Computer-aided Process Research Articles

Integrated computer-aided molecular and process design: Green solvents for the hydroformylation of long-chain olefines

Integrated design of green solvents using an optimization-based hierarchical approach is investigated using a new method for an efficient solvent-design space reduction. Group-contribution methods for predicting solvent properties are used to identify additional viable solvent candidates in a neighborhood of solvents obtained by database screening. This neighborhood is quantified by introducing σ-moment domains, which rapidly and effectively reduces the molecular search-space. σ-moments are fingerprints of molecules based on quantum chemical calculations using the COSMO theory. To avoid suboptimal local minima during the process optimizations, a multistart approach is used. The methodology allows to identify solvent molecules that are optimal on a process wide level, and was successfully applied to the hydroformylation of olefines in a thermomorphic multiphase system including the recycling of a homogeneously dissolved catalyst.

Coupling Matrix Extraction of Microwave Filters by Using One-Dimensional Convolutional Autoencoders

The tuning of microwave filter is important and complex. Extracting coupling matrix from given S-parameters is a core task for filter tuning. In this article, one-dimensional convolutional autoencoders (1D-CAEs) are proposed to extract coupling matrix from S-parameters of narrow-band cavity filter and apply this method to the computer-aided tuning process. The training of 1D-CAE model consists of two steps. First, in the encoding part, one-dimensional convolutional neural network (1D-CNN) with several convolution layers and pooling layers is used to extract the coupling matrix from the S-parameters during the microwave filters’ tuning procedure. Second, in the decoding part, several full connection layers are employed to reconstruct the S-parameters to ensure the accuracy of extraction. The S-parameters obtained by measurement or simulation exist with phase shift, so the influence of phase shift must be removed. The efficiency of the presented method in this article is validated by a sixth-order cross-coupled filter simulation model tuning example.

Knowledge-Driven Manufacturing Process Innovation: A Case Study on Problem Solving in Micro-Turbine Machining.

Micromachining techniques have been applied widely to many industrial sectors, including aerospace, automotive, and precision instruments. However, due to their high-precision machining requirements, and the knowledge-intensive characteristics of miniaturized parts, complex manufacturing process problems often hinder production. To solve these problems, a systematic scheme for structured micromachining process problem solving and an innovation support system is required. This paper presents a knowledge-based holistic framework that enables process planners to achieve micromachining innovation design. By analyzing innovation design procedures and available knowledge sources, an open multi-source Machining Process Innovation Knowledge (MPIK) acquisition paradigm is presented, including knowledge units and a knowledge network. Further, a MPIK network-driven structured process problem-solving and heuristic innovation design method was explored. Subsequently, a knowledge-driven heuristic design system for machining process innovation was integrated in the Computer-Aided Process Innovation (CAPI) platform. Finally, a case study involving specific process problem-solving and innovation scheme design for micro-turbine machining was studied to validate the proposed approach.

Systematic Approach for Synthesizing Carbon–Hydrogen–Oxygen Networks Involving Detailed Process Simulations

Carbon–hydrogen–oxygen symbiotic networks (CHOSYNs) provide a systematic multiscale framework for integrating multiple hydrocarbon processing plants. Earlier approaches have focused on high-level performance benchmarking using atomic and stoichiometric targeting techniques, which are subsequently detailed to identify the implementation strategies for allocation and processing. These sequential approaches are very powerful for the cases involving benchmarks that are independent of the system details (e.g., minimum usage of raw materials, minimum discharge of wastes, maximum revenue). For objectives involving detailed process information (e.g., capital cost minimization), there is a need to include the system details early enough in optimization. This paper proposes a systematic approach for incorporating detailed process simulations throughout the various stages of synthesizing a CHOSYN. The proposed approach involves the use of computer-aided process simulators and simultaneous optimization for synthesizing a CHOSYN. Critical data are generated and used at each design stage. For existing facilities, targeting uses the simulated data on available resources and sinks (e.g., flow rate, composition, pressure, and temperature). Process synthesis is used to generate a set of candidate new units and plants to be added, while detailed simulation is used to size these plants and identify the specific needs for flows and compositions. The proposed approach is implemented for a case study that involves five existing plants and a set of seven candidate plants that can induce symbiosis. The results show final configurations with the selection of new plants, the reduction of raw material, the allocation of internal and external sources, and the details of process information, capital and operating costs, and flow rate, pressure, and temperature of the exchangeable streams across the network.

CHISA 2021 virtually – Worldwide Chemical Engineers Meeting

CHISA 2021 virtually – Worldwide Chemical Engineers Meeting You have just accessed a special issue of Chemical Engineering & Technology which is composed of contributions presented at the 24th International Congress of Chemical and Process Engineering CHISA held virtually on March 15–18, 2021 due to the pandemic of covid-19. Despite the difficult situation for both the organizers and the participants in regard to a new conference format, I feel that a nice virtual meeting of chemical engineers from all over the world was organized. However, it is clear that face-to-face discussions and meeting others in person cannot be easily replaced. The CHISA 2021 congress addressed the full spectrum of chemical and process engineering topics, such as reaction and catalysis engineering, separation processes, fluid flow and multiphase systems, computer-aided process engineering, and pharmaceutical engineering. Many interesting lectures and talks were presented, some of which are part of this special issue. The presented topics reflect the current focus of chemical engineering research on socially relevant subjects, such as decreasing environmental pollution. Studies on this topic deal with, e.g., introducing a new technology for efficient cleaning and heat recovery of flue gas from small-scale boilers, optimizing combustion processes via plasma activation while minimizing harmful emissions or removal of VOCs using an ionic liquid membrane. Another important topic is the transition from coal to green energy sources and the utilization of energy from waste; this is covered in studies on biomass gasification/combustion and fast pyrolysis of biomass and PE waste. Also included are studies of two-phase flow CFD modeling and image analysis of flow maldistribution during boiling in parallel minichannels and the utilization of catalytic processes to produce alkanes, biofuels, or synthesis gas. This is just a small sample of studies contained in this issue and I wish every reader to find a topic of interest. I want to thank the CHISA 2021 Scientific and Organizing Committees for providing an excellent platform and I hope that the next conference in 2022 will be in the usual format, allowing us again to meet in person in Prague. Prof. Dr.-Ing. Lucie Obalová Institute of Environmental Technology, CEET, VSB-Technical University of Ostrava Lucie Obalová

Machining sequence learning via inverse reinforcement learning

In recent years, process planning automation has been strongly promoted in conjunction with the development of information technology (IT). In manufacturing industries, machining sequencing is one of the elements of computer-aided process planning (CAPP) systems where it has a significant impact on the quality and cost of machined components. Therefore, effective and robust planning rules are essential for practical CAPP systems, and various metrics and constraints have been proposed to facilitate the creation of those rules. However, since it is challenging to address explicit factors such as interference between rules, processing difficulties, and manageability, discrepancies that require manual corrections often arise between the generated sequences and the planner's intentions. To resolve this problem, we propose a method of acquiring rules that reproduce the planner's decisions by inverse reinforcement learning (IRL). To apply the IRL process, we focus on identifying a machining sequence that characterizes the planner's decision based on past production processes and interviews with experts. This machining sequence can then be represented using a Markov decision process (MDP) when changing the workpiece shape, which enables the application of IRL. Additionally, to reflect the drawing information in the sequence decision, the workpiece shape is represented as a graph with attached tolerance and roughness values. The graphed machining sequence is then inputted to the graph, where convolutional networking and training are performed. We verified the validity of our proposed method using a small dataset.

In silico analysis of 3D QSAR and Molecular Docking studies to discover new thiadiazole-thiazolone derivatives as mitotic kinesin Eg5 inhibition

A series of twenty one thiadiazole-thiazolone derivatives which is a class of highly potent human mitotic kinesin Eg5 inhibitor reported from published article were studied through a series of computer-aided drug design processes such as three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) and Surflex-docking method using 17 compounds as training set. Both models showed good statistical quality and satisfying predictive ability (Q 2 = 0.617 and R 2 =0.919 for CoMFA ) and (Q 2 = 0.638 and R 2 =0.919 for CoMSIA ). The aim of this study was to explore 3D-QSAR approaches to propose new thiadiazole-thiazolone derivatives as Eg5 inhibitors for human mitotic kinesin. The CoMFA/CoMSIA contour maps were generated to provide the information about regions where the activity might be increased or decreased. Moreover, Based on the X-ray crystallized complex (PDB ID: 2UYM) molecular docking was performed on the most potent proposed Eg5 inhibitors using Surflex-dock method as an approach to investigate the stability of docked conformation and study the binding interactions in detail.

Research on the Application of Computer-aided Analysis System in the Concept of Visual Aesthetics in Graphic Design

The utilization of computer-aided analysis in graphic design mainly focuses on the design and testing of works, which helps to realize the effective use of visual aesthetics in graphic design works, and can maximize its effectiveness, so it has important research value. Based on this, this paper first analyzes the method of computer-aided graphic design, and then studies the parametric, modular and visual design in computer-aided graphic design, and finally gives the computer-aided analysis process of visual aesthetics of graphic design.

Integrated Computer-Aided Molecular and Process Design: Green Solvents for the Hydroformylation of Long-Chain Olefines

Integrated Computer-Aided Molecular and Process Design: Green Solvents for the Hydroformylation of Long-Chain Olefines

Integrated Computer-Aided Molecular and Process Design: Green Solvents for the Hydroformylation of Long-Chain Olefines

Integrated Computer-Aided Molecular and Process Design: Green Solvents for the Hydroformylation of Long-Chain Olefines

AFLN-DGCL: Adaptive Feature Learning Network with Difficulty-Guided Curriculum Learning for skin lesion segmentation

Background and problems:Automated skin lesion segmentation is a crucial step in the whole computer-aided (CAD) skin disease process. Recently, the fully convolutional network (FCN) has achieved outstanding performance on this task. However, it remains challenging because of three problems: (1) the difficult cases on dermoscopy images, including low contrast lesion, bubble and hair occlusion cases; (2) the overfitting problem of FCN-based methods that is caused by the imbalanced training of difficult samples and easy samples; (3) the over-segmentation problem of FCN-based methods. Method:This work proposes a new skin lesion segmentation framework. Specifically, feature representations from dermoscopy images are learned by the Adaptive Feature Learning Network (AFLN). An ensemble learning method is introduced to build a fusion model, enabling the AFLN model to capture the multi-scale information. We propose a Difficulty-Guided Curriculum Learning (DGCL) with step-wise training strategy to handle the overfitting problem caused by the imbalanced training. Finally, a Selecting-The-Biggest-Connected-Region (STBCR) is proposed to alleviate the over-segmentation problem of the fusion model. Experimental results:The method performance is compared using the same defined metrics (DICE, JAC, and ACC) with other state-of-the-art works on publicly available ISIC 2016, ISIC 2017, and ISIC 2018 databases, and results (0.931, 0.875, and 0.966), (0.881, 0.807, and 0.948), and (0.920, 0.856, and 0.966) illustrate its advantages. Conclusion:The excellent and robust performances on three public databases proved that our method has the potential to be applied to CAD skin diseases diagnosis.

COSMO-susCAMPD: Sustainable solvents from combining computer-aided molecular and process design with predictive life cycle assessment

Sustainable solvents are crucial for chemical processes and can be tailored to applications by Computer-Aided Molecular and Process Design (CAMPD). Recent CAMPD methods consider not only economics but also environmental hazards and impacts. However, holistic environmental assessment needs to address the complete life cycle of solvents. Here, we propose a CAMPD framework integrating Life Cycle Assessment (LCA) of solvents from cradle-to-grave: COSMO-susCAMPD. The framework builds on the COSMO-CAMPD method for predictive design of solvents using COSMO-RS and pinch-based process models. Cradle-to-grave LCA is enabled by combining predictive LCA from cradle-to-gate using an artificial neural network with gate-to-grave life cycle inventory data from the process models. The framework is applied to design solvents in a hybrid extraction-distillation process. The results highlight the need for cradle-to-grave LCA as objective function: Heuristics, economics, or cradle-to-gate LCA lead to suboptimal solvent choices. COSMO-susCAMPD thus enables the holistic environmental design of solvents using cradle-to-grave LCA.

Machining technology of shaft parts based on CAD technology

With the continuous development of manufacturing industry, the requirements of process design with system intelligence and informatization are higher and higher. Traditional manual technology can not meet the modern requirements, modern intelligent computer-aided process design is more and more widely used. The purpose of this study is to use CAD technology with process shaft technology. In this study, different radii of shaft components were selected and horizontal tensions of 600 kN and 1200 kN were applied to test their effects and efficiency. We compared the processing efficiency and accuracy of conventional manual processing technology and computer-assisted design. In this study, a parametric process planning template with constraint knowledge is established for each part group and stored in the process planning template library. A parametric process plan was derived using the 3d model and the feature parameters extracted from the specific manufacturing environment knowledge base. The results show that the amplitude frequency characteristic of the whole workpiece increases first and then decreases from the chuck to the tip, and the amplitude frequency characteristic amplitude is the largest at the distance of 3/4 from the chuck. The maximum value appears at 174.47Hz and the amplitude is 0.000427N/mm. Compared with manual machining, the machining time of CAD machining mode is reduced by 22.94%, the metal removal rate is increased by 34%, and the cutting efficiency is significantly improved. It is concluded that the computer-aided design technology in this study plays a more accurate and time-saving role in the processing of shaft parts and contributes to the intelligent part processing.

Research on Artificial Intelligence Interaction in Computer-Aided Arts and Crafts

Background. With the continuous maturity of computer software and hardware technology, the theory and method of computer-aided art design have developed rapidly. Objective. Applying artificial intelligence theory to computer-aided process art design is one of the newly developed research hotspots, and it is also the development trend of industrial design modernization. Methods. On the one hand, it can transplant the research results in the field of artificial intelligence into computer-aided art design, and on the other hand, it expands the application field of artificial intelligence, so that the two can be perfectly combined to promote common development. Results. With the development of artificial intelligence technology, computer art has gradually become a very active field, and a large number of computer art works are available every year. Conclusions. This paper briefly describes the basic concepts of computer-aided art design and artificial intelligence and discusses the application of artificial intelligence in computer-aided art design.

Automated synthesis of steady-state continuous processes using reinforcement learning

Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

Automation of the technology of connecting parts in the manufacture of aerospace products

The paper describes the improvement of the technology of manufacturing parts and components of aerospace production using computer-aided design and technological process control. The theoretical foundations and algorithms for constructing the technological process of manufacturing parts and components of the aerospace industry using various methods of joining heat-resistant materials, for example, by diffusion welding, are designed on the basis of theoretical and experimental studies proposed by the author of the patented connection method «Method for joining a heat-resistant cobalt-based alloy with silicon nitride-based ceramics» and technological equipment «Installation for obtaining metal-ceramic products», as well as «Attribute database for creating technological processes for obtaining parts of aerospace production by diffusion welding» and «Attribute database of technological equipment, tools and devices for mechanical processing of aerospace production parts», registered in the register of databases of the Russian Federation. The research is conducted at the Department of Mechanical Engineering Technology of the Institute of Mechanical Engineering and Mechatronics of the Siberian State University of Science and Technology named after academician M.F. Reshetnev.

Проектирование мехатронных технологических комплексов при цифровизации традиционного и аддитивного производства

The paper presents the analysis of the steps and stages of designing process equipment for traditional manufacturing. The features of building process equipment using energy flows and consumables are studied. Structural synthesis of mechatronic systems in digitalized manufacturing make it possible to add new stages to the process of creating process equipment for both traditional automated subtractive and new additive manufacturing. The processes of manufacturing parts without forming equipment described by the algorithms according to the proposed structural diagram of connections provide an opportunity to analyze the existing equipment and develop new equipment for laminate synthesis of products. The paper illustrates the use of methods and procedures for laminate synthesis and fabrication of parts from composite materials using process equipment based on the application of energy flows and material components for new additive and traditional subtractive manufacturing. Also, methods and diagrams for automation and computer-aided process control over product manufacturing are shown.

A review of G code, STEP, STEP-NC, and open architecture control technologies based embedded CNC systems

Computer Numeric Control (CNC) is a significant pillar of manufacturing for decades. These systems are an essential part of future manufacturing. In the urge towards the development of next-generation manufacturing systems, various technologies have been introduced. Moreover, based on those technologies, numerous systems have been developed all around the world. In order to assist in future research, the related major technologies and developments are discussed in this content. This paper presents the review of basics CN), Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), Computer-Aided Manufacturing (CAM), G codes (ISO 6983), Standard for the Exchange of Product (STEP) model data (STEP), STEP-numeric control (STEP-NC), and open architecture control (OAC) technologies. The in-depth discussion on the development and implementation of the STEP and STEP-NC technologies is also addressed. The review also highlights the benefits and role of the open architecture control technology in the CNC system development. Apart from that, the paper also presents the last 17 years of embedded CNC systems development in tabular form, which shows the cocktail of various technologies and ISO data interface models. Finally, the paper concludes with future directions and recommendations.

Electroplating and Ablative Laser Structuring of Elastomer Composites for Stretchable Multi-Layer and Multi-Material Electronic and Sensor Systems.

In this work we present the concept of electroplated conductive elastomers and ablative multi-layer and multi-material laser-assisted manufacturing to enable a largely automated, computer-aided manufacturing process of stretchable electronics and sensors. Therefore, the layers (conductive and non-conductive elastomers as well as metal layers for contacting) are first coated over the entire surface (doctor blade coating and electroplating) and then selectively removed with a CO2 or a fiber laser. These steps are repeated several times to achieve a multi-layer-structured design. Is it not only possible to adjust and improve the work previously carried out manually, but also completely new concepts such as fine through-plating between the layers to enable much more compact structures become possible. In addition, metallized areas allow the direct soldering of electronic components and thus a direct connection between conventional and stretchable electronics. As an exemplary application, we have used the process for manufacturing a thin and surface solderable pressure sensor with a silicone foam dielectric and a stretchable circuit board.

A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning

Computer-aided process planning represents the main link between computer-aided design and computer-aided manufacturing. One of the crucial tasks in computer-aided process planning is an operation sequencing problem. In order to find the optimal process plan, operation sequencing problem is formulated as an NP hard combinatorial problem. To solve this problem, a novel genetic crow search approach (GCSA) is proposed in this paper. The traditional CSA is improved by employing genetic strategies such as tournament selection, three-string crossover, shift and resource mutation. Moreover, adaptive crossover and mutation probability coefficients were introduced to improve local and global search abilities of the GCSA. Operation precedence graph is adopted to represent precedence relationships among features and vector representation is used to manipulate the data in the Matlab environment. A new nearest mechanism strategy is added to ensure that elements of machines, tools and tool approach direction (TAD) vectors are integer values. Repair strategy to handle precedence constraints is adopted after initialization and shift mutation steps. Minimization of total production cost is used as the optimization criterion to evaluate process plans. To verify the performance of the GCSA, two case studies with different dimensions are carried out and comparisons with traditional and some modern algorithms from the literature are discussed. The results show that the GCSA performs well for operation sequencing problem in computer-aided process planning.