Why is in situ quality control of laser keyhole welding a real challenge?

Optimization of Process Parameters for the Laser Polishing of Hardened Tool Steel

In mold making, the mold surface roughness directly affects the surface roughness of the produced part. To achieve surface roughness below 0.8 μm, the cost of surface finish is high and time-consuming. One alternative to the different grinding and polishing steps is laser polishing (LP). This study investigates and models the LP of tool steel (X38CrMoV5-1-DIN 1.2343), typical for the mold industry, having an initial rough surface obtained by electrical discharge machining. The microstructures of the re-melted layer and heat-affected zone due to the LP process were also studied. Four parameters: the laser spot size, velocity, maximum melt pool temperature and overlapping were investigated via a design of experiments (DoE) approach, specifically a factorial design. The responses were line roughness (Ra), surface roughness (Sa), and waviness (Wa). The surface topography was measured before and after the LP process by white light profilometer or confocal microscopy. DoE results showed that the selected factors interact in a complex manner, including the interactions, and depend on the responses. The DoE analysis of the results revealed that the roughness is mainly affected by the velocity, temperature and overlap. Based on a first DoE model, an optimization of the parameters was performed and allowed to find optimum parameters for the LP of the rough samples. The optimum conditions to minimize the roughness are a spot size of 0.9 mm, a velocity of 50 mm/s, a temperature of 2080 °C and an overlap of 90%. By using these parameters, the roughness could be reduced by a factor of almost 8 from 3.8 µm to approximately 0.5 µm. Observations of the microstructure reveal that the re-melted layer consists of columnar grains of residual austenite. This can be explained by the carbon intake of the electro-machined surface that helps stabilize the austenitic phase.

2D and 3D Triangulation Are Suitable In Situ Measurement Tools for High-Power Large Spot Laser Penetration Processes to Visualize Depressions and Protrusions before Perforating

During laser penetration, the irradiated samples form a melt pool before perforation. Knowledge of the dynamics of this melt pool is of interest for the correct physical description of the process and leads to improved simulations. However, a direct investigation, especially at the location of high-power laser interaction with large spot diameters in the centimeter range is missing until now. Here, the applicability of 2D triangulation for surface topology observations is demonstrated. With the designed bidirectional 2D triangulation setup, the material cross-section is measured by profile detection at the front and back side. This allows a comprehensive description of the penetration process to be established, which is important for a detailed explanation of the process. Specific steps such as surface melting, indentations, protrusions during melt pool development and their dynamics, and the perforation are visualized, which were unknown until now. Furthermore, a scanning 3D triangulation setup is developed to obtain more information about the entire melt pool at the front side, and not just a single intersection line. The measurements exhibit a mirror-symmetric melt pool and the possibility to extrapolate from the central profile to the outer regions in most cases.

Laser Beam and Laser-Arc Hybrid Welding of Aluminium Alloys

Aluminium alloys are widely used in many industries due to their high strength-to-weight ratios and resistance to corrosion. Due to their specific thermophysical properties and intricate physical metallurgy, these alloys are challenging to weld. Work-hardened alloys may experience strength loss in heat-affected zones (HAZ). The strength of precipitation-hardened alloys is severely damaged in both HAZ and weld metal due to coarsening or full dissolution. The high thermal conductivity and reflectivity of aluminium causes lower laser beam absorptivity with lower processing efficiency. Weld imperfections such as porosity, humping, and underfills are frequently formed due to the low melting point and density promoting high liquidity with low surface tension. Porosity is the most persistent imperfection and is detrimental for mechanical properties. In this work, extensive review was made on laser beam and laser-arc hybrid welding of aluminium alloys. Solidification cracking, evaporation of alloying elements, porosity and keyhole stability, and other challenges are studied in detail. The current development of laser welding of aluminium alloys is not so mature and new discoveries will be made in the future including the use of newly developed laser systems, welding consumables, welding methods, and approaches.

Machine learning monitoring for laser osteotomy

This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser-induced acoustic emission, detected by an airborne microphone sensor. The analysis of the acoustic signals is carried out using a machine learning algorithm that is pre-trained in a supervised manner. The efficiency of the method is experimentally evaluated with several types of tissues, which are: skin, fat, muscle, and bone. Several cutting-edge machine learning frameworks are tested for the comparison with the resulting classification accuracy in the range of 84-99%. It is shown that the datasets for the training of the machine learning algorithms are easy to collect in real-life conditions. In the future, this method could assist the doctors during laser osteotomy, minimizing the damage of the nearby healthy tissues and provide cleaner pathologic tissue removal.

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Laser welding is a key technology for many industrial applications. However, its online quality monitoring is an open issue due to the highly complex nature of the process. This work aims at enriching existing approaches in this field. We propose a method for real-time detection of process instabilities that can lead to defects. Hard X-ray radiography is used for the ground truth observations of the sub-surface events that are critical for the quality. A deep artificial neural network is applied to reveal the unique signatures of those events in wavelet spectrograms from the laser back-reflection and acoustic emission signals. The autonomous classification of the revealed signatures is tested on real-life data, while the real-time performance is reached by means of parallel computing. The confidence of the quality classification ranges between 71% and 99%, with a temporal resolution down to 2 ms and a computation time per classification task as low as 2 ms. This approach is a new paradigm in the digitization of industrial processes and can be exploited to provide feedbacks in a closed-loop quality control system.