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International trade is a long-standing issue for the development of any country. In the traditional theory of trade exports or trade pattern arises because of supply side differences between countries such as technological or factor endowment differences. Such theories predict inter-industry trade but not intra-industry trade (IIT). But in contrary, the simultaneous export and import of products of the same sector was led after the industrialization of developed countries from the 1960s onwards, which was described as “IIT” by [Balassa, B. (1986). Oxford Economic Papers, 38, 220–233]. In this study, India's bilateral IIT with major Asian trading partners was analyzed and the trends in IIT level for horizontal and vertical IIT were observed separately, along with gravity model for the year 2009–2018. This study examined the geographic component in knowledge flows, which could be found at the international level and whether or not an exchange of knowledge is related to foreign trade, particularly IIT. To measure the IIT level for investigating trade patterns between India and member countries of major Asian trading partners, IIT index will be used, known as Grubel-Lloyd index.

The purpose of this study is to improve the accuracy of a fused deposition modeling three-dimensional (3D) printer by identifying and compensating for position-independent geometric errors using a face-diagonal length test featuring a designed artifact and a Vernier caliper.

An artifact that does not require support when printing was designed and printed to allow performance of the face-diagonal length test. A Vernier caliper was used to measure the lengths of diagonals in the XY, YZ and ZX planes of the printed artifact specimen; this completed the face-diagonal length test. The relationships between position-independent geometric errors of the linear axes X, Y and Z and the measured diagonal lengths of the three planes were determined to identify geometric errors.

The approach was applied to a commercial fused deposition modeling 3D printer, and three position-independent geometric errors were rapidly identified. The artifact was re-printed after model-based compensation for these errors and the diagonal lengths were re-measured. The results were verified via coordinate measuring machine measurement of a simple test piece without and with model-based compensation for identified geometric errors. Furthermore, the proposed approach was applied to a commercial 3D printer.

The measured diagonal lengths of the printed artifacts varied greatly. Thus, further studies should investigate the effects of printing materials and parameters on the length discrepancies of 3D printed artifacts.

A software-based compensation of identified position-independent geometric errors has to be used at commercial 3D printers for accuracy improvements of printed parts.

Thus, the approach is of practical utility; it can be periodically used to identify position-independent geometric errors and ensure that the 3D printer is consistently accurate.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.