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This study aims to investigate the demoulding characteristics of material-jetted rapid mould inserts having different surface textures for micro-injection moulding using in-line measurements and surface metrology.

Material-jetted inserts with the negative cavity of a circular test product were fabricated using different surface finishes and printing configurations, including glossy, matte and vertical settings. In-line measurements included the recording of demoulding forces at 10 kHz, which was necessary to capture the highly-dynamic characteristics. A robust data processing algorithm was used to extract reliable demoulding energies per moulding run. Thermal imaging captured surface temperatures on the inserts after demoulding. Off-line measurements, including focus variation microscopy and scanning electron microscopy, compared surface textures after a total of 60 moulding runs.

A framework for capturing demoulding energies from material-jetted rapid tools was demonstrated and compared to the literature. Glossy surfaces resulted in significantly reduced demoulding forces compared to the industry standard steel moulds in the literature and their material-jetted counterparts. Minimal changes in the surface textures of the material-jetted inserts were found, which could potentially permit their prolonged usage. Significant correlations between surface temperatures and demoulding energies were demonstrated.

The research presented here addresses the very topical issue of demoulding characteristics of soft, rapid tools, which affect the quality of prototyped products and tool durability. This was done using state-of-the-art, high-speed sensing technologies in conjunction with surface metrology and their durability for the first time in the literature.

Open-access repositories (OARs) are essential for openly disseminating intellectual knowledge on the internet and providing free access to it. The current study aims to evaluate the growth and development of OARs in the field of technology by investigating several characteristics such as coverage, OA policies, software type, content type, yearly growth, repository type and geographic contribution.

The directory of OARs acts as the source for data harvesting, which provides a quality-assured list of OARs across the globe.

The study found that 125 nations contributed a total of 4,045 repositories in the field of research, with the USA leading the list with the most repositories. Maximum repositories were operated by institutions having multidisciplinary approaches. The DSpace and Eprints were the preferred software types for repositories. The preferred upload content by contributors was “research articles” and “electronic thesis and dissertations”.

The study is limited to the subject area technology as listed in OpenDOAR; therefore, the results may differ in other subject areas.

The work can benefit researchers across disciplines and, interested researchers can take this study as a base for evaluating online repositories. Moreover, policymakers and repository managers could also get benefitted from this study.

The study is the first of its kind, to the best of the authors’ knowledge, to investigate the repositories of subject technology in the open-access platform.

This study aims to elucidate the machining properties of low-cost expanded clay-reinforced syntactic foams by using different neural network models for the first time in the literature. The main goal of this endeavor is to create a casting machining-neural network modeling flow-line for real-time foam manufacturing in the industry.

Samples were manufactured via an industry-based die-casting technology. For the slot milling tests performed with different cutting speeds, depth of cut and lubrication conditions, a 3-axis computer numerical control (CNC) machine was used and the force data were collected through a digital dynamometer. These signals were used as input parameters in neural network modelings.

Among the algorithms, the scaled-conjugated-gradient (SCG) methodology was the weakest average results, whereas the Levenberg–Marquard (LM) approach was highly successful in foreseeing the cutting forces. As for the input variables, an increase in the depth of cut entailed the cutting forces, and this circumstance was more obvious at the higher cutting speeds.

The effect of milling parameters on the cutting forces of low-cost clay-filled metallic syntactics was examined, and the correct detection of these impacts is considerably prominent in this paper. On the other side, tool life and wear analyses can be studied in future investigations.

It was indicated that the milling forces of the clay-added AA7075 syntactic foams, depending on the cutting parameters, can be anticipated through artificial neural network modeling.

It is hoped that analyzing the influence of the cutting parameters using neural network models on the slot milling forces of metallic syntactic foams (MSFs) will be notably useful for research and development (R&D) researchers and design engineers.

This work is the first investigation that focuses on the estimation of slot milling forces of the expanded clay-added AA7075 syntactic foams by using different artificial neural network modeling approaches.