Multi-sensor in-process metrology of laser powder bed fusion additive manufacturing: Fusing form, texture and temperature measurement.

激光粉末床熔融增材制造的多传感器过程中计量：熔融形式、纹理和温度测量。

基本信息

批准号：
EP/P021468/1
负责人：
Simon Donald Aldred John Lawes
金额：
$ 36.08万
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FP021468%2F1
关键词：
Multi sensor process metrology laser

项目摘要

This proposal is to develop a multi-sensor system for in-process metrology of parts made by the additive manufacturing (AM) process - laser powder bed fusion (L-PBF). AM, also known as '3D printing', is changing the way that engineers solve the problems of today. Unlike subtractive manufacturing methods, where materials must be cut away to produce a finished part, AM processes build parts up layer-by-layer. This provides almost limitless design freedom, allowing the design of more organic, more lightweight, more bespoke solutions. However, the technology is not without its challenges. The current state of AM technology cannot produce parts with the consistency or geometric tolerances that are required for many applications. The production of metal parts by AM is particularly challenging. The most prominent technology for producing AM metal parts is L-PBF, also called selective laser melting. To produce parts economically the process must be fast with high laser power making L-PBF a highly energetic process that is sensitive to a changes in process variables. Defects can occur at any stage of the process: incomplete melting, aggregation of unmelted powder, pitting, balling, spattering, as well as defects caused by thermal or residual stresses: cracking, spalling and layer separation. Effective control of the L-PBF process is an extremely challenging task, and the subject of significant research both in the UK and global research communities. One aspect of that challenge that has become clear in the last few years is the need for step change improvements in in-process condition monitoring and metrology. The key parameters for in-process control are the melt pool temperature, the powder bed temperature and the presence of physical defects in the powder laying and laser fusion stages. The laser fusion event that consolidates the powder takes place over a few hundreds of nanoseconds, making it very difficult to observe and control in real-time. Fortunately, a great deal of information about the melting conditions can be observed in the consolidated surface that fusion leaves behind; a so-called process signature or fingerprint. By capturing information on the form and the texture of the part surface it is possible to determine whether the laser and scan parameters have been chosen correctly, and critically it is also possible to monitor whether any major defects have occurred. AM processes, including L-PBF, are not yet mature enough that quality can be assured. Each machine will have slightly different performance characteristics, and the part quality can change from day to day, with small changes in the environment, the powder quality or the laser condition. For AM to be more widely adopted, industries need assurance, and that means highly robust in-process measurements.Current in-process measurement methods are inadequate; 2D imaging methods cannot identify all of the common defects or measure surface texture in the process fingerprint. The few pre commercial 3D measurement systems that have been demonstrated, have been unable to accommodate the extreme range in texture observed for L-PBF. In simple terms the surfaces are either too reflective for some methods, or too diffuse for others, often producing misleading imaging artefacts or missing significant defects. This lack of robust in-process metrology, stymies development and slows the wider adoption of L-PBF. What is required is a robust measurement of form, texture and thermal distribution of the metal powder bed. This proposal will achieve that aim by the intelligent combination of measurement data captured by multiple sensor systems. Each sensor individually cannot capture the whole surface, but when combined, will offer the most complete in process measurement achievable to date. This multi-sensor system will have profound benefits for process control of L PBF processes as well as providing a wealth of in process data to feed into future research.

该建议是开发一个多传感器系统，用于添加剂制造（AM）过程-激光粉末床融合（L-PBF）制造的零件中的计量。 AM也称为“ 3D打印”，正在改变工程师解决当今问题的方式。与减税制造方法不同，必须切掉材料以生产成品的零件，AM流程会逐层建立零件。这几乎提供了无限的设计自由，可以设计更有机，更轻巧，更定制的解决方案。但是，该技术并非没有挑战。 AM技术的当前状态无法产生许多应用所需的一致性或几何公差的零件。 AM生产金属零件特别具有挑战性。生产AM金属零件的最突出的技术是L-PBF，也称为选择性激光熔化。要在经济上产生零件，该过程必须快速使用高激光功率，这使得L-PBF成为一个高能的过程，对过程变量的变化很敏感。缺陷可能在过程的任何阶段发生：不完整的熔化，未渗透粉末的聚集，点斑，球，飞溅，以及由热应力或残留应力引起的缺陷：开裂，剥落和层分离。 L-PBF流程的有效控制是一项极具挑战性的任务，并且在英国和全球研究社区中都进行了重要的研究。在过去几年中，这一挑战的一个方面是需要改善过程中的状况监测和计量的步骤变更。进程内控制的关键参数是熔融池温度，粉末床温度以及粉末铺设和激光融合阶段中的物理缺陷。巩固粉末的激光融合事件发生在几百纳秒内，因此很难实时观察和控制。幸运的是，可以在融合叶片留下的合并表面上观察到有关熔融条件的大量信息。所谓的过程签名或指纹。通过捕获有关零件表面的形式和质地的信息，可以确定是否正确选择了激光和扫描参数，并且在批判性地可以监视是否发生任何重大缺陷。包括L-PBF在内的AM流程还不够成熟，可以确保质量得到确保。每台机器的性能特性都会略有不同，并且零件质量每天都会发生变化，环境，粉末质量或激光条件的变化很小。为了使AM被更广泛地采用，行业需要保证，这意味着高度健壮的过程中的测量值。流程内的测量方法不足； 2D成像方法无法在过程指纹中识别所有常见缺陷或测量表面纹理。已经证明的少数商业3D测量系统无法适应L-PBF观察到的质地的极端范围。简而言之，对于某些方法而言，表面太反光了，或者对于其他方法而言太分散了，通常会产生误导性的成像人工制品或缺失的显着缺陷。缺乏强大的过程中的计量，Stymies的发展和减慢了L-PBF的更广泛采用。需要的是对金属粉床的形式，质地和热分布的强大测量。该建议将通过多个传感器系统捕获的测量数据的智能组合来实现这一目标。每个传感器都无法单独捕获整个表面，但是当组合在一起时，将提供最完整的过程测量值。该多传感器系统将对L PBF流程的过程控制以及提供大量的过程数据有深远的好处，以融入未来的研究中。