montanera natural resources

Study on the Main Influencing Factors in the Removal Process of Non-Stick Fluoropolymer Coatings Using Nd:YAG Laser

The coatings with fluoropolymer resins rich in fluorinated ethylene propylene (FEP) and polytetrafluoroethylene (PTFE) are applied as anti-adherent coatings on aluminum–magnesium substrates for use in food containers. In many cases, due to wear, they must be stripped for the application of a new coating on the same substrate. There are several processes for this: blasting, plasma, pyrolysis, chemical processes, laser, high pressure water, and combinations of these. This work focuses on the characterization of the main factors that condition the FEP coating removal process by a continuous wave (CW) Nd:YAG laser, and on the determination of the efficiency of this type of technology used for this purpose. Stripping surface per unit of time and energy consumption per unit area has been determined among other efficiency indicators. Regarding the characterization of the coating object of study, its thickness, surface roughness, contact angle, microhardness and absorbance-reflectance responses have been determined, and the results have been compared with those obtained in the case of PTFE. In addition, to evaluate the mechanical damage caused in the substrate after coating removal by (CW) Nd:YAG laser, the tensile strength, Vickers hardness, Ra and Rz roughness, and the substrate thickness have been measured and analyzed

Inverse gas chromatography a tool to follow physicochemical modifications of pharmaceutical solids: Crystal habit and particles size surface effects

Powders are complex systems and so pharmaceutical solids are not the exception. Nowadays, pharmaceutical ingredients must comply with well-defined draconian specifications imposing narrow particle size range, control on the mean particle size, crystalline structure, crystal habits aspect and surface properties of powders, among others. The different facets, physical forms, defects and/or impurities of the solid will alter its interaction properties. A powerful way of studying surface properties is based on the adsorption of an organic or water vapor on a powder. Inverse gas chromatography (IGC) appears as a useful method to characterize the surface properties of divided solids. The aim of this work is to study the sensitivity of IGC, in Henry’s domain, in order to detect the impact of size and morphology in surface energy of two crystalline forms of an excipient, d-mannitol. Surface energy analyses using IGC have shown that the α form is the most energetically active form. To study size and shape influence on polymorphism, pure α and β mannitol samples were cryomilled (CM) and/or spray dried (SD). All forms showed an increase of the surface energy after treatment, with a higher influence for β samples (γsd of 40–62mJm−2) than for α mannitol samples (γsd of 75–86mJm−2). Surface heterogeneity analysis in Henry’s domain showed a more heterogeneous β-CM sample (62–52mJm−2). Moreover, despite its spherical shape and quite homogeneous size distribution, β-SD mannitol samples showed a slightly heterogeneous surface (57–52mJm−2) also higher than the recrystallized β pure sample (∼40mJm−2)

Use of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturing

In the present work, ten data mining algorithms have been used to generate models capable of predicting the surface roughness of parts printed on polylactic acid (PLA) by using fused deposition modeling (FDM). The models have been trained using experimental data measured on 27 horizontal (XY) and 27 vertical (XZ) specimens, printed using different values for the parameters studied (layer height, extrusion temperature, print speed, print acceleration and flow). The models generated by multilayer perceptron (MLP) and logistic model trees (LMT) have obtained the best results in a cross-validation. Although it does not obtain such optimal results, the J48 algorithm (C4.5) allows the generation of models in the form of a decision tree. These trees permit to determine which print parameters have an influence on the surface roughness. For XY specimens, the surface roughness measured in the direction parallel to the extrusion path (Ra,0,XY ) depends on the flow, the print temperature and the layer height; in the direction perpendicular to the extrusion path, the surface roughness (Ra,90,XY) depends only on the flow. For XZ specimens, the surface roughness measured in the direction parallel to the extrusion path (Ra,0,XZ) depends only on the print speed; in the direction perpendicular to the extrusion path (Ra,90,XZ), it depends on the layer height and the extrusion temperature. According to the study carried out, the most suitable set up provides values of Ra,0,XY, Ra,90,XY, Ra,0,XZ and Ra,90,XZ equal to 0.46, 1.18, 0.45 and 11.54, respectively. A practical application of this work is the manufacture of PLA frame glasses using FDM