Digital twin control of multi-axis wood CNC machining center based on LinuxCNC

Abstrack: This paper presents an application of an open architecture control system implemented on a multi-axis wood computer numerical control milling machining center, as a digital twin control. The development of the digital twin control system was motivated by research and educational requirements, especially in the field of configuring a new control system by “virtual commissioning”, enabling the validation of the developed controls, program verification, and analysis of the machining process and monitoring. The considered wood computer numerical control (CNC) machining system is supported by an equivalent virtual machine in a computer-aided design and computer-aided manufacturing (CAD/CAM) environment, as well as in the control system, as a digital twin. The configured virtual machines are used for the verification of the machining program and programming system via machining simulation, which is extremely important in multi-axis machining. Several test wood workpieces were machined to validate the effectiveness of the developed control system based on LinuxCNC

USING PROC NLMIXED TO ANALYZE A TIME OF WEED REMOVAL STUDY

Many studies in weed science involve fitting a nonlinear model to experimental data. Examples of such studies include dose-response experiments and studies to determine the critical period of weed control. The experiments typically use block designs and often have additional complexity such as split-plot features. However, nonlinear models are typically fit using software such as SAS PROC NLIN that are limited to a single error term and whose ability to account for blocking is either awkward or lacking entirely. For example, Seefeldt et al. (1995) only proceeded in fitting the nonlinear model after establishing that the block effect was negligible. Issues such as multiple error terms in split-plot designs are simply not dealt with at all. In this paper, we examine a weed removal study carried out as a split-plot design with blocks and illustrate the use of SAS PROC NLMIXED to account for blocks and the two-level error structure

Early-season insect defoliation influences the critical time for weed removal in soybean

To develop more effective pest-management strategies, it is essential to understand how different pests interact with each other and the crop. Field studies were conducted in 2003 and 2004 at two Nebraska locations to determine the effects of early-season crop defoliation on the critical time for weed removal (CTWR) in narrow-row soybean. Three soybean defoliation levels were selected to simulate 0, 30, and 60% leaf tissue removal by the bean leaf beetle. Weeds were allowed to compete with the crop until V2, V4, V6, R3, and R5 growth stages. There were also season-long weedy and weed-free treatments. Results indicated that the CTWR in soybean occurred earlier as defoliation levels increased from 0 to 60%. The CTWR occurred at V3, V2, and V1 growth stage for 0, 30, and 60% defoliation levels, respectively. Overall, 60% defoliation resulted in earlier CTWR by at least 14 d. Yield losses from defoliation and weed interference were primarily associated with a reduction in number of pods per plant-1

MACROS AS A PROGRAMMING TOOL FOR SYNCHRONIZATION OF TWO NON‐SYNCHRONIZABLE INDUSTRIAL ROBOTS

The main goal of this paper was to connect two controllers of the older generation into one robotic cell, then software implements their synchronized work on servicing and welding certain objects. Two controllers manufactured by Yaskawa Motoman were used with the associated manipulators. The hardware solution involved connecting the controllers via General I/O circuit board. The software solution involved parallel programming of two robots for synchronized operation on a common task. During programming, it was necessary to create macro jobs that implemented repetitive actions, such as calling and waiting for robots, grabbing and releasing objects, as well as the actions of starting and stopping the program. The conclusion of presented research is that the robotic cell, formed by two robots that are not intended for synchronization, meets the requirements that until now could only be solved with robots of the newer generation. Proposed solution was confirmed by experimental verification

EC02-173 Spotted and Diffuse Knapweed

Spotted knapweed (Centaure amaculosa Lam. = C. biebersteinii DC.) and diffuse knapweed (C.diffusa Lam.) are two of Nebraska’s seven noxious weeds. They are also noxious in at least 17 other states. These are closely related species that are well adapted to a variety of habitats including open forests, rangelands and pastures, Conservation Reserve Program lands, roadsides, and ditch banks. Centaurea is a large genus of over 400 species, 32 of which are common weeds of the United States and several of which [e.g., yellowstar thistle, C. solstitalis L, and Russian knapweed, C. repens L. =Acroptilon repens (L.) DC.] have been identified officially as noxious weeds in nearby western states. Other Centaurea species areused as ornamentals. The knapweeds were introduced to the United States from the grasslands of southeastern Europe and Asia. Spotted knapweed now infests more than seven million acres and diffuse knapweed more than three million acres of rangeland and pastures in the western United States

EC02-173 Spotted and Diffuse Knapweed

Spotted knapweed (Centaure amaculosa Lam. = C. biebersteinii DC.) and diffuse knapweed (C.diffusa Lam.) are two of Nebraska’s seven noxious weeds. They are also noxious in at least 17 other states. These are closely related species that are well adapted to a variety of habitats including open forests, rangelands and pastures, Conservation Reserve Program lands, roadsides, and ditch banks. Centaurea is a large genus of over 400 species, 32 of which are common weeds of the United States and several of which [e.g., yellowstar thistle, C. solstitalis L, and Russian knapweed, C. repens L. =Acroptilon repens (L.) DC.] have been identified officially as noxious weeds in nearby western states. Other Centaurea species areused as ornamentals. The knapweeds were introduced to the United States from the grasslands of southeastern Europe and Asia. Spotted knapweed now infests more than seven million acres and diffuse knapweed more than three million acres of rangeland and pastures in the western United States

Interaction of quizalofop-p-ethyl with 2,4-D choline and/or glufosinate for control of volunteer corn in corn resistant to aryloxyphenoxypropionates

Corn resistant to aryloxyphenoxypropionates (FOPs) (Enlist™ corn) enables the use of quizalofop-p-ethyl (QPE) as a selective postemergence (POST) herbicide for control of glufosinate/glyphosate-resistant corn volunteers. Growers usually mix QPE with 2,4-D choline and/or glufosinate to achieve broad-spectrum weed control in Enlist™ corn. The objectives of this study were to (1) evaluate the efficacy of QPE applied alone or mixed with 2,4-D choline and/or glufosinate for control of glufosinate/glyphosate-resistant corn volunteers in Enlist™ corn and (2) determine the impact of application time (V3 or V6 growth stage of volunteer corn) of QPE-based treatments on volunteer corn control as well as Enlist™ corn injury and yield. Field experiments were conducted at South Central Agricultural Lab, Clay Center, NE in 2021 and 2022. Quizalofop-p-ethyl (46 or 93 g ai ha‒1 ) applied at V3 or V6 growth stage controlled volunteer corn ≥ 88% and ≥ 95% at 14 and 28 d after treatment (DAT), respectively. The QPE (46 g ai ha‒1 ) mixed with 2,4-D choline (800 g ae ha‒1 ) had 33% less expected control of V3 volunteer corn in 2021, and 8% less than expected control of V6 volunteer corn in 2022 at 14 DAT. Volunteer corn control was improved by 7%-9% using the higher rate of QPE (93 g ai ha‒1 ) in a mixture with 2,4-D choline (1,060 g ae ha‒1 ). The QPE mixed with glufosinate had an additive effect and interactions in any combinations were additive beyond 28 DAT. Mixing 2,4-D choline can reduce QPE efficacy on glufosinate/glyphosate-resistant corn volunteers up to 14 DAT when applied at the V3 or V6 growth stage; however, the antagonistic interaction did not translate into corn yield loss. Increasing the rate of QPE (93 g ai ha‒1 ) while mixing with 2,4-D choline can reduce antagonism