This paper discusses the development of an experimental software prototype that uses surrogate models for predicting the monthly energy consumption of urban-scale community design scenarios in real time. The surrogate models were prepared by training artificial neural networks on datasets of urban form and monthly energy consumption values of all zip codes in San Diego county. The surrogate models were then used as the simulation engine of a generative urban design tool, which generates hypothetical communities in San Diego following the county's existing urban typologies and then estimates the monthly energy consumption value of each generated design option. This paper and developed software prototype is part of a larger research project that evaluates the energy performance of community microgrids via their urban spatial configurations. This prototype takes the first step in introducing a new set of tools for architects and urban designers with the goal of engaging them in the development process of community microgrids.

Cold rolling involves large deformation of the workpiece leading to temperature increase due to plastic deformation. This process is highly nonlinear and leads to large computation times to fully model the process. This paper describes the use of dimension-reduced neural networks (DR-NNs) for predicting temperature changes due to plastic deformation in a two-stage cold rolling process. The main objective of these models is to reduce computational demand, error, and uncertainty in predictions. Material properties, feed velocity, sheet dimensions, and friction models are introduced as inputs for the dimensionality reduction. Different linear and nonlinear dimensionality reduction methods reduce the input space to a smaller set of principal components. The principal components are fed as inputs to the neural networks for predicting the output temperature change. The DR-NNs are compared against a standalone neural network and show improvements in terms of lower computational time and prediction uncertainty.

Configuration assistants are tools for guiding the final user in simple configuration tasks, such as product assembling and customization or study plans generation. For their wide availability, web-based configuration assistants are valuable in fields such as electronic commerce and information services. We describe a general approach for building web-based configuration assistants: from a high-level description of the configuration constraints and of the basic items, given in a declarative language, the hypertext files for user guidance and the Java code for constraint checking are generated. We claim that the general approach of process-oriented configuration, where the user is guided through the configuration process by an explanatory hypertext, as opposed to product-oriented configuration, where one starts from a high-level description of the product of the configuration, is better suited for many application domains.

Self-organizing systems (SOS) are developed to perform complex tasks in unforeseen situations with adaptability. Predefining rules for self-organizing agents can be challenging, especially in tasks with high complexity and changing environments. Our previous work has introduced a multiagent reinforcement learning (RL) model as a design approach to solving the rule generation problem of SOS. A deep multiagent RL algorithm was devised to train agents to acquire the task and self-organizing knowledge. However, the simulation was based on one specific task environment. Sensitivity of SOS to reward functions and systematic evaluation of SOS designed with multiagent RL remain an issue. In this paper, we introduced a rotation reward function to regulate agent behaviors during training and tested different weights of such reward on SOS performance in two case studies: box-pushing and T-shape assembly. Additionally, we proposed three metrics to evaluate the SOS: learning stability, quality of learned knowledge, and scalability. Results show that depending on the type of tasks; designers may choose appropriate weights of rotation reward to obtain the full potential of agents’ learning capability. Good learning stability and quality of knowledge can be achieved with an optimal range of team sizes. Scaling up to larger team sizes has better performance than scaling downwards.

The sustainability concept is becoming ingrained in the international engineering community. The next generation of engineers has to be trained to appreciate economic, environmental, and societal impacts of its decisions, with an international perspective and at a local and global scale. The aim of this paper is to contribute to identifying the strengths and limits of current sustainability education practices to will be able to improve future decision-makers' and product designers' training. The first step of our research concerns the identification of knowledge, skills, attitudes, and values associated with sustainability and ecodesign to highlight what kind of engineers' competencies for sustainability have to be trained. Based on this identification, we examine the French education system from secondary school to university to study sustainability and ecodesign integration in different technological French curricula and practices. The objective is to estimate the effective place of sustainable development and ecodesign in the technological training courses. We analyze programs and complete our approach by observing teachers in their classrooms or by conducting interviews. Even if concepts, knowledge and skills related to ecodesign appear in many different programs, our study emphasizes that there is not a continuum between pre-secondary school, high school, and university. As a consequence, teachers have difficulties to understand the aims and the coherence of the program, and students are not really comfortable with the issues of sustainability and ecodesign. We propose to improve programs and practices by developing multidisciplinary curricula covering most of the sustainable issues and integrating best practices of international universities.

This article is an attempt to improve the efficiency of procedures for compositional synthesis of design solutions using building blocks. These procedures have found use in a wide range of applications, and are one of the most substantial outcomes of research into automated synthesis of design solutions. Due to their combinatorial nature, these procedures are highly inefficient in solving problems, especially when the database of building blocks for synthesis or the problem size is large. Previous literature often focuses on improving only the algorithm part of a procedure, while it is both its algorithm and database which together determine the overall efficiency of the procedure. This article uses a case study to investigate and develop a set of rules for structuring and preprocessing a database of building blocks so as to improve the efficiency of synthesis of design solutions using this database.

I present an implementation of shape grammars that is aimed at supporting designers. It has two parts: a grammar editor and a stand-alone interpreter. The editor is the modeling application Rhinoceros3d using Python scripts. The interpreter is general, is three-dimensional, and supports subshape detection. A grammar is a Rhinoceros3d model; thus users can manipulate all its parts directly and immediately. That is, they can modify any shape without selecting or invoking an editor, and they can lay out the parts of the grammar in any way they find meaningful. Using this approach, which I call a whole-grammar approach, users are shielded from most subdomain tasks, like typing text files or specifying transformations. Informal observations suggest that users of this implementation can work effectively.

Identifying failure paths and potentially hazardous scenarios resulting from component faults and interactions is a challenge in the early design process. The inherent complexity present in large engineered systems leads to nonobvious emergent behavior, which may result in unforeseen hazards. Current hazard analysis techniques focus on single hazards (fault trees), single faults (event trees), or lists of known hazards in the domain (hazard identification). Early in the design of a complex system, engineers may represent their system as a functional model. A function failure reasoning tool can then exhaustively simulate qualitative failure scenarios. Some scenarios can be identified as hazardous by hazard rules specified by the engineer, but the goal is to identify scenarios representing unknown hazards. The incidences of specific subgraphs in graph representations of known hazardous scenarios are used to train a classifier to distinguish hazard from nonhazard. The algorithm identifies the scenario most likely to be hazardous, and presents it to the engineer. After viewing the scenario and judging its safety, the engineer may have insight to produce additional hazard rules. The collaborative process of strategic presentation of scenarios by the computer and human judgment will identify previously unknown hazards. The feasibility of this methodology has been tested on a relatively simple functional model of an electrical power system with positive results. Related work applying function failure reasoning to a team of robotic rovers will provide data from a more complex system.

Special Issue Part 1 (Issue 3) and Part 2 (Issue 4) of AIEDAM are based on a workshop on Learning and Creativity held at the 2002 conference on Artificial Intelligence in Design, AID '02 (www.cad.strath.ac.uk/AID02_workshop/Workshop_webpage.html; Gero, 2002), the sixth of similar workshops, with the previous five focusing on Machine Learning in Design and being held at AIDs '92, '94, '96, '98, and '00 (Gero, 1992, 2000; Gero & Sudweeks, 1994, 1996, 1998). The first three workshops also resulted in special issues of AIEDAM (Maher et al., 1994; Duffy et al., 1996, 1998).

After 20 years of personal experience in introducing advanced software into several engineering contexts (engineering consulting firms, construction companies, large diversified companies, and commercial research establishments), some observations may be of interest. Although some development efforts succeeded and software is still in use, not one development effort has met the expectations that were expressed at the beginning of the project. The least successful experiences are design applications.

This paper defines, for use in design, rules for propagating “distribution constraints” through relationships such as algebraic or vector equations. Distribution constraints are predicate logic statements about the values that physical system parameters may assume. The propagation rules take into account “variation source causality”: information about when and how the values are assigned during the design, manufacturing, and operation of the system.

When developing a product, designers must decide what consumer variation will be addressed and how they will address it, because each consumer has a unique set of human factors, preferences, personal knowledge, and solution constraints. Numerous design methodologies exist to support the design of a product or set of products that address this consumer variation. However, currently there is little work supporting the selection of a design methodology, resulting in an ad hoc or a priori decision before conceptual design begins. This paper presents an affordance-based design method for use prior to conceptual design to help designers understand the consumer variation that is present. This facilitates the creation of a product or set of products that meets the demands of both the consumer(s) and the organization that is developing the product. Once consumer variation is understood, conceptual design can be performed with a more complete understanding of the overall problem.

The predictive methods of tool wear are usually based on different algorithm predictors, different source data, and different sensing devices for remaining useful life (RUL). In general, it has challenges to model and ensure all of the cutting conditions that are suitable in the actual cutting process for sustainable manufacturing. In order to overcome the doing large amount of experimental data and predict different tool RULs, this study combines the analytical force modeling, the back-propagation neural network (BPNN) machine learning, and the current sensor which all are integrated in smart machine box (SMB) to realize the practical RUL prediction for on-line and real-time applications. The analytical model of the cutting force coefficients of shear and ploughing was established from average cutting forces, it could reduce the experimental number and predict the different cutting conditions. In general, the loading current of the cutting tool from a spindle motor is relatively easier acquired than the resultant forces. Thus, the loading currents of the spindle are used to train and predict the cutting forces using the BPNN model during intelligent manufacturing. The SMB architecture mainly performed the autonomous actions based on the edge layer, the fog layer, and the cloud layer via the TCP/IP, the MQTT protocol, and the unified communication library. Results showed that a predictive error for the ends of the tool life is about 3–10% that are based on the calculating of the cumulative current ratio.

Reflective actions in collaborative design can potentially improve design performance and results. This paper quantitatively reexamines the relationships between reflective activities and design performance during the collaborative design process in terms of reflection in action. Twenty sets of protocol data were encoded by a modified version of Valkenburg and Dorst's coding scheme. Using statistical testing, the relationship between the design performance and the number of activities plus the transitions was examined. A significant statistical correlation was found between the percentage of mature framing (setting up of a desired goal with sufficient follow-ups) and the overall performance. These quantitative results verify the qualitative findings of the previous study.

The increase in Electrical and Electronic Equipment (EEE) usage in various sectors has given rise to repair and maintenance units. Disassembly of parts requires proper planning, which is done by the Disassembly Sequence Planning (DSP) process. Since the manual disassembly process has various time and labor restrictions, it requires proper planning. Effective disassembly planning methods can encourage the reuse and recycling sector, resulting in reduction of raw-materials mining. An efficient DSP can lower the time and cost consumption. To address the challenges in DSP, this research introduces an innovative framework based on Q-Learning (QL) within the domain of Reinforcement Learning (RL). Furthermore, an Enhanced Simulated Annealing (ESA) algorithm is introduced to improve the exploration and exploitation balance in the proposed RL framework. The proposed framework is extensively evaluated against state-of-the-art frameworks and benchmark algorithms using a diverse set of eight products as test cases. The findings reveal that the proposed framework outperforms benchmark algorithms and state-of-the-art frameworks in terms of time consumption, memory consumption, and solution optimality. Specifically, for complex large products, the proposed technique achieves a remarkable minimum reduction of 60% in time consumption and 30% in memory usage compared to other state-of-the-art techniques. Additionally, qualitative analysis demonstrates that the proposed approach generates sequences with high fitness values, indicating more stable and less time-consuming disassembles. The utilization of this framework allows for the realization of various real-world disassembly applications, thereby making a significant contribution to sustainable practices in EEE industries.

We are interested in investigating the different types of knowledge and mechanisms that cause design problem-solving to become routine. We use the model of Routine Parametric Design incorporated in the language DSPL (Brown & Chandrasekaran, 1989), where both the parameters to be given values and the design knowledge needed are known in advance.

Function-based design approaches have been criticized for being too narrow to properly guide design. Specifically, they are said to be unable to cope with nonfunctional considerations, such as cost or maintenance issues without invoking other concepts, such as constraints. This paper investigates two alternative conceptualizations of the design process: the practical affordance-based design approach, as elaborated by Maier and Fadel, and the more theoretical use plan approach by Houkes and Vermaas. This paper compares function-, affordance-, and use plan-based design approaches. It highlights strengths and weaknesses of each approach and proposes a definition of the function of an artifact in terms of its affordances.

This paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.

Generative product design systems used in the context of mass customization are required to generate diverse solutions quickly and reliably without necessitating modification or tuning during use. When such systems are employed to allow for the mass customization of product form, they must be able to handle mass production and engineering constraints that can be time-consuming to evaluate and difficult to fulfill. These issues are related to how the constraints are handled in the generative design system. This article evaluates two promising sequential constraint-handling techniques and the often used weighted sum technique with regard to convergence time, convergence rate, and diversity of the design solutions. The application used for this purpose was a design system aimed at generating a table with an advanced form: a Voronoi diagram based structure. The design problem was constrained in terms of production as well as stability, requiring a time-consuming finite element evaluation. Regarding convergence time and rate, one of the sequential constraint-handling techniques performed significantly better than the weighted sum technique. Nevertheless, the weighted sum technique presented respectable results and therefore remains a relevant technique. Regarding diversity, none of the techniques could generate diverse solutions in a single search run. In contrast, the solutions from different searches were always diverse. Solution diversity is thus gained at the cost of more runs, but no evaluation of the diversity of the solutions is needed. This result is important, because a diversity evaluation function would otherwise have to be developed for every new type of design. Efficient handling of complex constraints is an important step toward mass customization of nontrivial product forms.