基于邻层数据匹配的工业 CT 图像生成 G 代码方法

针对目前工业CT图像转换为3D打印G代码方法效率低的问题,提出一种基于邻层数据匹配的工业CT图像直接转换成G代码的方法。首先采用Canny算子提取工业CT图像的轮廓,然后处理轮廓分叉问题,实现邻层间几何信息数据匹配,其次进行邻层间轮廓插值以满足3D打印层间厚度要求,从而避免"阶梯效应",最后通过填充编码得到用于3D打印的G代码。使用本文提出的方法,轮毂CT图像转换为G代码的时间为10.5 s,耗时远小于其他间接转换方法;3D打印出的轮毂无"阶梯效应",平均尺寸误差率为0.25%。实验结果表明,该方法不涉及中间格式,转换效率高,转换误差与传统方法相当,适用于具有复杂内腔结构的零件。     

PBAT‒PLA膜袋受控需氧堆肥条件下的降解研究

目的 通过对广泛使用的PBAT–PLA生物降解膜袋在受控需氧堆肥条件下的降解机制研究，为生物降解塑料的大规模推广提供重要理论基础。方法 根据GB/T 19277.1—2011，在(58±2)℃需氧条件下，对PBAT–PLA膜袋进行为期160 d的生物降解测试(即工业堆肥)，并以常见的可降解材料微晶纤维素作为参比样品。对降解前后的材料进行红外、扫描电镜、能谱分析，并结合其所在堆肥样本的脂肪酶活性，从多角度探寻降解机制。结果 PBAT–PLA膜袋与微晶纤维素所在的堆肥脂肪酶活性都达到空白堆肥的3倍以上。红外显示由微晶纤维素水分子吸附、糖环打开、基团氧化形成的吸收峰加强，PBAT–PLA膜袋中的酯键峰明显减弱;扫描电镜发现降解的PBAT–PLA膜袋表面覆盖了微生物膜;能谱分析发现，碳元素大幅减少，氧元素增加。结论 微生物在PBAT–PLA膜袋表面生长形成生物膜，分泌大量脂肪酶，水解PBAT–PLA的酯键，使聚合物降解为不同链长的中间体或小分子，同时伴随着氧化，随后被作为碳源，在相关微生物体内被代谢利用，形成最终产物。     

稳产国Ⅵ柴油并兼产喷气燃料技术方案的工业应用

介绍了浙江石油化工有限公司新建的3 Mt/a柴油加氢精制装置，其配套使用中国石化石油化工科学研究院有限公司开发的催化剂级配技术，并实施了可根据原料供应及市场产品需求情况灵活调整切换的2种生产技术方案。1 a的安稳生产运行结果表明:该装置以直馏柴油为主原料，通过分馏塔的馏分切割及其侧线抽出，实现了稳产国Ⅵ柴油并兼产喷气燃料技术方案的工业化应用；在实施以兼产喷气燃料为主的生产技术方案时，通过调整常一线柴油的掺炼量，不仅可以生产含硫量小于10.0 μg/g的精制柴油产品，同时兼产所得到的喷气燃料产品含硫量小于0.5 μg/g，赛波特颜色号值大于30；在实施主产精制柴油组分方案时，通过掺炼质量分数为20%~40%的催化柴油，并使所提炼得到的精制柴油组分含硫量小于6.0 μg/g的前提下，这些精制柴油组分产品既可直接作为满足国Ⅵ柴油产品出厂待售，也可作为柴油调和组分储存待用于产品的进一步优化。     

产业链视角下新冠疫情对我国皮革制造业发展的影响研究

为妥善应对疫情对我国皮革制造业造成的冲击,应从稳定产业链结构出发,通过升级自身制造水平,确保我国皮革产业在全球产业链的重构过程中维持原有不可替代性。文章从疫情对产业链的影响出发,分析我国皮革行业经济运行情况与产业链阶段性变化,并得出皮革产业在限制性条件下的发展启示。     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.     

定制家居订单智慧标签一码贯通技术研究与应用

新一轮全球科技革命和产业变革兴起,受其影响,定制家居也在智能制造的大潮中快速转型升级,从传统制造迅速转变为数据驱动型的先进制造。定制家居行业的特点决定了企业需要确保差异化订单产品能在规模化生产线上快速、精准流转,进而可以准确交付产品给客户。为此,研究并建立基于工业互联网的个性化定制家居订单智慧标签一码贯通技术和系统平台,在实现标准化编码的基础上,运用智慧标签高效、通畅、精准的数据驱动整体业务流转,实现从客户到客户的全业务环节智能流转和制造环节的集成贯通,通过智慧标签的流转解决了个性化订单差异化产品精准交付的问题,同时提高生产流程的自动化、智能化、数字化水平,促进企业高速高质量发展,推动行业进步。     

Effect of baking in different ovens on the quality and structural characteristics of saltine crackers

The aim of the study was to evaluate the physical and microstructural characteristics of crackers baked in four different industrial baking ovens (indirect radiation-cyclotherm, indirect convection, hybrid and industrial tunnel-ITO). Indirect convection and cyclotherm ovens provide the highest (5685.43 ± 51 W m⁻²) and the lowest (4860 ± 38.87 W m⁻²) amount of heat flux, respectively. Despite the amount of heat flux, indirect convection led to crackers with the highest moisture (7.86% vs. 4.82% in clyclotherm) and specific volume, but the lowest hardness. Cyclotherm resulted in crackers with lower specific volume, surface area, porosity, smooth and regular surface. Conversely, the hybrid and ITO ovens showed closer heat flux, leading to crackers with similar moisture content, texture parameters, specific volume, browning and inner porosity. Overall results show the potential of baking using different ovens for modifying the quality parameters of the crackers.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

Co?N graphene encapsulated cobalt catalyst for H2O2 decomposition under acidic conditions

Hydrogen peroxide (H₂O₂) has been listed as one of the 100 most important chemicals in the world. However, huge amount of residual H₂O₂ is hard to timely decomposed into O₂ and H₂O under acidic condition, easily resulting in explosion hazard. Here, we reported a core–shell structure catalyst, that is graphene with Co N structure encapsulated Co nanoparticles. Co N graphene shell serves as the active site for the H₂O₂ decomposition, and Co core further enhance this decomposition. Benefiting from it, the H₂O₂ decomposition were close to 100% after 6 cycles without pH adjustment, which increased 6 orders of magnitude compared with no catalyst. At the same time, the O₂ generation reached 99.67% in 2 h with little metal leaching, and ·OH has been greatly inhibited to only 0.08%. This work can cleanly remove H₂O₂ with little deep oxidation and protect the process of H₂O₂ utilization to achieve a safer world.