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In this research, a recommendation system was designed for optimizing the injection molding process parameters. The system incorporates the utilization of process windows, eXtreme Gradient Boosting (XGBoost), and genetic algorithms. Computer-aided engineering (CAE) simulations were conducted to generate process window data and simulation data. Automatic hyperparameter optimization of the XGBoost was performed using grid search and cross-validation methods. The system employs 5 injection molding feature parameters as input and one product feature as output, and the strengthen elitist genetic algorithms (SEGA) was used for predicting the optimal injection molding process parameters. The performance of the prediction model was evaluated using an RMSE of 0.0202 and an R2 of 0.9826. The accuracy of the system was verified by conducting real production. The deviation of the product weight obtained from real production from the desired weight is 0.22%, which means that the prediction model achieves a correct rate of 99.78%. This recommendation system has a significant application value in reducing production costs and cycle time, as it can provide initial injection process parameter suggestions solely through the mold’s digital data.

As one of the most important applications of digitalization, intelligence, and service, the digital twin (DT) breaks through the constraints of time, space, cost, and security on physical entities, expands and optimizes the relevant functions of physical entities, and enhances their application value. This phenomenon has been widely studied in academia and industry. In this study, the concept and definition of DT, as utilized by scholars and researchers in various fields of industry, are summarized. The internal association between DT and related technologies is explained. The four stages of DT development history are identified. The fundamentals of the technology, evaluation indexes, and model frameworks are reviewed. Subsequently, a conceptual ternary model of DT based on time, space, and logic is proposed. The technology and application status of typical DT systems are described. Finally, the current technical challenges of DT technology are analyzed, and directions for future development are discussed.

The extra-ordinary properties of carbon fiber reinforced polymer (CFRP) composites and its outstanding performance in their structural applications, especially in aerospace industry where high strength to weight ratio is highly desirable, have given them a steadfast attention in the engineering materials research world. Drilling of CFRP materials to facilitate holes for component assembly subjects them to various modes of failure due to their anisotropy and in homogeneity. A requisite means of investigation and analysis is therefore required in order to understand these failure modes. Traditionally, the failure modes have been studied through experiments and more recently numerical simulations. There are dozens of finite element (FE) software available for numerical simulation with Abaqus/CAE being the mostly used for composites analysis. The numerical setup is a complicated process requiring some level of expertise in order to get accurate results which can be validated with experimental results. Upcoming researchers find it difficult, and they end up spending much time preparing for a successful simulation due to scattered information in scientific literature about modeling. This article tries to make work easier for new researchers by doing a review of 3D modeling of drilling CFRP using Abaqus/CAE software. The article features models from highly experienced researchers and their validated results with step-by-step setup guidelines. The objective is to provide a basic guideline to new researchers on the appropriate material properties and damage models for a successful and accurate simulation.

Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

The concepts of digital twins (DTs) have been widely studied to predict system performance, shorten design cycles, and implement preventive maintenance, but mainly, in large-scale enterprises. It is extremely beneficial to the whole manufacturing sector, since DTs can be readily implemented in small and medium-sized enterprises (SMEs) with basic computer aided engineering (CAE) tools; over 95% enterprises are SMEs. This paper aims to prove the feasibility of using commercial CAE tools, such as SolidWorks Simulation, to design air-quenching processes for SMEs. SMEs can benefit to explore new business opportunities, reduce system design cycle, and improve existing air-quenching processes. To our knowledge, it will be the first work of adopting DTs in conceptual design of an air-quenching process in sense that (1) the need of simulating an air-quenching process before physical implementation is discussed thoroughly; (2) heat transfer processes are classified, governing mathematical models for various heat transfer behaviors are introduced to present an evaluation model of a heat transfer process; (3) main process variables of air-quenching are identified; (4) a DT of an air-quenching process is developed and simulated to verify the capabilities of commercial SolidWorks Simulation; (5) case studies are developed to show how a CAE tool can be used in DTs. The findings from the reported work are summarized with a debrief of our future work.

Motivated by the suboptimal performances observed in existing compressed air energy storage (CAES) systems, this work focuses on the efficiency optimization of CAES through thermal energy storage (TES) integration. The research explores the dependence of CAES performance on power plant layout, charging time, discharging time, available power, and cavern volume. Hence, a range of solutions are examined, encompassing both solid and liquid TES options, alongside the potential utilization of external air heaters. Inefficiencies in solid TES due to significant retention of thermal power within the medium after complete discharge are identified and mitigated through optimization strategies. In addition, solutions to prevent ice formation at the low-pressure expander phase are suggested to avoid icing issues in CAES layouts with liquid TES. Through this comprehensive investigation, the study provides valuable insights into enhancing the efficiency and sustainability of CAES systems. By constructing a volume–power–time conversion table, the research contributes to the advancement of CAES technology, facilitating more efficient energy storage and utilization, thereby addressing critical challenges in the field of energy storage.

This study proposes a new metaheuristic algorithm called sand cat swarm optimization (SCSO) which mimics the sand cat behavior that tries to survive in nature. These cats are able to detect low frequencies below 2 kHz and also have an incredible ability to dig for prey. The proposed algorithm, inspired by these two features, consists of two main phases (search and attack). This algorithm controls the transitions in the exploration and exploitation phases in a balanced manner and performed well in finding good solutions with fewer parameters and operations. It is carried out by finding the direction and speed of the appropriate movements with the defined adaptive strategy. The SCSO algorithm is tested with 20 well-known along with modern 10 complex test functions of CEC2019 benchmark functions and the obtained results are also compared with famous metaheuristic algorithms. According to the results, the algorithm that found the best solution in 63.3% of the test functions is SCSO. Moreover, the SCSO algorithm is applied to seven challenging engineering design problems such as welded beam design, tension/compression spring design, pressure vessel design, piston lever, speed reducer design, three-bar truss design, and cantilever beam design. The obtained results show that the SCSO performs successfully on convergence rate and in locating all or most of the local/global optima and outperforms other compared methods.

Building information modelling (BIM) plays a prominent role in a good deal of architecture, engineering and construction (AEC) works, envisaging a full transition to digitalization for the construction industry. This is also due to a number of national and international regulations regarding the design, erection, and management of civil engineering constructions. For this reason, full interoperability of software environments such as computer-aided design (CAD) and computer-aided engineering (CAE) is a necessary requirement, particularly when the exchange of information comes from different disciplines. Users, throughout the years, have faced CAD–CAE interoperability issues despite following the IFC neutral open file format. This inability to share data (CAD to CAD, CAD to CAE) often generates model-interpretation problems as well as a lack of parametric information and a disconnection of elements. This paper addresses issues and mapping mechanisms in the exchange of data for the purpose of defining a baseline for the current status of bidirectional data exchange between AEC CAD/CAE software via the IFC format. A benchmark study, covering three years of software releases is illustrated; the assessment of the software performance was made with reference to criteria associated with the software’s level of suitability for use of the structural models. Four classes of performance, depending on the accuracy of the data transfer and on the associated corrective actions to be taken, were adopted. This confirmed that at the moment, the implementation of the IFC standard by software manufacturers is geared towards an expert class of users. Further efforts are needed in order to ensure its application is adopted by a wider class, thus extending and regulating its use by national, regional, and local authorities.

Bio-based polymers are a class of polymers made by living organisms, a few of them known and commercialized yet. Due to poor mechanical strength and economic constraints, they have not yet seen the extensive application. Instead, they have been an appropriate candidate for biological applications. Growing consumer knowledge of the environmental effect of polymers generated from petrochemical sources and a worldwide transition away from plastics with a lifespan of hundreds of years has resulted in greater interest in such hitherto unattainable sectors. Bio-based polymers come in various forms, including direct or “drop-in” replacements for their petrochemical counterparts with nearly identical properties or completely novel polymers that were previously unavailable, such as polylactide. Few of these bio-based polymers offer significantly improved technical specifications than their alternatives. Polylactic acid (PLA) has been well known in the last decade as a biodegradable thermoplastic source for use in 3DP by the “fused deposition modeling” method. The PLA market is anticipated to accomplish 5.2 billion US dollars in 2020 for its industrial usage. Conversely, 3DP is one of the emerging technologies with immense economic potential in numerous sectors where PLA is one of the critical options as the polymer source due to its environmentally friendly nature, glossiness, multicolor appearance, and ease of printing. The chemical structure, manufacturing techniques, standard features, and current market situation of PLA were examined in this study. This review looks at the process of 3DP that uses PLA filaments in extrusion-based 3DP technologies in particular. Several recent articles describing 3D-printed PLA items have been highlighted.

Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.