Purpose: The purpose of this paper is to evaluate the existing scenarios for 3D printing (3DP) in order to identify the “white space” where future opportunities have not been proposed or developed to date. Based around aspects of order penetration points, geographical scope and type of manufacturing, these gaps are identified. Design/methodology/approach: A structured literature review has been carried out on both academic and trade publications. As of the end of May 2016, this identified 128 relevant articles containing 201 future scenarios. Coding these against aspects of existing manufacturing and supply chain theory has led to the development of a framework to identify “white space” in the existing thinking. Findings: The coding shows that existing future scenarios are particularly concentrated on job shop applications and pull-based supply chain processes, although there are fewer constraints on geographical scope. Five distinct areas of “white space” are proposed, reflecting various opportunities for future 3DP supply chain development. Research limitations/implications: Being a structured literature review, there are potentially articles not identified through the search criteria used. The nature of the findings is also dependent upon the coding criteria selected. However, these are theoretically derived and reflect important aspect of strategic supply chain management. Practical implications: Practitioners may wish to explore the development of business models within the “white space” areas. Originality/value: Currently, existing future 3DP scenarios are scattered over a wide, multi-disciplinary literature base. By providing a consolidated view of these scenarios, it is possible to identify gaps in current thinking. These gaps are multi-disciplinary in nature and represent opportunities for both academics and practitioners to exploit.

Research in metal matrix composites (MMCs) indicates that superior mechanical properties may be achieved by embedding reinforcement materials. However, the development of new composite powder for additive manufacturing requires an in-depth understanding of its key characteristics prior to its use in the fabrication process. This paper focuses on the low-energy ball milling (LEBM) of aluminium 2024 alloy (AA2024) reinforced with graphene nanoplatelets (GNPs). The main aim is to investigate the effect of the milling time (from 0.5 to 16 h) on the morphology and flowability of the powder. The study shows that, while short milling times (under 2 h) could not break the Van der WaRals forces between nanoparticles, GNPs were well separated and sufficiently covered the powder surface after 4 h of milling, thanks to the continuously applied impact energy. Longer milling time provides increasingly similar flowability results, confirmed by both the experimental work and discrete element model (DEM) simulations. Moreover, the ball milling process decreases the crystallite size of the milled powder by 24%, leading to a 3% higher microhardness. Lastly, the surface energy of the powder was determined as 1.4 mJ/m² by DEM, using the angle of repose of the as-received powder from experimental work.

In the production of cold-rolled strip products, strip breakage is one of the most common failures during the cold rolling process. However, the existing prediction models on strip breakage use the conventional sliding window algorithm to process the time series data collected from the actual production, resulting in a massive amount of non-informative data, which increases the computational cost for data-driven modelling. In order to tackle this issue, this article proposed a sliding window filter method to optimise the data pre-processing of the strip breakage. Firstly, based on the existing research and understanding of strip breakage, the data characteristics in the process of strip breakage was analysed. Based on the analysis, sample variance (VAR) and length normalised complexity estimate (LNCE) were chosen to determine how informative the time window was related to strip breakage. Secondly, compared with the conventional sliding window approach, the sliding windows were classified through a filter using VAR and LNCE. Thirdly, the filtered data was fed into the Recurrent Neural Network (RNN) for strip breakage modelling. An experimental study based on actual production data collected by a cold-rolled strip manufacturer was conducted to verify this method's effectiveness. The results show that pre-processing data using the sliding window filter decreases the model's computational cost.