Seletividade de produtos utilizados no cultivo orgânico a Trichogramma pretiosum Riley (Hymenoptera: trichogramatidae).

O impacto de insumos para o controle de pragas no cultivo orgânico sobre inimigos naturais é ainda pouco conhecido. Assim sendo, avaliou-se, em condições de laboratório, a seletividade de produtos utilizados no cultivo da soja orgânica sobre pupas e adultos de Trichogramma pretiosum seguindo os protocolos padronizados da ?International Organization for Biological Control? (IOBC) para estudos de seletividade. O delineamento experimental utilizado foi inteiramente casualizado com 10 tratamentos e cinco repetições. Os tratamentos foram: testemunha negativa (água), testemunha positiva Lorsban® (0,8L/ha), Baculovirus anticarsia AEE® (140x109 cpi/ha), Dipel® (0,5L/ha), Neemseto® (4L/ha), Arrast® (4L/ha), Fish-Fértil® (3L/ha), Borda-Ferti® (1,8L/ha), Silicato de Na (4L/ha) e Calda sulfocálcica composta por enxofre e cal (1,8L/ha). Verificou-se que quando aplicados sobre pupas de T. pretiosum, os produtos testados são seletivos e não afetam a emergência dos parasitoides, exceto o inseticida Lorsban® que foi nocivo a todas as fases testadas. Porém, a partir do quinto dia de avaliação, os tratamentos Dipel® e Silicato de Na, reduziram o parasitismo de fêmeas emergidas e foram classificados como levemente nocivos (classe 2). Fish-Fértil®, em comparação a testemunha negativa, mostrou-se moderadamente nocivo (classe 3) ao parasitoide. No bioensaio com adultos de T. pretiosum, os produtos foram inócuos (classe 1) no primeiro dia de avaliação, entretanto, a partir do segundo dia, todos os produtos testados apresentaram efeitos negativos na porcentagem de parasitismo, sendo classificados como levemente nocivos (classe 2) (Baculovirus anticarsia AEE®, Fish-Fértil®, Borda-Ferti®, Calda sulfocálcica) e moderadamente nocivos (classe 3) (Dipel®, Neemseto®, Arrast®, Silicato de Na). Como os efeitos dos produtos sobre o parasitoide nesse trabalho levaram a classificação dos produtos como, em geral, classe 1 ou 2, a compatibilidade do uso desses produtos voltados para cultivos orgânicos e de T. pretiosum no manejo de pragas surge como uma estratégia viável no contexto de agricultura sustentável.SICONBIOL 2011

Flexible robotic cell for in-process inspection of multi-pass welds

Welds are currently only inspected after all the passes are complete, and after allowing sufficient time for any hydrogen cracking to develop, typically over several days. Any defects introduced between passes are therefore unreported until fully buried, greatly complicating re-work and also delaying early corrections to the weld process parameters. In-process inspection can provide early intervention but involves many challenges, including operation at high temperatures with significant gradients affecting acoustic velocities and hence beam directions. Reflections from the incomplete parts of the weld would also be flagged as lack of fusion defects, requiring the region of interest to adapt as the weld is built up. The collaborative SIMPLE (Single Manufacturing Platform Environment) project addresses these challenges by incorporating robotic inspection within a robotic TIG welding cell. This has been accomplished, initially with commercial off-the-shelf ultrasonic phased arrays, but flexibly enough to adapt to future developments with higher temperature solutions. The welding and inspection robots operate autonomously. The former can introduce deliberate defects to validate the the latter which uses 5MHz 64 element phased arrays on high temperature wedges to generate sector scans after each weld pass. Results are presented, confirming the challenges have been addressed and demonstrating the feasibility of this approach

Development of a phased array ultrasonic system for residual stress measurement in welding and additive manufacturing

Residual Stress (RS) in engineering components can lead to unexpected and dangerous structural failures, and thus represent a significant challenge to quality assurance in both welding and metal additive manufacturing (AM) processes. The RS measurement using the ultrasonic method is based on the acoustoelasticity law, which states that the Time-of-Flight (ToF) of an ultrasonic wave is affected by the stress field. Longitudinal Critically Refracted (LCR) waves have the highest sensitivity to the stress in comparison with the other type of ultrasonic waves. However, they are also sensitive to the material texture which negatively affects the accuracy of the RS measurement. In this paper, a Phased Array Ultrasonic Testing (PAUT) system, rather than the single element transducers which are traditionally used in the LCR stress measurement technique, is innovatively used to enhance the accuracy of RS measurement. An experimental setup is developed that uses the PAUT to measure the ToFs in the weld, where the maximum amount of tensile RS is expected, and in the parent material, stress-free part. The ToF variations are then interpreted and analyzed to qualify the RS in the weld. The same measurement process is repeated for the Wire Arc Additive Manufacture (WAAM) components. Based on the results, some variations between different acoustic paths are measured which prove that the effect of the residual stress on the ultrasonic wave is detectable using the PAUT system

In-process non-destructive evaluation of wire + arc additive manufacture components using ultrasound high-temperature dry-coupled roller-probe

In 2019, the global metal Additive Manufacturing (AM) market size was valued at € 2.02 billion and was predicted to grow by up to 27.9% annually until 2024. Additive Manufacturing plays a significant role in Industry 4.0, where the demand for smart factories capable of fabricating high-quality customized products cost-efficiently exists. Wire + Arc Additive Manufacturing (WAAM) is one such technique that WAAM utilizes industrial robotics and arc-based welding processes to produce components on a layer-by-layer basis. is enables automated, time and material-efficient production of high-value and geometrically complex metal parts. To strengthen the benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace manually deployed inspection techniques deployed after the full part completion. The research presents a new synchronized multi-robot WAAM deposition & ultrasound NDE cell aiming to achieve defect detection in-process, enable possible in-process repair, and prevent costly scrappage or rework. Within the cell, the plasma-arc WAAM process, controlled by deposition software, is employed to build components. The full external control NDE approach is achieved by the real-time force/torque sensor-enabled adaptive kinematics control package. A high-temperature dry-coupled ultrasound roller-probe device is employed to assess the structural integrity of freshly deposited layers of WAAM components. The WAAM roller-probe is tailored to facilitate the in-process inspection by dry-coupling coupling with the hot (< 350 °C) non-flat surface of WAAM using a flexible outer silicone tyre and solid core delay-line at speed and at coupling high force[1-3]. The demonstration of the in-process inspection approach is performed on hot as-built titanium (Ti-6Al-4V) WAAM samples. The defect detection capabilities are assessed on artificial tungsten reflectors embedded in WAAM builds. In this work the defect detection is accomplished and analyzed using two separate approaches 1) layer-specific beamforming focusing imaging and 2) volumetric inspection using post-processing algorithms applied on collected Full Matric Capture data. The ultrasound in-process inspection using the dry-coupled roller-probe is driven by live Ultrasound Testing (UT) data acquisition, initiated within a minute from layer deposition completion. The collected UT B-scan frames are based on electronically focused beamforming through the roller-probe media into the depth of targeted layers. Subsequently, the results are presented on a plotted C-scan image, showing a top view over the interior of the targeted built volume. The results in this work are analyzed and compared to the X-ray computed tomography scan, conducted after the full-built completion and sample processing. The processed UT images show positionally accurate detection of embedded tungsten reflectors, with a minimum of 15 dB of signal-to-noise ratio. An accurate size estimation is also achieved for the tungsten defect extended along the sample’s length. The outcome of this research shows a successful defect detection and hence directly supports the industrial benefits of the WAAM process intending to achieve the automated production of first-time-right parts

Transforming industrial manipulators via kinesthetic guidance for automated inspection of complex geometries

The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors, requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilising traditional programming approaches demand specialised robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system, to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz utilizing an external off-the-shelf Force Torque (FT) sensor. The proposed work is demonstrated on a 500 mm2 near net shaped Wire + Arc Additive Manufacturing (WAAM) complex component with embedded defects, by teaching the inspection path for defect detection with a standard industrial robotic manipulator in a collaborative fashion and adaptively generating the kinematics resulting for uniform coupling of ultrasound inspection. The utilized method proved superior performance and speed, accelerating the programming time over online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources and achieving flexible production

The fallow period plays an important role in annual CH4 emission in a rice paddy in southern Brazil.

Paddy fields are significant anthropogenic sources of methane (CH4) emissions. In southern Brazil, rice is grown in lowland flooded areas once a year, followed by a long fallow period. This study aimed to measure CH4 fluxes in a rice paddy field in southern Brazil during the rice-growing season of 2015/2016 and the following fallow period. The fluxes were estimated using the eddy covariance (EC) technique and soil chamber (SC). Diurnal and seasonal variations of CH4 fluxes and potential meteorological drivers were analyzed. The CH4 fluxes showed distinct diurnal variations in each analyzed subperiod (vegetative, reproductive, pre-harvest, no rice, and land preparation), characterized by a single-peak diurnal pattern. The variables that most influenced methane emissions were air and surface temperatures. In the growing season, the rice vegetative stage was responsible for most of the measured emissions. The accumulated annual emission estimated was 44.88 g CH4 m?2 y?1, being 64% (28.50 g CH4 m?2) due to the rice-growing season and 36% (16.38 g CH4 m?2) due to the fallow period. These results show the importance of including fallow periods in strategies to mitigate methane emissions in flood irrigated rice-growing areas

Collaborative robotic Wire + Arc Additive Manufacture and sensor-enabled in-process ultrasonic Non-Destructive Evaluation

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM & NDE cell aiming to achieve 1) defect detection in-process, 2) enable possible in-process repair and 3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of the robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, is performed on a titanium WAAM straight sample containing intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data is acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient Signal to Noise Ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair

Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturing

The scale of the global market size for metal Additive Manufacturing (AM) in recent years, at €2.02 billion in 2019, and predictions for the continuous growth up to 27.9% annually until 2024 highlight the key role that these processes will play in the future of high-value manufacturing. AM technology leverages the concepts of the latest industrial revolution, Industry 4.0, where the manufacturing is made more flexible and smarter capable of fabricating cost-effective high-quality customized products. Among the various AM technologies, only a few such as the Wire + Arc Additive Manufacturing (WAAM) process can meet some industries’ demands for producing large components in a short time. The process typically involves industrial robots and arc-based welding performing layer-by-layer deposition on substrates and building up components to their final desired shape. The process is majorly used to manufacture low volume, high mix, critical components for applications in aerospace and nuclear industries. Therefore, the imposed inspection requirements demand very high detection sensitivities. Robotically-deployed Non-Destructive Evaluation (NDE) during the manufacturing process might just be the inspection process needed to ensure the component’s integrity as it is being built, paving the way for an easier part certification process which is normally of concern to many end-users of the technology. In-process automated inspection of WAAM, deployed after deposition of every few layers, adds to the process cost-effectiveness as the early defect detection capability provides the opportunity for the process intervention and taking remedial rework actions reducing the time/material waste. This work presents a demonstration of the concept of in-process NDE of WAAM using two different modalities: a) a high temperature phased array Ultrasound Testing (UT) roller-probe, and b) a high-temperature flexible Eddy Currents (EC) testing array. The automation cell is composed of two robots, where one is dedicated to the WAAM deposition process and the other to NDE sensor delivery on the WAAM. A titanium WAAM component with a straight geometry was deposited using the plasma-arc process and oscillation strategy, where the deposition path and process parameters were controlled by software. Intentionally-embedded tungsten tube and ball reflectors of varying sizes/orientations were inserted in between different WAAM layers to assess the in-process detectability of each of the employed NDE modalities. Full external control of the sensor-enabled adaptive motion control for the NDE robot and the integrated UT and EC array controllers and array probes were achieved through a central program developed in the LabVIEW platform. Moreover, real-time robot motion corrections, driven by the Force-Torque sensor feedback, were established to adjust the contact force and orientation of the sensors to the component surface during the scan. The high-temperature (up to 350 °C), dry-coupled UT roller-probe inspection of WAAM was conducted in-process at the dwelling time between the layers while the surface was at a maximum temp of 130C. Subsequently, EC scan was also carried out in the dwell time at high temperature. The C-scans were produced live from both UT and EC arrays demonstrating the successful detection of embedded tungsten defects with high SNRs. The WAAM component was X-ray CT scanned after production to confirm the exact location if the defects and compare it against the other NDE findings

Automated multi-modal in-process non-destructive evaluation of wire + arc additive manufacturing

High deposition rates for manufacturing of large components through Wire + Arc Additive Manufacturing (WAAM) has given the technology a distinct edge over other AM techniques. Given their target markets in defense, aerospace and nuclear industries, high Non-Destructive Evaluation (NDE) reliability is critical for the components. Therefore, in this work, two NDE modalities were robotically deployed during the manufacturing process at high temperatures to ensure the component’s integrity as it is being built. This approach for In-process automated inspection of WAAM, deployed after deposition of every few layers, provides the opportunity for the process intervention as the defects can be detected early in the process reducing the time/cost associated to part scrappage. This work presents a proof of the concept of in-process NDE of WAAM using two different sensor modalities: a) a high temperature phased array Ultrasound Testing (UT) roller-probe, and b) a high-temperature flexible Eddy Currents (EC) testing array. The automation cell is composed of two robots dedicated to the WAAM deposition process and the NDE sensor delivery on the WAAM. A titanium WAAM component with a straight geometry was deposited using the plasma-arc process and tungsten tube and ball reflectors of varying sizes/orientations were intentionally embedded in between different WAAM layers to assess the performance of each of the NDE modalities in-process. Full external control of the sensor-enabled adaptive motion control for the NDE robot and the integrated UT and EC array controllers and probes were achieved through a central program developed in the LabVIEW platform. Moreover, real-time robot motion corrections, driven by the Force-Torque sensor feedback, were established to adjust the contact force and orientation of the sensors to the component surface during the scan. The C-scans were produced live from both UT and EC arrays demonstrating the successful detection of embedded tungsten defects with high SNRs