Thermo-mechanical properties of polyethylene composites filled with soapstone waste

In this study, soapstone waste originated from craftsmanship activities was used as an alternative filler (0–30 wt%) for a high-density polyethylene (PE) matrix. The aim of this paper is to understand the effect of the filler particles on crystallinity, thermal stability and thermo-mechanical properties of this newly developed composite material. Physico-chemical characterization was performed by x-ray diffraction (XRD) and Fourier-transform infrared (FTIR) spectroscopy. Thermogravimetric analysis (TGA), oxidation induction time (OIT) and dynamic mechanical thermal analysis (DMA) were performed to assess the effect of the filler on the themo-mechanical properties of PE. Thermal stability, measured by TGA, was enhanced, while OIT values reduced with filler content. A significant increase on the storage modulus of the composites (up to 148% in comparison with unfilled PE) was observed and this reinforcing effect was even more prominent at higher temperatures. XRD analysis revealed that the degree of crystallinity improved significantly with soapstone loading, which explains the substantial increase in stiffness observed. Increased crystallinity is also associated with higher strength, reduced residual stress, and better dimensional stability of end products, which can be particularly attractive for pressure pipe applications.     

A multivariate surface roughness modeling and optimization under conditions of uncertainty

Correlated responses can be written in terms of principal component scores, but the uncertainty in the original responses will be transferred and will influence the behavior of the regression function. This paper presents a model building strategy that consider the multivariate uncertainty as weighting matrix for the principal components. The main objective is to increase the value of R² predicted to improve model’s explanation and optimization results. A case study of AISI 52100 hardened steel turning with Wiper tools was performed in a Central Composite Design with three-factors (cutting speed, feed rate and depth of cut) for a set of five correlated metrics (R_a, R_y, R_z, R_q and R_t). Results indicate that different modeling methods conduct approximately to the same predicted responses, nevertheless the response surface to Weighted Principal Component – case b – (WPC1^b) presented the highest predictability.     

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes.     

Natural language manual programming for pulsed fiber laser micromachining

This paper proposes the use of voice commands expressed in natural language for part program generation for pulsed fiber laser micromachining. Traditionally, according to ISO G-code, a part program is an ordered list of program blocks in an ASCII formatted computer file. Each block identifies a machine operation and defines its parameters, following a predefined syntax. Under the proposed approach, such a file is replaced by natural language voice commands without any particular syntax constraints. Such an approach has the potential to facilitate the generation of part programs in domains (laser micromanufacturing, rapid prototyping, desktop computer numerical control) where traditional ISO G-code programming seems inappropriate, and the adoption and implementation of complex solutions such as STEP-NC or CAD/CAM systems could be unjustified. The input system is responsible for the recognition and interpretation of voice commands according to a statistical knowledge base learnt from examples. The proposed approach has been implemented in a prototype computer system, named smart programming system that has been validated by an application to the programming of pulsed fiber laser micromachining.     

Initial appraisal of water quality of Lake Santa Ana,Mexico

Lake Santa Ana is located in the Mexican central plateau, a semiarid region characterized by low rainfall and high evaporation rates. Because of the area's climatic conditions, most of its few existing lakes and reservoirs are ephemeral, with significantly reduced surface areas during the dry season. Mexican authorities currently pay little or no attention to these special aquatic ecosystems. As a consequence, several of these waterbodies are severely degraded in regard to both water quantity and quality. As a result, this report describes Lake Santa Ana as a case study. Several water quality parameters, including physicochemical, microbiological and heavy metal concentrations, were measured at eight sampling sites in the lake, and four sites in the lake basin, in 2005. Some dissolved ions were also analysed in 2007 in the lake, and in a well located close to the lake, in order to evaluate possible interactions between groundwater and surface waters. These study results indicate that the lake does not have significant interactions with groundwater in the local aquifer. Most of the measured water quality parameters do not meet the international suggested standards for inland surface waters to sustain aquatic life, and preserve the habitat for both endemic and migratory waterfowl. Loss of aquatic habitats puts at risk the value of the central portion of Mexico as a migratory route for North American waterfowl species.     

Simulation-based optimization for discharge/loading operations at a maritime container terminal

The discharge/loading process of a single container ship by multiple quay cranes and shuttle vehicles moving back and forth from the quay to the yard and vice versa is focused in this paper. The core problem of this major operational issue reduces to finding the optimal assignment and optimal sequencing (schedule) of bays (jobs) processed by a fixed number of available cranes (machines). Under the classical assumption that machines have no release time and that their processing occurs with continuity, at a constant rate, in literature it has been tackled as a deterministic machine scheduling problem and formulated by integer programming as the quay crane scheduling problem (QCSP). Here, instead, the QCSP is viewed as a decisional step within an uncertain and dynamic logistic process where the quay cranes are the resources to be managed at the best, i.e., by minimizing the time spent waiting for each other due to conflicts, as well as the time wasted for blocking and starvation phenomena due to congestion occurring along the path from the quay area and to the stacking yard and vice versa. We present a simulation-based optimization (SO) model for this wider modeling problem with the objective of finding the schedule which optimizes a classical objective function. The search process for the optimal schedule is accomplished by a simulated annealing (SA) algorithm, while performance estimation of the overall container discharge/loading process is provided by the simulation framework as a whole. Numerical experiments on a real instance are presented for tuning purposes of the SA procedure implemented within the simulator.     

The growth of carbon nanotubes on large areas of silicon substrate using commercial iron oxide nanoparticles as a catalyst

A new approach to chemical vapour deposition (CVD) growth of carbon nanotubes (CNTs) using commercial magnetite nanoparticles, avoiding its in situ synthesis, is reported. Commercial magnetite nanoparticles were used as catalyst material to growth multiwalled carbon nanotubes by chemical vapour deposition onto a silicon substrate of several square centimeters in area. It is shown that the application of an alternating electric field during the deposition of catalytical nanoparticles is an effective technique to avoid their agglomeration allowing nanotube growth. Scanning electron microscopy showed that the nanotubes grow perpendicularly to the substrate and formed an aligned nanotubes array. The array density can be controlled by modifying the deposited nanoparticle concentration.     

Interlaminar and intralaminar reinforcement of composite laminates with aligned carbon nanotubes

Three-dimensional reinforcement of woven advanced polymer–matrix composites using aligned carbon nanotubes (CNTs) is explored experimentally and theoretically. Radially-aligned CNTs grown in situ on the surface of fibers in a woven cloth provide significant three-dimensional reinforcement, as measured by Mode I interlaminar fracture testing and tension-bearing experiments. Aligned CNTs bridge the ply interfaces giving enhancement in both initiation and steady-state toughness, improving the already tough system by 76% in steady state (more than 1.5 kJ/m² increase). CNT pull-out on the crack faces is the observed toughening mechanism, and an analytical model is correlated to the experimental fracture data. In the plane of the laminate, aligned CNTs enhance the tension-bearing response with increases of: 19% in bearing stiffness, 9% in critical strength, and 5% in ultimate strength accompanied by a clear change in failure mode from shear-out failure (matrix dominated) without CNTs to tensile fracture (fiber dominated) with CNTs.     

Novel bacterial cellulose–acrylic resin nanocomposites

The preparation and characterization of new nanocomposite films based on two acrylic emulsions, composed of random copolymers of butyl acrylate and methyl methacrylate, and bacterial cellulose is reported. The new composite materials were obtained through a simple and green approach by casting water-based suspensions of the acrylic emulsions and bacterial cellulose nanofibrils. The excellent compatibility between these matrices and the natural reinforcing fibers, observed by scanning electron microscopy (SEM), was reflected in the enhanced thermal and mechanical properties of the ensuing composites. Thus, an increase of around 30 °C in the maximum degradation temperature was observed for a 10% content of bacterial cellulose. The new composites showed glass–rubber transition temperature profiles comparable to those of the pristine matrices, as shown by DMA, and increasing elastic moduli with increasing the bacterial cellulose content. The tensile tests revealed a substantial increase in Young’s modulus and tensile strength and a corresponding decrease in elongation at break with increasing bacterial cellulose load.