Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds to perform selective treatments and increase yield and crop health while reducing the amount of chemicals used. Deep‐learning approaches have recently achieved both excellent classification performance and real‐time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labeling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep‐learning‐based classifiers for different crop types, with the goal of reducing the retraining time and labeling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds and compare the performance and retraining efforts required when using data labeled at pixel level with partially labeled data obtained through a less time‐consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible and reduces training times for up to 80%. Furthermore, we show that even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data.     

喷射成形Al-Zn-Mg-Cu合金挤压态的组织和力学性能

研究了利用喷射成形辅以挤压制备Al-5.72Zn-2.36Mg-1.66Cu合金后优化的组织结构特征和力学性能。结果表明,合金基体组织均匀细化,晶粒形貌趋于圆整,平均晶粒大小达到10 μm左右。当合金的冷却条件通过快速凝固技术改变时,会产生不同程度的固溶强化效果和第二相弥散强化效果,从而改善了合金的整体性能。合金的屈服强度和抗拉强度分别平均提高了20%左右,且伸长率也略有提高。     

生物质电厂废弃物草木灰成分分析及成形

生物质热电厂焚烧废弃物的主要成分是草木灰，其产生量大、密度小。大量草木灰不经处理会造成环境污染问题。本文首先分析草木灰的化学成分，确定草木灰的肥料特征，通过添加NH₄H₂PO₄使草木灰的pH达到国家复合肥的相关标准。选用聚天冬氨酸（30%水溶液）作为复合肥的黏结剂，应用于草木灰复合肥的成形造粒，以增加其抗压强度和肥效。研究不同烘干时间、烘干温度、聚天冬氨酸添加量对草木灰复合肥抗压强度的影响规律，通过分析植物的形态及生理指标，进而研究草木灰复合肥的肥效。结果表明：最佳烘干条件为10h、140℃，聚天冬氨酸对草木灰的抗压强度有明显的增强作用，并且草木灰与聚天冬氨酸的复配比为6g∶1mL时肥效最好。此方法解决了元素循环中断、草木灰环境污染，在运输过程中草木灰复合肥易碎，纯草木灰肥效低等问题。     

Dairy farmers with larger herd sizes adopt more precision dairy technologies

An increase in the average herd size on Australian dairy farms has also increased the labor and animal management pressure on farmers, thus potentially encouraging the adoption of precision technologies for enhanced management control. A survey was undertaken in 2015 in Australia to identify the relationship between herd size, current precision technology adoption, and perception of the future of precision technologies. Additionally, differences between farmers and service providers in relation to perception of future precision technology adoption were also investigated. Responses from 199 dairy farmers, and 102 service providers, were collected between May and August 2015 via an anonymous Internet-based questionnaire. Of the 199 dairy farmer responses, 10.4% corresponded to farms that had fewer than 150 cows, 37.7% had 151 to 300 cows, 35.5% had 301 to 500 cows; 6.0% had 501 to 700 cows, and 10.4% had more than 701 cows. The results showed that farmers with more than 500 cows adopted between 2 and 5 times more specific precision technologies, such as automatic cup removers, automatic milk plant wash systems, electronic cow identification systems and herd management software, when compared with smaller farms. Only minor differences were detected in perception of the future of precision technologies between either herd size or farmers and service providers. In particular, service providers expected a higher adoption of automatic milking and walk over weighing systems than farmers. Currently, the adoption of precision technology has mostly been of the type that reduces labor needs; however, respondents indicated that by 2025 adoption of data capturing technology for monitoring farm system parameters would be increased.     

A new one-phase material model for the numerical prediction of critical material flow conditions in thixoforging processes

Thixoforging allows one-step forming processes of near-net shape components having excellent mechanical properties. However, the high sensitivity of thixoforging regarding process conditions requires precise modelling and determination of process related parameters. At the same time, simple numerical design proves challenging because of the inaccuracy of existing one-phase material models regarding the shear thinning flow behaviour of semi solid metals. Consequently, this paper deals with the development of a new one-phase material model providing a more precise simulation of materials’ shear rate dependency. By using this model, simulations could be performed, which allowed the prediction of solidification and flow-related component defects.     

Bulging in incremental sheet forming of cold bonded multi-layered Cu clad sheet: Influence of forming conditions and bending

Bulge is a defect that causes geometrical inaccuracy and premature failure in the innovative incremental sheet forming (ISF) process. This study has two-fold objectives: (1) knowing the bulging behavior of a Cu clad tri-layered steel sheet as a function of forming conditions, and (2) analyzing the bending effect on bulging in an attempt to identify the associated mechanism. A series of ISF tests and bending analysis are performed to realize these objectives. From the cause-effect analysis, it is found that bulge formation in the layered sheet is sensitive to forming conditions in a way that bulging can be minimized utilizing annealed material and performing ISF with larger tool diameter and step size. The bending under tension analysis reveals that the formation of bulge is an outgrowth of bending moment that the forming tool applies on the sheet during ISF. Furthermore, the magnitude of bending moment depending upon the forming conditions varies from 0.046 to 10.24 N·m/m and causes a corresponding change in the mean bulge height from 0.07 to 0.91 mm. The bending moment governs bulging in layered sheet through a linear law. These findings lead to a conclusion that the bulge defect can be overcome by controlling the bending moment and the formula proposed can be helpful in this regards.     

镍基高温合金锥筒形件拉深旋压成形机理研究

针对镍基高温合金因加工硬化严重成形时极易产生破裂和起皱等典型缺陷的问题,以锥筒形壳体类零件为对象,提出了一种由锥形预制坯经过真空固溶处理后拉深旋压成形锥筒形件的方法,并对其成形机理进行了研究。基于Abaqus/Explicit平台,建立了锥筒形件拉深旋压有限元模型,分析了成形过程中的瞬态等效应力、等效塑性应变、切向应力、壁厚及三向应变分布规律。结果表明:在旋压成形过程中,最大瞬态等效应力位于旋轮接触区及附近区域、最大瞬态等效塑性应变位于坯料口部;瞬态切向压应力最大值位于旋轮接触区,而瞬态切向拉应力最大值位于旋轮接触区附近的两侧区域。筒形段中部壁厚减薄,而坯料口部壁厚增厚。旋压成形试验表明,锥形预制坯经拉深旋压后可获得壁厚均匀的锥筒形件。     

[成形过程的数据挖掘与深度学习方法]基于机器学习的管材弯曲回弹有效预测与补偿

采用基于优化的误差反向传播(BP)神经网络的机器学习算法建模，提出了考虑材料参数、几何参数等多因素的弯管回弹精确预测和高效控制方法。该方法通过引入非线性惯性权重及遗传算法的杂交算子，改进了粒子群优化(PSO)算法，进而通过改进的PSO算法对BP神经网络进行优化，构建了基于改进的PSO-BP神经网络机器学习回弹预测和补偿模型。以多种规格的铝合金数控弯管构件为对象，将实际生产中不同规格、批次、成形参数下回弹数据作为训练样本，实现了所建机器学习预测模型的应用验证。所建模型获得的预测结果平均相对误差为6.3%，与未优化的BP神经网络等传统模型相比，预测精度最大提高了18.5%，计算时间可从1.5 h缩短至300 s，同时实现了回弹预测与补偿精度以及计算效率的显著提高。     

高温合金蜂窝成形制造工艺及性能评价

目的 研究高温合金蜂窝成形工艺参数优化规律。方法 采用辊压成形、整形和点焊工艺制备高温合金蜂窝芯体，并对成形后的蜂窝芯体进行性能表征及失效分析。研究辊压成形工艺、整形工艺和点焊工艺参数，对采用最优工艺参数得到的高温合金蜂窝芯体样件分别进行压缩、剪切性能测试，对样件进行性能表征及失效分析，得出其最大压缩力、平面压缩强度以及失效方式。结果 选取30 t压力时，校形效果良好；当焊接电流为500 A，焊接时间为0.005 s时，焊点质量最好。结论 采用优化后的工艺参数制得的样件尺寸精度和质量较高，能够为高温合金蜂窝制造提供指导。     

基于连续介质损伤力学的7075铝合金高温成形极限理论预测与数值仿真

开展7075铝合金高温单向拉伸试验和成形极限试验，获得了不同温度和应变率的应力-应变曲线和成形极限曲线。结果表明，在较高的温度和应变率时7075铝合金的强度减小、成形性提高。为描述7075铝合金高温损伤演化过程，提出一种改进的连续介质损伤模型，并建立了耦合损伤的多轴统一黏塑性本构模型。基于试验结果，运用NSGAII遗传算法标定了模型中的参数，标定后的本构模型可以很好地预测7075铝合金的高温热力行为和极限应变。通过有限元软件Abaqus的用户材料子程序VUMAT，该本构模型被编入到Abaqus软件中进行数值仿真计算。结果表明，仿真获得的成形极限曲线和应变场分布与试验和理论结果吻合度好，进一步证明了所建立的耦合损伤的多轴本构模型的正确性及其在高温成形极限有限元仿真中的适用性。