Purpose: The pulsed-laser powder bed fusion (PBF) process is an additive manufacturing technology that uses a laser with pulsed beam to melt metal powder. In this case, stainless steel SS316L alloy is used to produce complex components. To produce components with acceptable mechanical performance requires a comprehensive understanding of process parameters and their interactions. This study aims to understand the influence of process parameters on reducing porosity and increasing part density. Design/methodology/approach: The response surface method (RSM) is used to investigate the impact of changing critical parameters on the density of parts manufactured. Parameters considered include: point distance, exposure time, hatching distance and layer thickness. Part density was used to identify the most statistically significant parameters, before each parameter was analysed individually. Findings: A clear correlation between the number and shape of pores and the process parameters was identified. Point distance, exposure time and layer thickness were found to significantly affect part density. The interaction between these parameters also critically affected the development of porosity. Finally, a regression model was developed and verified experimentally and used to accurately predict part density. Research limitations/implications: The study considered a range of selected parameters relevant to the SS316L alloy. These parameters need to be modified for other alloys according to their physical properties. Originality/value: This study is believed to be the first systematic attempt to use RSM for the design of experiments (DOE) to investigate the effect of process parameters of the pulsed-laser PBF process on the density of the SS316L alloy components.

Deep neural network models have become powerful tools of machine learning and artificial intelligence. They can approximate functions and dynamics by learning from examples. This paper reviews the state-of-art of deep learning-based object detection frameworks that are used for fruit detection in general and for olive fruit in particular. A dataset of olive fruit on the tree is built to train and evaluate deep models. The ultimate goal of this work is the capability of on-edge real-time olive fruit detection on the tree from digital videos. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed You Only Look Once version five (YOLOv5). This paper builds a dataset of 1.2 K source images of olive fruit on the tree and evaluates the latest object detection algorithms focusing on variants of YOLOv5 and YOLOR. The results of the YOLOv5 models show that the YOLOv5 new network models are able to extract rich olive features from images and detect the olive fruit with a high precision of higher than 0.75 mAP_0.5. YOLOv5s performs better for real-time olive fruit detection on the tree over other YOLOv5 variants and YOLOR.

Additive friction stir deposition (AFSD) is an emerging solid-state additive manufacturing process with a high deposition rate. Being a non-fusion additive manufacturing (AM) process, it significantly eliminates problems related to melting such as cracking or high residual stresses. Therefore, it is possible to process reactive materials or high-strength alloys with high susceptibility to cracking. Although the residual stresses are lower in this process than with the other AM processes, depending on the deposition path, geometry, and boundary conditions, residual stresses may lead to undesired deformations and deteriorate the dimensional accuracy. Thermal cycling during layer deposition, which also depends on the geometry of the manufactured component, is expected to affect mechanical properties. To this day, the influence of the deposit geometry on the residual stresses and mechanical properties is not well understood, which presents a barrier for industry uptake of this process for large-scale part manufacturing. In this study, a stepped structure with 4, 7, and 10 passes manufactured via AFSD is used to investigate changes in microstructure, residual stress, and mechanical property as a function of the number of passes. The microstructure and defects are assessed using scanning electron microscopy and electron backscatter diffraction. Hardness maps for each step are created. The residual stress distributions at the centreline of each step are acquired via non-destructive neutron diffraction. The valuable insights presented here are essential for the successful utilisation of AFSD in industrial applications.

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology that utilises bar feedstocks as the input material and frictional–deformational heat as the energy source. AFSD offers high deposition rates and is a promising technique for achieving defect-free material properties like wrought aluminium, magnesium, steel, and titanium alloys. While it offers benefits in terms of productivity and material properties, its low technology readiness level prevents widespread adoption. Academics and engineers are conducting research across various subfields to better understand the process parameters, material properties, process monitoring, and modelling of the AFSD technology. Yet, it is also crucial to compile and compare the research findings from past studies on this new technology to gain a comprehensive understanding and pinpoint future research paths. This paper aims to present a comprehensive review of AFSD focusing on process parameters, material properties, monitoring, and modelling. In addition to examining data from existing studies, this paper identifies areas where research is lacking and suggests paths for future research efforts.

Additive friction stir deposition (AFSD) is a solid-state AM method that feeds, plasticizes, and deposits solid bars using frictional heat. Although the AFSD is a promising method, its limited technology readiness level precludes its wider use. The use of optimum process parameters is critical for achieving successful results, and closed-loop control of process parameters can improve quality even further by reacting to and resolving any unanticipated issues that arise during the process. This article investigates the utilization of a process monitoring setup including various sensors to examine temperatures, forces, vibrations, and sound during the AFSD of the Al6061 aluminum alloy. Furthermore, it benchmarks the outcomes of the same process’ parameter set with or without utilizing a proportional–integral–derivative (PID). Large thermal gradients were observed at various locations of the deposit. Significant fluctuations in temperature and force were demonstrated for the initial layers until stability was reached as the height of the deposit increased. It has been shown that the change in the process parameters may lead to undesired results and can alter the deposit shape. Finally, residual stresses were investigated using the contour measurement technique, which revealed compressive stresses at the core of the part and tensile stresses in the outer regions.