Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.

Digital Twin in Industry: State-of-the-Art

https://www.aeromech.usyd.edu.au/wwwcomp/pzferev.pdf

Advances in piezoelectric finite element modeling of adaptive structural elements: a survey

http://mdog.mcgill.ca/paper%20pdf%20to%20upload/Optimum%20stacking%20sequence%20design%20part1.pdf

Optimum stacking sequence design of composite materials Part I: Constant stiffness design

Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology. However, its broad adoption in industry is still hindered by high entry barriers of design for additive manufacturing (DfAM), limited materials library, various processing defects, and inconsistent product quality. In recent years, machine learning (ML) has gained increasing attention in AM due to its unprecedented performance in data tasks such as classification, regression and clustering. This article provides a comprehensive review on the state-of-the-art of ML applications in a variety of AM domains. In the DfAM, ML can be leveraged to output new high-performance metamaterials and optimized topological designs. In AM processing, contemporary ML algorithms can help to optimize process parameters, and conduct examination of powder spreading and in-process defect monitoring. On the production of AM, ML is able to assist practitioners in pre-manufacturing planning, and product quality assessment and control. Moreover, there has been an increasing concern about data security in AM as data breaches could occur with the aid of ML techniques. Lastly, it concludes with a section summarizing the main findings from the literature and providing perspectives on some selected interesting applications of ML in research and development of AM.

Machine learning in additive manufacturing: State-of-the-art and perspectives

Finite element modelling of piezolaminated smart structures for active vibration control with distributed sensors and actuators

Development of a novel vibration-based fatigue testing methodology

An integrated computational-experimental approach for prediction of total fatigue life applied to a uniaxial stress state is developed. The approach consists of the following elements: (1) development of a vibration based fatigue testing procedure to achieve low cost bending fatigue experiments and (2) development of a life prediction and estimation implementation scheme for calculating effective fatigue cycles. A series of fully reversed bending fatigue tests were carried out using a vibration-based testing procedure to investigate the effects of bending stress on fatigue limit. The results indicate that the fatigue limit for 6061-T6 aluminum is approximately 20% higher than the respective limit in fully reversed tension-compression (axial). To validate the experimental observations and further evaluate the possibility of prediction of fatigue life, an improved high cycle fatigue criterion has been developed, which allows one to systematically determine the fatigue life based on the amount of energy loss per fatigue cycle. A comparison between the prediction and the experimental results was conducted and shows that the criterion is capable of providing accurate fatigue life prediction.

Development of an Improved High Cycle Fatigue Criterion

A new modeling technique for piezoelectrically actuated beams

In this study, a novel topology optimization approach based on conditional Wasserstein generative adversarial networks (CWGAN) is developed to replicate the conventional topology optimization algorithms in an extremely computationally inexpensive way. CWGAN consists of a generator and a discriminator, both of which are deep convolutional neural networks (CNN). The limited samples of data, quasi-optimal planar structures, needed for training purposes are generated using the conventional topology optimization algorithms. With CWGANs, the topology optimization conditions can be set to a required value before generating samples. CWGAN truncates the global design space by introducing an equality constraint by the designer. The results are validated by generating an optimized planar structure using the conventional algorithms with the same settings. A proof of concept is presented which is known to be the first such illustration of fusion of CWGANs and topology optimization.

A Novel Topology Optimization Approach using Conditional Deep Learning.

A strain-energy-based method has been developed to predict the fatigue life of a structure subjected to either shear or biaxial bending loads at various stress ratios. The framework for this method is an advancement of previously conducted research that validates a uniaxial energy-based fatigue-life-prediction approach. The understanding behind the approach states that the total strain energy dissipated during a monotonic fracture and a cyclic process is the same material property, where the experimental strain-energy density of each can be determined by measuring the area underneath the monotonic true stress-strain curve and the area within a hysteresis loop, respectively. The developed framework consists of two elements: a life-prediction method that calculates shear fatigue-life cycles and a multi-axial life-prediction method capable of calculating biaxial fatigue-life cycles. A comparison was made between the two framework elements and experimental results from three different aluminum alloys. The comparison shows encouraging agreement, thus providing credence in the prediction capabilities of the proposed energy-based framework.

M.-H. Herman Shen

Papers

Development of a novel vibration-based fatigue testing methodology

Development of an Improved High Cycle Fatigue Criterion

A new modeling technique for piezoelectrically actuated beams

A Novel Topology Optimization Approach using Conditional Deep Learning.

Multi-Axial Fatigue-Life Prediction via a Strain-Energy Method