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The paper describes problems with the current additive manufacturing chain before considering additive manufacturing as part of a modern manufacturing chain. Additive manufacturing can be used for near net-shape for finishing, for repair or for adding special features which cannot be made with traditional manufacturing. This paper describes how STEP-NC deals with these different scenarios in terms of accuracy, multi-material and variation of slice direction. The possibilities of multi-material objects also raises questions about the design of such objects and how these need to be handled by an advanced controller. The paper also describes non-planar slicing. Curved direction and cylindrical direction are shown to improve the accuracy of curved structure additive manufacturing. STEP-NC using boundary representation has better capability of depicting complex internal structures for additive processes. By using exact model of the final product represented by STEP-NC, the paper demonstrates improvements in data size reduction, slicing accuracy, and precise manipulation of internal structure.
Jumyung Um; Joung Min Park; Ian Anthony Stroud. STEP-NC Based Squashing Slicing Algorithm for Multi-Material and Multi-Directional Additive Process. 2021, 1 .
AMA StyleJumyung Um, Joung Min Park, Ian Anthony Stroud. STEP-NC Based Squashing Slicing Algorithm for Multi-Material and Multi-Directional Additive Process. . 2021; ():1.
Chicago/Turabian StyleJumyung Um; Joung Min Park; Ian Anthony Stroud. 2021. "STEP-NC Based Squashing Slicing Algorithm for Multi-Material and Multi-Directional Additive Process." , no. : 1.
Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods.
Jumyung Um; Ian Anthony Stroud; Yong-Keun Park. Deep Learning Approach of Energy Estimation Model of Remote Laser Welding. Energies 2019, 12, 1799 .
AMA StyleJumyung Um, Ian Anthony Stroud, Yong-Keun Park. Deep Learning Approach of Energy Estimation Model of Remote Laser Welding. Energies. 2019; 12 (9):1799.
Chicago/Turabian StyleJumyung Um; Ian Anthony Stroud; Yong-Keun Park. 2019. "Deep Learning Approach of Energy Estimation Model of Remote Laser Welding." Energies 12, no. 9: 1799.
Substantial progress and development in additive manufacturing (AM) technologies have been realised during the last decade but have hardly been implemented on AM systems due to old numerical solutions still used by the AM digital thread, for instance STL (1987) and G-code (ISO 6963, 1982 ISO 6983-1. 1982. Numerical Control of Machines – Program Format and Definition of Address Words – Part1: Data Format for Positioning, Line Motion and Contouring Control Systems. International Standard Organization. [Google Scholar]). In this field, more traditional processes like machining have challenged this issue by adopting the Standard for the Exchange of Product model data compliant Numerical Control (STEP-NC) standard that enables advanced and intelligent manufacturing by taking advantage of the full computing performance of numerical controllers in manufacturing machines. This standard integrates the whole digital chain (CAD-CAM-CAPP-CNC-CMM) in a unique file with information on design and manufacturing and enables manufacturing of high-value products directly without numerical data conversion or post-processing. This study presents a new STEP-NC data model for AM technologies developed with the ISO TC184/SC1/WG7 committee. A STEP-NC platform initially developed for machining processes has been adapted to implement and validate the AM data model. It enables AM directly from a STEP-NC file, as well as hybrid manufacturing (AM and machining), and allows integration of several optimisation and simulation modules that extend the possibility of advanced and intelligent AM, for instance in-process manufacturing optimisation.
Renan Bonnard; Jean-Yves Hascoët; Pascal Mognol; Ian Stroud. STEP-NC digital thread for additive manufacturing: data model, implementation and validation. International Journal of Computer Integrated Manufacturing 2018, 31, 1141 -1160.
AMA StyleRenan Bonnard, Jean-Yves Hascoët, Pascal Mognol, Ian Stroud. STEP-NC digital thread for additive manufacturing: data model, implementation and validation. International Journal of Computer Integrated Manufacturing. 2018; 31 (11):1141-1160.
Chicago/Turabian StyleRenan Bonnard; Jean-Yves Hascoët; Pascal Mognol; Ian Stroud. 2018. "STEP-NC digital thread for additive manufacturing: data model, implementation and validation." International Journal of Computer Integrated Manufacturing 31, no. 11: 1141-1160.