Browse Topic: Production
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Agrícola Cana Caiana and Grunner have developed an innovative vehicle for sugarcane harvesting, focused on reducing fuel consumption. This optimization is vital and relevant for similar operations in the largest global producers: Brazil (724 mi t - 37%), India (439 mi t - 22%), China (103 mi t - 5.3%), Thailand (92 mi t - 4.7%), Pakistan (88 mi t - 4.5%), Mexico (55 mi t - 2.8%), Colombia (35 mi t - 1.8%), Indonesia (32 mi t - 1.6%), USA (31 mi t - 1.6%), and Australia (28 mi t - 1.4%). In Brazil, São Paulo leads with 383.4 mi t (54.1% of the 23/24 harvest), followed by Minas Gerais (81.3 mi t). This innovative agricultural machinery, a result of the owners' experience, has already sold over a thousand units, proving its impact on the efficiency of the sugar-alcohol sector. The Belei family's expertise generated this solution that optimizes resources and increases harvesting productivity, with the potential to advance sustainability and profitability globally, driving agricultural
Automating harvesters started out as a necessary solution to a severe labor shortage in 1990, Trebro Manufacturing states on its website. The Billings, Montana-based manufacturer has been producing turf harvesting machines since 1999, and its automated sod harvesters and entire harvesting process feature self-driving, automated-control functions. The company's tag line, “The Future of Turf Harvesting,” refers to its position of being the first in the industry to offer automated turf harvesting products. Trebro's AutoStack 3 harvester is an automated combine for turf that steers itself while an operator monitors and performs quality control actions when needed. The harvesting process combines several automated control processes.
NASA has developed a novel approach for macroscale biomaterial production by combining synthetic biology with 3D printing. Cells are biologically engineered to deposit desired materials, such as proteins or metals, derived from locally available resources. The bioengineered cells build different materials in a specified 3D pattern to produce novel microstructures with precise molecular composition, thickness, print pattern, and shape. Scaffolds and reagents can be used for further control over material product. This innovation provides modern design and fabrication techniques for custom-designed organic or organic-inorganic composite biomaterials produced from limited resources.
As countries race to expand renewable energy infrastructure, balancing clean electricity production with land use for food remains a pressing challenge — especially in Japan, where mountainous terrain limits space. A recent study led by researchers from the University of Tokyo explores a promising solution: integrating solar panels with traditional rice farming in a practice known as agrivoltaics.
In order to meet the demand for the transformation of traditional manufacturing industries into intelligent manufacturing, a virtual monitoring system for the production workshops of nuclear - key products has been built. There are problems such as poor environment, long distance and remote collaborative office in this production workshop, and managers lack information tools to master the workshop status in real time. In order to minimize the harm of nuclear radiation to the human body, in view of the problems of low transparency, poor real - time performance and low data integration in traditional two - dimensional forms, configuration software and video monitoring, a remote monitoring system for virtual workshops driven by digital models has been developed. This system realizes the remote dynamic display of real - time information in the workshop based on data collection and three - dimensional modeling technologies. Virtual monitoring technology improves the management efficiency of
Over the past 25 years, the heavy fabrication and construction equipment industry has experienced significant transformation. Driven by a global surge in demand for construction machinery, manufacturers are under increasing pressure to deliver higher volumes within shorter timelines and at competitive costs. This demand surge has been compounded by workforce-related challenges, including a declining interest among the new generation in acquiring traditional manufacturing skills such as welding, heat treatment, and painting. Furthermore, the industry faces difficulties in staffing third-shift operations, which are essential to meet production targets. The adoption of automation technologies in heavy fabrication and construction equipment manufacturing has been gradual and often hindered by legacy product designs that were optimized for conventional manufacturing methods. As the industry transitions toward smart, connected manufacturing environments under the industry 4.0 paradigm, it
Large farms cultivating forage crops for the dairy and livestock sectors require high-quality, dense bales with substantial nutritional value. The storage of hay becomes essential during the colder winter months when grass growth and field conditions are unsuitable for animal grazing. Bale weight serves as a critical parameter for assessing field yields, managing inventory, and facilitating fair trade within the industry. The agricultural sector increasingly demands innovative solutions to enhance efficiency and productivity while minimizing the overhead costs associated with advanced systems. Recent weighing system solutions rely heavily on load cells mounted inside baling machines, adding extra costs, complexity and weight to the equipment. This paper addresses the need to mitigate these issues by implementing an advanced model-based weighing system that operates without the use of load cells, specifically designed for round baler machines. The weighing solution utilizes mathematical
The development of 3D game ready models is a critical component of the asset creation workflow in industries. However, traditional modeling techniques often demand extensive manual input, particularly in the areas of modeling, retopology, and texturing. To address challenges, we propose the integration of generative AI technologies into the 3D modeling workflow, aiming to enhance efficiency and streamline processes. This paper presents a comprehensive methodology that leverages advanced algorithms, machine learning techniques, and specialized software to automate repetitive tasks associated with 3D asset creation. By harnessing the power of generative AI, we aim to significantly reduce the manual effort required to produce high-quality 3D models, thereby accelerating the overall development timeline. The aim is to enter a prompt/Image as input to get a fully developed Model. Through a series of experimental implementations, we are aiming to demonstrate the effectiveness of our proposed
Measuring the volume of harvested material behind the machine can be beneficial for various agricultural operations, such as baling, dropping, material decomposition, cultivation, and seeding. This paper aims to investigate and determine the volume of material for use in various agricultural operations. This proposed methodology can help to predict the amount of residue available in the field, assess field readiness for the next production cycle, measure residue distribution, determine hay readiness for baling, and evaluate the quantity of hay present in the field, among other applications which would benefit the customer. Efficient post-harvest residue management is essential for sustainable agriculture. This paper presents an Automated Offboard System that leverages Remote Sensing, IoT, Image Processing, and Machine Learning/Deep Learning (ML/DL) to measure the volume of harvested material in real-time. The system integrates onboard cameras and satellite imagery to analyze the field
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