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The main issue in the mass customization of apparel products is how to efficiently produce products of various sizes. A parametric pattern-making system is one of the notable ways to rectify this issue, but there is a lack of information on the parametric design itself and its application to the apparel industry. This study compares and analyzes three types of parametric clothing pattern CAD (P-CAD) software currently in use to identify the characteristics of each, and suggest a basic guideline for efficient and adaptable P-CAD software in the apparel industry.

This study compared three different types of P-CAD software with different characteristics: SuperALPHA: PLUS(as known as YUKA), GRAFIS and Seamly2D. The authors analyzed the types and management methodologies of each software, according to the three essential components that refer to previous studies about parametric design systems: entities, constraints and parameters.

The results demonstrated the advantages and disadvantages of methodology in terms of three essential components of each software. Based on the results, the authors proposed five strategies for P-CAD development that can be applied to the mass customization of clothing.

This study is meaningful in that it consolidates and organizes information about P-CAD software that has previously been scattered. The framework used in this study has an academic value suggesting guidelines to analyze P-CAD systems.

This paper aims to present a reverse engineering (RE) approach for three-dimensional (3D) model reconstruction and fast prototyping (FP) of broken chess pieces.

A case study involving a broken chess piece was selected to demonstrate the effectiveness of the proposed unconventional RE approach. Initially, a laser 3D scanner was used to acquire a (non-uniform rational B-spline) surface model of the object, which was then processed to develop a parametric computer aided design (CAD) model combined with geometric design and tolerancing (GD&T) technique for evaluation and then for FP of the part using a computer numerical controlled (CNC) machine.

The effectiveness of the proposed approach for reconstruction and FP of rotational parts was ascertained through a sample part. The study demonstrates non-contact data acquisition technologies such as 3D laser scanners together with RE systems can support to capture the entire part geometry that was broken/worn and developed quickly through the application of computer aided manufacturing principles and a CNC machine. The results indicate that design communication, customer involvement and FP can be efficiently accomplished by means of an integrated RE workflow combined with rapid product development tools and techniques.

This research established a RE approach for the acquisition of broken/worn part data and the development of parametric CAD models. Then, the developed 3D CAD model was inspected for accuracy by means of the GD&T approach and rapidly developed using a CNC machine. Further, the proposed RE led FP approach can provide solutions to similar industrial situations wherein agility in the product design and development process is necessary to produce physical samples and functional replacement parts for aging systems in a short turnaround time.

This study aims to automate the process of converting grading patterns into parametric patterns using artificial intelligence and to objectively evaluate the fitness of the converted patterns.

The developed system consists of a user interface that defines input data by importing multi-size grading patterns, an artificial neural network that learns the relationship between human body size and pattern geometry, and a module that converts training results into parametric patterns. In order to evaluate the fitness of the generated pattern, an objective fitting evaluation method using drape simulation was developed.

The body sizes of the wearer were input to the converted parametric pattern to generate a customized pattern. Resulting pattern showed a better fit than the grading pattern on the off-average body model.

In this study, a method has been developed that enables the users with minimal pattern drafting knowledge to convert grading patterns into parametric patterns using artificial intelligence and drape simulation. The human body's symmetry and the physical properties of fabric were not considered.

The system developed in this study requires less data compared to existing methods that attempt to design clothing patterns with machine learning. In addition, it was possible to evaluate pattern fitness on various body models through drape simulation based fit evaluation process for the first time.

The purpose of this research is to test the effectiveness of integrating Grasshopper 3D and measuring attractiveness by a categorical based evaluation technique (M-MACBETH) for building energy simulation analysis within a virtual environment. Set of energy retrofitting solutions is evaluated against performance-based criteria (energy consumption, weight and carbon footprint), and considering the preservation of the cultural value of the building, its architectural and spatial configuration.

This research addresses the building energy performance analysis before and after the design of retrofitting solutions in extreme climate environments (2030–2100). The proposed model integrates data obtained from an advanced parametric tool (Grasshopper) and a multi-criteria decision analysis (M-MACBETH) to score different energy retrofitting solutions against energy consumption, weight, carbon footprint and impact on architectural configuration. The proposed model is tested for predicting the performance of a traditional timber-framed dwelling in a historic parish in Lisbon. The performance of distinct solutions is compared in digitally simulated climate conditions (design scenarios) considering different criteria weights.

This study shows the importance of conducting building energy simulation linking physical and digital environments and then, identifying a set of evaluation criteria in the analysed context. Architects, environmental engineers and urban planners should use computational environment in the development design phase to identify design solutions and compare their expected impact on the building configuration and performance-based behaviour.

The unavailability of local weather data (EnergyPlus Weather File (EPW) file), the high time-resource effort, and the number/type of the energy retrofit measures tested in this research limit the scope of this study. In energy simulation procedures, the baseline generally covers a period of thirty, ten or five years. In this research, due to the fact that weather data is unavailable in the format required in the simulation process (.EPW file), the input data in the baseline is the average climatic data from EnergyPlus (2022). Additionally, this workflow is time-consuming due to the low interoperability of the software. Grasshopper requires a high-skilled analyst to obtain accurate results. To calculate the values for the energy consumption, i.e. the values of energy per day of simulation, all the values given per hour are manually summed. The values of weight are obtained by calculating the amount of material required (whose dimensions are provided by Grasshopper), while the amount of carbon footprint is calculated per kg of material. Then this set of data is introduced into M-MACBETH. Another relevant limitation is related to the techniques proposed for retrofitting this case study, all based on wood-fibre boards.

The proposed method for energy simulation and climate change adaptation can be applied to other historic buildings considering different evaluation criteria and context-based priorities.

Context-based adaptation measures of the built environment are necessary for the coming years due to the projected extreme temperature changes following the 2015 Paris Agreement and the 2030 Agenda. Built environments include historical sites that represent irreplaceable cultural legacies and factors of the community's identity to be preserved over time.

This study shows the importance of conducting building energy simulation using physical and digital environments. Computational environment should be used during the development design phase by architects, engineers and urban planners to rank design solutions against a set of performance criteria and compare the expected impact on the building configuration and performance-based behaviour. This study integrates Grasshopper 3D and M-MACBETH.

This paper aims to develop a new method of fuselage drag optimization that can obtain results faster than the conventional methods based on full computational fluid dynamics (CFD) calculations and can be used to improve the efficiency of preliminary design.

An efficient method for helicopter fuselage shape optimization based on surrogate-based optimization is presented. Two numerical simulation methods are applied in different stages of optimization according to their relative advantages. The fast panel method is used to calculate the sample data to save calculation time for a large number of sample points. The initial solution is obtained by combining the Kriging surrogate model and the multi-island genetic algorithm. Then, the accuracy of the solution is determined by using the infill criteria based on CFD corrections. A parametric model of the fuselage is established by several characteristic sections and guiding curves.

It is demonstrated that this method can greatly reduce the calculation time while ensuring a high accuracy in the XH-59A helicopter example. The drag coefficient of the optimized fuselage is reduced by 13.3%. Because of the use of different calculation methods for samples, this novel method reduces the total calculation time by almost fourfold compared with full CFD calculations.

To the best of the authors’ knowledge, this is the first study to provide a novel method of fuselage drag optimization by combining different numerical simulation methods. Some suggestions on fuselage shape optimization are given for the XH-59A example.

The paper aims to investigate the comfort-related performances of an innovative solar shading solution based on a new composite patented material that consists of a cement-based matrix coupled with a stretchable three-dimensional textile. The paper’s aim is, through a performance-based generative design approach, to develop a high-performance static shading system able to guarantee adequate daylit spaces, a connection with the outdoors and a glare-free environment in the view of a holistic and occupant-centric daylight assessment.

The paper describes the design and simulation process of a complex static shading system for digital manufacturing purposes. Initially, the optical material properties were characterized to calibrate radiance-based simulations. The developed models were then implemented in a multi-objective genetic optimization algorithm to improve the shading geometries, and their performance was assessed and compared with traditional external louvres and overhangs.

The system developed demonstrates, for a reference office space located in Milan (Italy), the potential of increasing useful daylight illuminance by 35% with a reduced glare of up to 70%–80% while providing better uniformity and connection with the outdoors as a result of a topological optimization of the shape and position of the openings.

The paper presents the innovative nature of a new composite material that, coupled with the proposed performance-based optimization process, enables the fabrication of optimized shading/cladding surfaces with complex geometries whose formability does not require ad hoc formworks, making the process fast and economic.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

This paper aims to propose a generative design method combined with meta-heuristic algorithm for automating and optimizing the floor layout of modular buildings using typical standardized module units, which are the room module, the corridor module and the stair module.

The integrated framework involves the generative design method and optimization for modular construction. The generative rules are provided by geometric relationships and functionalities of the module units. An evaluation function of the generated floor plans is also presented by the combination of project cost and cost penalties for the geometric features. The multi-population genetic algorithm (MPGA) method is provided for the optimization of the combination of costs.

The proposed MPGA method is demonstrated fast and efficient at discovering the globally optimal solution. The results indicate that when the unit price of modules is high, the transportation distance is long, or the land cost is high, the layout cost, which related to the symmetry, the compactness and the energy is tend to be lower, making the optimal layout economical.

This paper presented an integrated framework of generative floor layout and optimization for modular construction by using typical module units. It fulfills the need for automated layout generation with repetitive units and corresponding assessment during the early design stage.

Throughout an aircraft development process, the conceptual design phase is an extremely important milestone; hence, the quality and success of this step directly affect the overall cost and lead time of the project. Because of this fact, the purpose of this study is to provide outputs and suggestions to the designing engineer regarding the requirements for reducing overall design time as well as costs and creating an ideal design at the early phases of the project by optimizing the aircraft development process.

The system has been prepared parametrically and presents some performance specifications for the aircraft in the early phases of the design, for example, coefficients for lift C_L as well as drag C_D and weight as well as fuel estimations. The software uses a combination of well-known design techniques within just one platform in contrast to many other applications. Because of this feature, it is not needed to use different sub-platforms which would require an appropriate environment and even though would lead to complications with regard to the connectivity. The system also presents relevant information about the aircraft performance like velocity versus load factor (V-n) diagrams, maximum turn rate of climb, turn rate and climb angle graphs in contrast to many other open-source conceptual design platforms.

In this study, authentic General Dynamics F-16 Fighting Falcon and McDonnell Douglas F-15 Eagle data were used as input to the system, and advanced geometric and/or performance graphs were obtained and compared to the literature where a good agreement of the results was observed. These results with regard to the aircraft performance are typically product specific and quite rare in the literature. These data obtained by use of the software during the aircraft design are, thus, of major interest, especially for the design of new aerospace platforms. In this study, all of these graphs (especially the remarkable V-n diagram) are obtained on one platform.

The aircraft conceptual design and analysis system software provides information and suggestions regarding the requirements for reducing the overall design time, reducing the design costs and creating an optimized design at the early phases of a project by optimizing the aircraft development process within just one convenient, that is, user friendly, platform, where it uses a combination of varying methodologies. Besides presenting one interface, which is quite typical for conceptual design tools, it allows applications of methods like vortex lattices and finite differences for obtaining aerodynamic performance parameters.

This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health emergency, which intended to conduct reasonable layout design of hospital building to meet different performance requirements for both high efficiency during normal periods and low risk in the pandemic.

The research design follows a sequential mixed methodology. First, key points and parameters of hospital building layout design (HBLD) are analyzed. Then, to meet the requirements of high efficiency and low risk, adjacent preference score and infection risk coefficient are constructed as constraints. On this basis, automatic generative design is conducted to generate building layout schemes. Finally, multi-objective deviation analysis is carried out to obtain the optimal scheme of hospital building layouts.

Automatic generative design of building layouts that integrates adjacent preferences and infection risks enables hospitals to achieve rapid transitions between normal (high efficiency) and pandemic (low risk) periods, which can effectively respond to public health emergencies. The proposed approach has been verified in an actual project, which can help systematically explore the solution for better decision-making.

The form of building layouts is limited to rectangles, and future work can explore conducting irregular layouts into optimization for the framework of generative design.

The contribution of this paper is the developed approach that can quickly and effectively generate more hospital layout alternatives satisfying high operational efficiency and low infection risk by formulating space design rules, which is of great significance in response to public health emergency.