Additive manufacturing (AM) is a revolutionary approach to industrial produc-tion that enables manufacturing lighter, more significant components and sys-tems by directing hardware to deposit material in precise geometric shapes, layer upon layer, using data from computer-aided design (CAD) software or 3D object scanners. Design for additive manufacturing (DfAM) refers to creating a product design that takes advantage of the unique capabilities of AM. This study investi-gates the role of Augmented Reality (AR) as a tool for enhancing the learning experience of DfAM using Head-mounted Displays (HMDs). An AR application for Microsoft HoloLens 2 is created to allow the exploration of selected DfAM models and their industrial use cases. We analyse how the design thinking tech-nique is implemented in creating this study, from the stage of understanding and defining the problem to the final solution. Further, we discuss the process of cre-ating the innovative interaction design through the cross-platform game engine Unity and examine the structure of the application AR DfAM Explorer as well as the relevance of DfAM models chosen for the visualization. A questionnaire was prepared, and a real-time hands-on validation of the developed prototype was carried out with experts from the field of AM as well as students learning AM. Moreover, a pilot study assessed the effect of learning using HMD compared to the paper variant on students’ knowledge change in DfAM principles and related AM topics. The results obtained from the validation and pilot study were scruti-nized and hence the advantages and limitations of the HMD visualization were discussed. Finally, an outlook for future developments and further extensions of this idea are provided.

Additive Manufacturing (AM) has great potential of disrupting product design and supply chains in many industries by means of its unique capabilities when compared to traditional manufacturing. Moreover, the use of AM offers potential to produce in a more sustainable way. Despite these advantages of AM, its implementation still progresses rather slowly. Given the growing significance of AM, this research aims to identify the essential skills that an employee must possess to excel in this field and effectively utilize AM in the industry. For this purpose, this study is focused on using natural language processing algorithms to process data and analyse text from AM job postings. After gathering the information from the source sites using a web scrapper and API, the data was cleaned, normalized, lemmatized, and tokenized. Word2Vec, LSTM and BERT algorithms were implemented to find the most requested skills. Using word2vec the algorithm was able to obtain similar words considering the inputted ones. Using the LSTM algorithm, the 35 most required skills were extracted. Further analysis was done, and the data was broken down by industry, AM technique, programming skills and design software. The third algorithm used was named entity recognition with BERT, which identified complementary skills to the ones mapped by the other algorithms. The three algorithms offered different and complementary insights to required skillset by AM industry, covering mostly hard and soft skills along the whole AM process chain. The use of each algorithm, corresponding user group and further developments are then suggested.

This paper discusses the relevance of digital assistance systems in the form of Augmented Reality (AR) in the context of the Metal Binder Jetting (MBJ) process chain. In its present condition, the MBJ process is characterized by highly divided manual process steps. The only step to be viewed as automated is the fabrication of the green parts. Thus, every other step must be operated by a trained or supervised individual. Some steps rely more on human interaction than others. The most human error-prone step is the manual depowdering of said green parts. Digital assistance comes in a wide variety of forms. In this paper, the utilization of AR in a head-mounted display (HMD) for instruction and quality improvement is investigated. For that purpose, an AR application was developed as a Proof of Concept (PoC). To evaluate, two independent groups of test subjects, with and without the assistive system, were tasked with the depowdering of green parts produced via MBJ. Half of the subjects had prior experience with depowdering. Under test circumstances, subjects displayed faster process times, and the interviews concluded that the overall acceptance of the technology was high. Distinct differences in the time to fulfillment were measured using a test scenario derived from the real-world process. This paper proposes that bridging shortcomings in automation via the use of digital assistance systems improves not only the success rate of manual processes in Additive Manufacturing (AM) but also shortens training and supervision times. Furthermore, this paper shows that lightweight digital assistance solutions may be implemented without breaking process chains apart and without the requirement for specialized infrastructure.