Chao Wei and Lin Li discuss how different material properties could be integrated into single parts using additive manufacturing
Analysis & opinion
11 December 2020
Nicholas Goffin investigates where energy savings can be made in laser processing
Figure 1: Complex two-phase copper vapour chambers, for high-power space applications, built using laser-powder bed fusion.
11 December 2020
Brett Diehl, of the Penn State Applied Research Laboratory, puts neural networks to work in identifying voids in additive manufacturing
25 November 2020
Dr Stefan Janssen discusses the scanner-based laser processing technique developed within the Carbolase project
25 November 2020
Simone Mazzucato discusses how lasers can be used to create hidden microfeatures in materials for anti-counterfeit purposes
25 September 2020
Trumpf’s David Harvilla, Stefanie Bisch and Sebastian Zaske discuss why green lasers represent the perfect tool for welding e-mobility components made of copper
25 September 2020
Nataliya Deyneka-Dupriez and Alexander Denkl, of Lessmüller Lasertechnik, describe how OCT can be used to perform online process monitoring when using an oscillating laser beam to weld e-mobility components
25 September 2020
ES Precision’s new UV laser is paying for itself by opening up a range of valuable niche processing jobs, writes director Andrew May
The recent emergence of blue laser processing shows promise for copper processing applications in e-mobility. (Image: Nuburu)
25 September 2020
Over the summer laser users learned how two types of blue diode laser are dramatically improving copper welding for fabricating e-mobility components
15 June 2020
Anibal Di Luch, of TWI, reports on the development of a robotic platform for the laser processing of large-scale, high-end structures for aircraft
11 June 2020
Dr Paulina Morawska, research associate at Heriot-Watt University, discusses how ultrafast lasers can be used to address the encapsulation challenges of OLEDs
09 June 2020
Christian Knaak and Peter Abels, of Fraunhofer ILT, explain how imaging and machine learning can be used to identify weld imperfections with high accuracy
