An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

While polymer additive manufacturing (AM) has advanced significantly over the past few decades, the limitations in material properties, speed of manufacture, and part size have relegated this technology to the space of rapid prototyping rather than the legitimate manufacture of end-use parts. Carbon fiber offers a low density, a low coefficient of thermal expansion, and high thermal conductivity and is an ideal material for bringing polymer-based AM from the realm of form and fit to that of form, fit, and function. Use of carbon fiber in AM can improve material properties, reduce the time required to manufacture functional parts compared with traditional subtractive technologies, and reduce warping, thereby enabling a larger possible build envelope. Therefore, the addition of carbon fiber to various AM technologies is of increasing interest in academic and industrial communities. This paper examines the work performed in this fast-growing area to date. Specifically, the effects of fiber reinforcement on the structure and mechanical properties of 3D printed parts are investigated within the body of literature. Upper bounds for tensile properties of carbon fiber composites are theoretically evaluated and compared with experimentally measured values. Moreover, current and potential applications of additively manufactured carbon fiber composites in the context of desktop 3D printing and big area AM are discussed. Recent innovations and industry breakthroughs in this field are also examined. This review is intended to organize and synthesize the present body of work surrounding AM of carbon fiber reinforced plastics, identify the most promising technologies, and prescribe viable research and development paths forward to advance AM from the application space of rapid prototyping to that of functional, load-bearing, end-use parts.

Additively manufactured carbon fiber-reinforced composites: State of the art and perspective

Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection

Automated fiber placement (AFP) is a composite manufacturing technique used to fabricate complex advanced air vehicle structures that are lightweight with superior qualities. The AFP process is intricate and complex with various phases of design, process planning, manufacturing, and inspection. An understanding of each of these phases is necessary to achieve the highest possible manufacturing quality. This literature review aims to summarize the entire AFP process from the design of the structure through inspection of the manufactured part to generate an overall understanding of the lifecycle of AFP manufacturing. The review culminates with highlighting the challenges and future directions for AFP with the goal of achieving a closed loop AFP process.

Automated fiber placement: A review of history, current technologies, and future paths forward

Engineering and manufacturing processes and systems designs involve many challenges, such as dynamism, chaotic behaviours, and complexity. Of late, the arrival of big data, high computational speed, cloud computing and artificial intelligence techniques (like machine learning and deep learning) has reformed how many engineering and manufacturing professionals approach their work. These technologies offer thrilling innovative ways for engineers and manufacturers to tackle real-life challenges. On the other hand, the field of Artificial Intelligence (AI) is extensive. Several diverse theories, algorithms, and methods are available, which presents a challenge and a barrier in choosing the right AI technique for the appropriate engineering process or manufacturing process and environments. Besides, the pertinent literature is disseminated over various journals, conference proceedings, and research communities. Hence, conducting a systematic survey to scrutinise and classify the existing literature is worthwhile. However, it is challenging, but previous review studies have not adequately addressed AI’s use and advancement in engineering and manufacturing (EM). Besides, some concentrated on single AI models, and others focused on a specific area in EM. This paper presents a comprehensive systematic review of studies on AI and its application in EM. To limit the scope of the current study, we conducted a keyword search in official publisher websites and academic databases, such as Springer, Elsevier, Scopus, Science Publication, Taylor & Francis, Directory of Open Access Journals (DOAJ), Association for Computing Machinery (ACM), Wiley online library, Inderscience and Google scholar. The search results (173 articles) were filtered according to a proposed framework, which resulted in ninety-one (91) relevant research articles. We reviewed the articles based on a proposed taxonomy (the year of publication, the AI algorithm and machine learning task adopted, the application area in EM, the train and test split of data, the error, and accuracy metrics used, the potential benefits). Our assessment using the proposed taxonomy gave a helpful insight into the literature’s anatomy on various AI applications in engineering and manufacturing. Also, we identified opportunities for future research in AI application in the field of EM.

Applications of artificial intelligence in engineering and manufacturing: a systematic review

Automated fiber placement (AFP) provides precision and repeatable manufacturing of both simple and complex geometry composite parts. However, AFP also introduces the possibility for unique flaws such as overlapping tows, gaps between tows, tow twists, lack of layer adhesion and foreign object debris. These types of flaws can all result in a significant loss of performance in the final part. The current inspection method for these flaws is a costly and time intensive visual inspection of each ply layer. This work describes some initial efforts to incorporate thermal inspection on the AFP head and analysis of the data to identify the previously mentioned flaws. Previous bench-top laboratory experiments demonstrated that laps, gaps, and twists were identified from a thermal image. The AFP head uses an on- board lamp to preheat the surface of the part during layup to increase ply consolidation. The preheated surface is used as a thermal source to observe the state of the new material after compaction. We will present data collected with the Integrated Structural Assembly of Advanced Composites (ISAAC) AFP machine at Langley Research Center showing that changes to the temperature profile is sufficient for identifying all types of flaws.

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In-situ thermography of automated fiber placement parts

The advent of Automated Fiber Placement (AFP) systems have aided the rapid manufacturing of composite aerospace structures. One of the challenges that AFP systems bring is the variability of the deposited prepreg tape layers, which are prone to gaps, overlaps and twists. The current detection method used in industry involves halting fabrication and performing a time consuming visual inspection of each tape layer. Typical AFP systems use a quartz lamp to heat the base layer to make the surface tacky as it deposits another tape layer. The innovation discussed in this paper is to use the preheated base layer as a through transmission heat source and inspect the newly added tape layer in situ using a thermographic camera mounted onto the AFP structure. Such a system would not only increase manufacturing throughput by reducing inspection times, but would also aid in process development for new structural designs or material systems. To this end, a small thermal camera was mounted onto an AFP robotic research platform at NASA, and thermal data was collected during typical and experimental layup operations. The data was post processed to reveal defects such as tow overlap/gap, wrinkling, and peel-up. Defects that would have been impossible to detect visually were also revealed in the data, such as poor/loss of adhesion between plies and the effects of vacuum debulking. This paper will cover the results of our experiments, and the recent progress on the data reduction algorithms in preparation for machine learning development.

In situ thermal inspection of automated fiber placement manufacturing

Passive thermography has been shown to be an effective method for in situ and real time nondestructive evaluation (NDE) to measure damage growth in a composite structure during cyclic loading. The heat generation by subsurface flaw results in a measurable thermal profile at the surface. This paper models the heat generation as a planar subsurface source and calculates the resultant temperature profile at the surface using a three dimensional quadrupole. The results of the model are compared to finite difference simulations of the same planar sources.

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Application of the quadrupole method for simulation of passive thermography

This paper presents data from an innovative nondestructive evaluation (NDE) method for automated composite fiber placement fabrication. Using Infrared images of the fiber, as it was being placed, we are able to provide valuable information about the quality of the part during fabrication. Herein, we discuss the methodology for data collection and processing. The described in situ thermal NDE process is found to be applicable for identifying fiber tow overlaps, gaps, twists, puckering, and poor ply adhesion prior to cure, thereby reducing the time and cost associated with post cure flaw repair or scrapping parts. This paper also describes the process of assembling data sets for an entire part beyond simple frame by frame analysis. Example data sets for both a flat part and a larger cylindrical part are presented to demonstrate the type of defect characterization information that can be obtained.

Elizabeth D. Gregory

Papers

Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection

In-situ thermography of automated fiber placement parts

In situ thermal inspection of automated fiber placement manufacturing

Application of the quadrupole method for simulation of passive thermography

In situ Thermal Nondestructive Evaluation for Assessing Part Quality During Composite Automated Fiber Placement