Special issue on machine learning in additive manufacturing

After 30 years of continuous development, additive manufacturing has successfully achieved mainstream acceptance as a popular manufacturing process. By creating products based on a 3D model, layer-bylayer, additive manufacturing allows for the production of complex parts with greater freedom in design optimization, surpassing traditional manufacturing techniques (Gibson et al. 2021). Meanwhile, machine learning has emerged as a hot technology with numerous applications in medical diagnosis, image processing, prediction, classification, learning association, and regression (Kotsiantis, Zaharakis, and Pintelas 2006). This has drawn increasing attention towards the use of machine learning in the manufacturing industry, particularly in the field of additive manufacturing (Jiang et al. 2020; Qin et al. 2022). The rapid progress of machine learning in additive manufacturing has brought us to this special issue, where we hope to bring together researchers with diverse research backgrounds in a common forum to accelerate the development of additive manufacturing technology through the aid of machine learning. We are enthusiastic about contributing to this cutting-edge research topic and driving further advancements in AM technology. In this special issue, there are nine papers accepted in the end with authors from Germany, India, Denmark, United States, Australia, Canada, Singapore, and China. The paper ‘A survey of machine learning in additive manufacturing technologies’ (Jiang 2023) gives a stateof-the-art survey on machine learning in additive manufacturing and provides some guidelines for future applications of machine learning in additive manufacturing. The next paper ‘Towards deep-learning-based image enhancement for optical camera-based monitoring system of laser powder bed fusion process’ (Zhang et al. 2022) introduces a super-resolution (SR) algorithm based on U-Net that can effectively enhance the details of optical camera monitoring images in the laser powder bed fusion (LPBF) process. A test setup was constructed in the laboratory to generate highresolution images for training. To obtain accurate original images for validation purposes, lowresolution images were created by downscaling and blurring high-resolution images. The effectiveness of the SR algorithm was evaluated using the peak signalto-noise ratio (PSNR) and plausibility of details as metrics. The results clearly demonstrate that this SR algorithm is capable of reconstructing intricate features from low-resolution images in the LPBF process. The paper ‘Prediction of mechanical properties for acrylonitrile-butadiene-styrene parts manufactured by fused deposition modelling using artificial neural network and genetic algorithm’ (Mohd et al. 2022) uses a multi-parameter regression model to predict the mechanical properties of acrylonitrile-butadienestyrene parts manufactured by fused deposition modelling. The model establishes a direct relation between choosing process parameters correctly and enhancing performance in fused deposition modelling. The model can provide optimal solutions for getting allotted output values. The paper ‘Online monitoring for error detection in vat photopolymerization’ (Frumosu et al. 2023) proposes an online monitor system for bottom-up photopolymerization additive manufacturing systems. The data generated by the sensor is utilized to detect the detachment error of manufactured parts from the build platform, which cannot be physically observed by machine operators. If left undetected, this detachment can result in material waste and downtime, without stopping the ongoing build job. The online monitoring procedure is performed in two distinct phases: an offline training phase, followed by an online monitoring phase. During the offline phase, a prediction model is trained to be used in conjunction with a control chart for online monitoring. The monitoring control chart is particularly beneficial as it allows for the detection and recording of detachment predictions exclusively. The next paper ‘Learning with supervised data for anomaly detection in smart manufacturing’ (Meiling et al. 2023) proposes a model selection architecture INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2023, VOL. 36, NO. 9, 1255–1257 https://doi.org/10.1080/0951192X.2023.2235679