Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls

This paper aims to propose an online two-stage thermal history prediction method, which could be integrated into a metal AM process for performance control. Based on the similarity of temperature curves (curve segments of a temperature profile of one point) between any two successive layers, the first stage of the proposed method designs a layer-to-layer prediction model to estimate the temperature curves of the yet-to-print layer from measured temperatures of certain points on the previously printed layer. With measured/predicted temperature profiles of several points on the same layer, the second stage proposes a reduced order model (ROM) (intra-layer prediction model) to decompose and construct the temperature profiles of all points on the same layer, which could be used to build the temperature field of the entire layer. The training of ROM is performed with an extreme learning machine (ELM) for computational efficiency. Fifteen wire arc AM experiments and nine simulations are designed for thin walls with a fixed length and unidirectional printing of each layer. The test results indicate that the proposed prediction method could construct the thermal history of a yet-to-print layer within 0.1 seconds on a low-cost desktop computer. Meanwhile, the method has acceptable generalization capability in most cases from lower layers to higher layers in the same simulation, as well as from one simulation to a new simulation on different AM process parameters. More importantly, after fine-tuning the proposed method with limited experimental data, the relative errors of all predicted temperature profiles on a new experiment are smaller than 0.09, which demonstrates the applicability and generalization of the proposed two-stage thermal history prediction method in online applications for metal AM.Comment: 30 pages, 21 figures, 2 table

Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows. Graphical abstract: [Figure not available: see fulltext.].publishedVersionPeer reviewe

Machine learning-supported manufacturing: a review and directions for future research

ABSTRACTThe evolution of manufacturing systems toward Industry 4.0 and 5.0 paradigms has pushed the diffusion of Machine Learning (ML) in this field. As the number of articles using ML to support manufacturing functions is expanding tremendously, the main objective of this review article is to provide a comprehensive and updated overview of these applications. 114 journal articles have been collected, analysed, and classified in terms of supervision approaches, function, ML algorithm, data inputs and outputs, and application domain. The findings show the fragmentation of the field and that most of the ML-based systems address limited objectives. Some inputs and outputs of the analysed support tools are shared across the reviewed contributions, and their possible combinations have been outlined. The advantages, limitations, and research opportunities of ML support in manufacturing are discussed. The paper outlines that the excessive specialization of the reviewed applications could be overcome by increasing the diffusion of transfer learning in the manufacturing domain

Modeling of the Process Parameters Influencing Cold Metal Transfer (CMT): Development of an Approach Based in Causal Networks

The new Additive Manufacturing technologies combined with other transformations such as increasing digitalization and automation are creating new opportunities and associated challenges. For all the AM technologies, both functional and topological design of parts need to be completely reconsidered, for example, it becomes possible to integrate multiple functions into a single part. From the manufacturing process outlook, the links between process parameters and design requirements have to be unveiled. Discovering those links is a challenging process because of the existence of cross-impacts. The nature of the relationships is also probably highly nonlinear in some cases. Using a traditional design of experiment approach to discover those links might be time-consuming and the number of parameters to test might be enormous. This article applies an approach based on SI metrics combined with the functional representation of the manufacturing process to form causal-graphs. Those graphs are used in a preliminary phase to generate models of the interrelations between manufacturing and design parameters. Those models are also used to guide the experimental process and to minimize the amount of experiments to be conducted to validate the model. The approach is applied to the Cold Metal Transfer (CMT) technology currently in test in our laboratorypublishedVersio