There has been significant development in metal additive manufacturing (MAM) technology over the past few decades, and considerable progress has been made in understanding how various processes and ...their parameters influence the properties of printed metallic parts. Despite this, the knowledge concerning its characteristics has been dispersed across a variety of publications and sources, making it difficult to gain a comprehensive understanding of the entire field, especially for businesses interested in additive manufacturing (AM). In order to bridge this gap, periodic reviews encompassing state-of-the-art as the whole are necessary. Therefore, this article provides a comprehensive overview of the essential features of MAM techniques based on the most recent scientific knowledge. It explores emerging research on four of the most significant technologies, including material extrusion (ME), binder jetting (BJ), powder bed fusion (PBF), and directed energy deposition (DED). As well as providing an outline of fundamental process characteristics, ongoing efforts to optimize them and current challenges, it also highlights gaps in understanding and future research and development needs. A significant feature of this review is the provision of substantial documentation regarding the mechanical properties of materials processed by a variety of commercial systems, including a variety of novel hybrid additive manufacturing (HAM) machines. This is accompanied by an investigation into the most recent works done to characterize the environmental impact along with a conceptual framework for improving the energy efficiency (EE) of the manufacturing process. As a result of reporting on both the characteristics of several MAM processes along with their sustainability features in one integrated article, it is anticipated that this information will serve as a valuable resource for both the academic and manufacturing communities to better appreciate and understand what differentiates MAM from traditional manufacturing (TM) processes, thus facilitating its future advancement and adoption.
Tool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, deep learning ...has been successfully applied to numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel deep learning data-driven modeling framework is presented, which includes a fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultraprecision machining. The proposed computational framework consists of two main structures. First, a training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features. Second, a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultraprecision machining case study with over 96% accuracy, while also outperforming comparable methodologies.
Manufacturing Process Selection Handbook provides engineers and designers with process knowledge and the essential technological and cost data to guide the selection of manufacturing processes early ...in the product development cycle. Building on content from the authors' earlier introductory Process Selection guide, this expanded handbook begins with the challenges and benefits of identifying manufacturing processes in the design phase and appropriate strategies for process selection. The bulk of the book is then dedicated to concise coverage of different manufacturing processes, providing a quick reference guide for easy comparison and informed decision making. For each process examined, the book considers key factors driving selection decisions, including: * Basic process descriptions with simple diagrams to illustrate * Notes on material suitability * Notes on available process variations * Economic considerations such as costs and production rates * Typical applications and product examples * Notes on design aspects and quality issues Providing a quick and effective reference for the informed selection of manufacturing processes with suitable characteristics and capabilities, Manufacturing Process Selection Handbook is intended to quickly develop or refresh your experience of selecting optimal processes and costing design alternatives in the context of concurrent engineering. It is an ideal reference for those working in mechanical design across a variety of industries and a valuable learning resource for advanced students undertaking design modules and projects as part of broader engineering programs. * Provides manufacturing process information maps (PRIMAs) provide detailed information on the characteristics and capabilities of 65 processes in a standard format * Includes process capability charts detailing the processing tolerance ranges for key material types * Offers detailed methods for estimating costs, both at the component and assembly level
Selected, peer reviewed papers from the 2014 3rd International Conference on Mechanical Design and Power Engineering (ICMDPE 2014), October 19, 2014, Jeju Island, Korea.
"Changeable and Reconfigurable Manufacturing Systems discusses key strategies for success in the changing manufacturing environment. Changes can often be anticipated but some go beyond the design ...range, requiring innovative change enablers and adaptation mechanisms. The book presents the new concept of Changeability as an umbrella framework that encompasses paradigms such as agility, adaptability, flexibility and reconfigurability. It provides the definitions and classification of key terms in this new field, and emphasizes the required physical/hard and logical/soft change enablers. The book presents cutting edge technologies and the latest research, as well as future directions to help manufacturers stay competitive. It contains original contributions and results from senior international experts, together with industrial applications. The book serves as a comprehensive reference for professional engineers, managers, and academics in manufacturing, industrial and mechanical engineering.
Industry 4.0 (I4.0) encompasses a plethora of digital technologies effecting on manufacturing enterprises. Most research on this topic examines the effects in the smart factory domain, focusing on ...production scheduling. However, there is still a lack of comprehensive research on the applications of I4.0 enabling technologies in manufacturing life-cycle processes. This paper is thus intended to provide a systematic literature review answering the following research question: What are the applications of I4.0 enabling technologies in the business processes of manufacturing companies? The study analyses 186 articles and the results show that production scheduling and control is the process most often investigated, while there is also an increasing trend in servitization and circular supply chain management. Moreover, there is extensive combined use of IoT, Big Data Analytics and Cloud, whose applications cover a wide range of processes. On the contrary, other technology like Blockchain is not as widely discussed in the domain of I4.0. This picture calls for a future research agenda extending the scope of investigation into I4.0 in manufacturing. Furthermore, the results of this research can prove extremely useful for practitioners who wish to implement one or more technologies, providing them with solutions for applications in manufacturing.
•Systematic review of machine learning (ML) in metal additive manufacturing.•Discussion of the shift from ML to physics-informed machine learning (PIML).•Discussion of the challenges of PIML in metal ...additive manufacturing.•Proposal of open questions to encourage future research.
Machine learning (ML) has shown to be an effective alternative to physical models for quality prediction and process optimization of metal additive manufacturing (AM). However, the inherent “black box” nature of ML techniques such as those represented by artificial neural networks has often presented a challenge to interpret ML outcomes in the framework of the complex thermodynamics that govern AM. While the practical benefits of ML provide an adequate justification, its utility as a reliable modeling tool is ultimately reliant on assured consistency with physical principles and model transparency. To facilitate the fundamental needs, physics-informed machine learning (PIML) has emerged as a hybrid machine learning paradigm that imbues ML models with physical domain knowledge such as thermomechanical laws and constraints. The distinguishing feature of PIML is the synergistic integration of data-driven methods that reflect system dynamics in real-time with the governing physics underlying AM. In this paper, the current state-of-the-art in metal AM is reviewed and opportunities for a paradigm shift to PIML are discussed, thereby identifying relevant future research directions.
PDF
