Early Design Phase Research Articles

A prioritized decision making method for reliability allocation: In optimistic and pessimistic view

Reliability allocation is a very important problem during early design and development phases of a system. There are several reliability allocation techniques which are used to achieve the target reliability. The feasibility of objectives (FOO) technique is one of them that is widely used to perform system reliability allocation. But this technique has two fundamental shortcomings. The first is the measurement scale and the second is that it does not consider the order weight of the reliability allocation factors. The prioritization of the factors is also an important topic in decision making. Practically, all factors in multi-criteria decision making (MCDM) are not in the same priority level. Hence, in decision making situation, it is usual for decision makers to consider different priority factors. So, considering the prioritization of the factors, a reliability allocation method is proposed here to overcome the shortcomings of the FOO technique. Also, a case study on reliability allocation in airborne radar system is considered here to verify the efficiency of the proposed approach. Finally, the results are calculated in different optimistic and pessimistic view point and compared with the FOO technique. This comparison exhibits the advantages and supremacy of the proposed approach.

Proposing an integrated indicator to measure product repairability

Repairing is one of the most relevant strategies within the Circular Economy (CE) concept since it contributes to waste prevention and extends product and components' lifespan. Thus, reparability becomes an essential issue from the early product design phases, where materials, geometries, and joints are defined. Despite some repairability indicators that can be found in the literature and are applied worldwide, there is a lack of connection between repairability and the early decision-making process for improving it from the design of components of subsystems of a product. To contribute in that research direction, this article presents the Product Repairability Index (PRI), which considers the intrinsic repairability of the product components, their assembly/disassembly complexity, repairing instructions, availability of spare parts, and the self-diagnosis aids provided by the product. The PRI also considers components' relative functional importance to identify those with higher repairability requirements concerning their functional importance in the whole product assembly. The proposed indicator has been applied to a coffee machine as a case study, following a step-to-step methodology and calculation criteria to generate a quantitative value and detect the possible aspects to redesign to make a product more repairable.

A new approach for evaluating experienced assembly complexity based on Multi Expert-Multi Criteria Decision Making method

In manufacturing, complexity is considered a key aspect that should be managed from the early phases of product and system design to improve performance, including productivity, efficiency, quality, and costs. The identification of suitable methods to assess complexity has always been of interest to researchers and practitioners. As complexity is affected by several aspects of different nature, it can be assessed from objective or subjective viewpoints or a combination of both. To assess experienced complexity, the analysis relies on the subjective evaluations given by practitioners, usually expressed on nominal or ordinal scales. However, methods found in the literature often violate the properties of the scales, potentially leading to bias in the results. This paper proposes a methodology based on the analysis of categorical data using the multi expert-multi criteria decision making method. A number of criteria are adopted to assess assembly complexity and, from subjective evaluations of operators, product assembly complexity is assessed at an individual level and then, aggregating results, at a global level. A comparison between experienced complexity and an objective assessment of complexity is also performed, highlighting similarities and differences. The assessment of experienced complexity is much more straightforward and less demanding than objective assessments. However, this study showed that it is preferable to use objective assessments for highly complex products as individuals do not discriminate between different complexity levels. An experimental campaign is conducted regarding a manual assembly of ball-and-stick products to show the applicability of the methodology and discuss the results.

Liability Factors and Conceptual Framework for Contracts to Manage Design for Digital Fabrication in Construction Projects

The adoption of digital fabrication—fabrication based on digital design—in the early design phase in projects requires a thorough understanding of the liability factors to design the contract. This paper addresses this issue using a two-stage research approach. First, a case study research maps the process from digital design to digital fabrication in an existing project that adopted digital fabrication using the design-bid-build model. Second, a three-round Delphi survey of 14 stakeholders of that project identifies and ranks 163 liability factors under eight categories: actors, resources, conditions, attributes, processes, artifacts, values, and risks. The resources of management capability and building information modeling (BIM) expertise rank as the two most important liability factors. Building on these findings, the paper presents a conceptual framework for contract design and discusses how the existing project delivery models—design-bid-build, construction management, design-build, and integrated project delivery (IPD)—can consider the liability factors in contracts.

Conceptual aero-structural design of a fan stage under distorted flow

Rotor blades experience unsteady forces and aerodynamic losses when operating in a distorted inflow. The structural analysis of rotors under these conditions, therefore, is of prime importance during the early design phase. This work introduces a computationally efficient design framework that links the aerodynamic design and structural analysis of the rotor. Rotor structural analysis comprises computing dynamic stresses, the excitation frequencies, and resonance margins at various modes of vibration. A design space encapsulating fan stage design variables, aerodynamic performance, and rotor structural constraints is explored and optimized for maximum fan stage efficiency subject to aerodynamic and structural constraints.

Object Shape Error Modelling and Simulation During Early Design Phase by Morphing Gaussian Random Fields

Geometric and dimensional variations in objects are caused by inevitable uncertainties in manufacturing processes and often lead to product quality challenges. Failing to model the effect of object shape errors, i.e., geometric and dimensional errors of parts, early during the design phase inhibits the ability to predict such quality challenges. This consequently leads to expensive design changes after freezing of design. State-of-art methodologies for modelling and simulating object shape error have limited defect fidelity, data versatility, and designer centricity that prevent their effective application during the early design phase. To overcome these limitations, this paper presents a novel Morphing Gaussian Random Field (MGRF) methodology for object shape error modelling and simulation. The MGRF methodology models the spatial correlation in the deviations of the part from its nominal design using Gaussian Random Fields and then, utilises the modelled spatial correlations to generate non-ideal parts by conditional simulations. The MGRF methodology has (i) high defect fidelity enabling it to simulate various part defects including local and global deformations, and technological patterns; (ii) high data versatility allowing it to simulate non-ideal parts under the constraint of limited data availability and to utilise historical non-ideal part data of similar parts; (iii) designer centric capabilities such as performing ‘what if?’ analysis of defects of practical importance; and; (iv) the ability to generate non-ideal parts conforming to statistical form tolerance specification without additional modelling effort. The aforementioned characteristics enable the MGRF methodology to accurately model and simulate the effect of object shape variations on product quality during the early design phase. Practical applications of the developed MGRF methodology and its advantages are demonstrated using sport-utility-vehicle door parts and compared against state-of-art methodologies.

Robust model for tunnel squeezing using Bayesian optimized classifiers with partially missing database

Accurately predicting and estimating the squeezing and ground response to tunneling remains challenging. Moreover, tunnel-squeezing hazards are much more likely to occur in deeply buried long tunnels with complex engineering-geological environments. Therefore, a high-performance predictive model for tunnel squeezing is necessary. A superior ensemble classifier is put forward in this study, which is composed of four individual classifiers (gradient boosting classifier, extra-trees classifier, AdaBoost classifier, and Logistic regression classifier) and two optimization algorithms (Bayesian optimization (BO) and sparrow search algorithm (SSA)). The training database covers five parameters: tunnel depth (H), rock tunneling quality index (Q), tunnel diameter (D), support stiffness (K), and strength stress ratio (SSR), about which the basic information is accessible at the early design phases. However, the dataset compiled from the literature is insufficient. Thus, the ten proposed methods are used to replace the missing values. During the model training process, BO shows its strong ability to optimize seventeen hyperparameters. When applied to tune the classifiers' weights, SSA achieves a fast and efficient performance. The novel Shapley Additive Explanations–LightGBM method indicates that the K is the most important input feature, followed by SSR, Q, H, and D, respectively. The ensemble classifier is then validated using the test set and additional historical case projects. The validation shows that the model can achieve an accuracy of 98% (i.e., the error rate is 2%) on the test set, higher than those achieved by previous prediction models. Moreover, the predicted probability could provide warning information for timely support measures. Finally, the application results are illustrated through tests on the tunnel sections that have not yet been excavated in the line of the Sichuan–Tibet railway project. The applied predictive tendencies and laws are in line with the practical experience. In summary, the proposed model's prediction results are reasonable, and its prediction will be more accurate as more data is collected and trained for prewarning the tunnel squeezing hazard.

An early-phase design process to enable long-term flexibility in assembly systems

Assembly systems require to be designed considering flexibility from a holistic perspective to produce the variety of current and future product generations. Reactive ad hoc changes after realizing both the product and assembly system designs require considerable effort and may even be impossible. A systematic collaborative approach that concurrently considers the early phases of product and assembly system development appears to be essential for working with long-term changes. To this end, a greater understanding of the working procedures and design activities concerning flexibility is required. In this paper, this is investigated based on a theoretical framework and a multiple case study at a world-leading manufacturer of heavy-duty vehicles. As a result, a developed early phase design process to enable long-term flexibility in assembly systems is presented. The theoretical implications provided and the findings are also relevant to those involved in the design process of flexible assembly systems.

Erweitertes Vorgehensmodell für intentionsbasiertes Engineering und Automatisierung modularer verfahrenstechnischer Anlagen

Abstract The modularization of process plants and their modular automation represent innovative concepts to meet the increasing demands for flexibility in plant operation. However, existing approaches to support the engineering of such plants do not focus on the early phases of requirements elicitation and concept design, although these early phases are crucial for the economic efficiency of the later plant. This paper presents a process model for the formalized description of intentions in the early engineering phases of modular process plants. The plant operator creates a formalized model of his intentions and submits it to the module manufacturers in a suitably modularized form. Thus, in the engineering phases, a reduction of effort and an increase in support in the planning and development of the plants are achieved. Furthermore, purpose-specific, required automation functionalities can be defined at an early stage.

Deep Learning Methods of Cross-Modal Tasks for Conceptual Design of Product Shapes: A Review

Abstract Conceptual design is the foundational stage of a design process that translates ill-defined design problems into low-fidelity design concepts and prototypes through design search, creation, and integration. In this stage, product shape design is one of the most paramount aspects. When applying deep learning-based methods to product shape design, two major challenges exist: (1) design data exhibit in multiple modalities and (2) an increasing demand for creativity. With recent advances in deep learning of cross-modal tasks (DLCMTs), which can transfer one design modality to another, we see opportunities to develop artificial intelligence (AI) to assist the design of product shapes in a new paradigm. In this paper, we conduct a systematic review of the retrieval, generation, and manipulation methods for DLCMT that involve three cross-modal types: text-to-3D shape, text-to-sketch, and sketch-to-3D shape. The review identifies 50 articles from a pool of 1341 papers in the fields of computer graphics, computer vision, and engineering design. We review (1) state-of-the-art DLCMT methods that can be applied to product shape design and (2) identify the key challenges, such as lack of consideration of engineering performance in the early design phase that need to be addressed when applying DLCMT methods. In the end, we discuss the potential solutions to these challenges and propose a list of research questions that point to future directions of data-driven conceptual design.

Investigations on System Integration Method and Optimum Design Method of Electro-Mechanical Actuator System

With technological advances and industrial upgrading, high-performance equipment has put higher demands on the performance of electro-mechanical actuators. With a view to making electro-mechanical actuators more reliable and integrated, firstly, an integrated electro-mechanical actuator module (IEMM) with multiple structural forms was proposed in this paper, and a comparative analysis was performed on the characteristics of different transmission schemes. Then, the feasibility of manufacturing the IEMM’s main bearing components using Carbon Fiber Reinforced Polymer (CFRP) with higher specific stiffness, specific modulus and specific strength was demonstrated by finite element simulation (FEA) software, in a bid to further reduce the weight of the IEMM. Next, a parameter estimation model, a heating power calculation model, and a thermal resistance calculation model were built, so that there is no need to rebuild the whole system model under different demand indexes. On this basis, a multi-objective optimization design model was built with light weight, low power loss, and high level of integration as optimization aims to achieve better comprehensive performance in the early design phase of the IEMM system. However, IEMM’s higher level of integration and its shell made of CFRP with a thermal conductivity of less than 5 W/m°C posed a challenge to the heat dissipation of the motor stator. Therefore, a thermal network model needs to be created in AMESim to evaluate the temperature of IEMM’s parts and components under different working conditions. Finally, the process of IEMM performance optimization design was described and improved, and performance optimization design was conducted by taking one of IEMM’s transmission schemes as an example.

An approach to the selection of design parameters based on conflict with constraints

The Axiomatic Design (AD) supports decision-making based on the axioms of independence and information. In the engineering design activity, the designer must make early decisions that impact the success of the project. These decisions are of paramount importance in the design of one-off products. In these products, one must make decisions in the early design phases, with scarce data about the product that will undeniably affect the solution. Applying the information axiom requires data from the alternative solutions under consideration that for one-off products are most often non-existent in the conceptual phase of the design.Additionally, the design process is highly conditioned by existing constraints, which significantly influence the outcome. This work suggests selecting design parameters (DP) for one-off products from different sets of proposals that comply with the independence of functional requirements (FR). AD design follows a hierarchical decomposition process from a conceptual to a detailed design. The proposed method allows selecting the DPs among the set of alternatives by quantifying the degree of conflict each solution presents to the constraints. It shows the first step to define the DPs, at each level of decomposition of the AD process. It is the first step to establish a zig in AD or, otherwise, a way to find a solution in the conceptual phase. It applies to one-off products – any product with one chance to be right. The paper applies this method to the DP selection of a manipulation device.

A Pilot Study on the Effect of Daylighting and Orientation in the Office Building: A Case of Kathmandu

Illuminance results in visibility of objects and daylighting is the major source of light for perceiving and carrying out certain function. Office blocks hold the space for people to perform economic task for 7-8 hours daily and humans seek comfort. Lighting the interior is a mindful job, as the space can be over lit or under lit resulting in visual discomfort and high use of energy to maintain human comfort. Quality and quantity of natural light in the indoor is guided by several factors such as climate, latitude, sky condition, site obstructions, orientation, opening design, surface reflectivity, space function etc. The study shows that the best orientation for Kathmandu is 175°N and for the building with window wall ratio greater than 60%, orientation does not play a vital role. Autodesk Ecotect 2011, has been used to simulate the building with the real case scenario in the urban context for the outmost result. The finding adds value during the early design phase and also to prepare guidelines, policies and bylaws relating to wellbeing of occupant for visually and functionally friendly interior.

Research, development and production costs prediction parametric model for future civil hypersonic aircraft

Assessing the economic viability of new high-speed systems concepts since the early design phases is crucial for the success of future hypersonic vehicles including cruisers, reusable access-to-space and re-entry systems. Besides literature reports few parametric cost models for high-speed vehicles, all of them makes exclusively use of mass as parameter and none of the models moves beyond the vehicle level. This paper describes a new parametric cost estimation model which moves beyond the state-of-the-art methodologies (1) by integrating vehicle design and operational parameters (in addition to the mass) as cost drivers for the prediction of the vehicle life-cycle cost, (2) by introducing prediction margins accounting for the uncertainties on the data-driven correlations, (3) by providing a first estimate of the costs of every on-board subsystem, including combined cycle engines and multi-functional subsystems, (4) by increasing the granularity of the analysis up to technology level, thus providing a valuable support to Technology Roadmaping activities. The parametric cost estimation model has been refined and exploited in the context of the Horizon 2020 STRATOFLY project, where the technological, operational, environmental, and economic viability of a Mach 8 waverider concept have been investigated.

Connected design decision networks: Multidisciplinary decision support for early building design LCA

Life Cycle Assessment (LCA) has become the standard method to evaluate environmental impact throughout the life cycle of buildings. However, detailed data about the future building as well as knowledge about the mutual influence of decisions concerning the various disciplines involved are often missing in early design phases, otherwise known to bear the highest potential for emissions savings. Hence, a meaningful basis for decision making is lacking.This study suggests a method to digitally represent decisions and their interdependencies in early design phases and visualize their possible consequences for the life cycle of the future building. The method is based on identification of relevant processes and tasks concerning architecture and Heating Ventilation and Air Conditioning (HVAC). Decisions trees of these tasks are used as a point of departure. Connecting the decision trees to a multidimensional, Connected Design Decision Network (CDDN) enables an interdisciplinary design team to pinpoint strategic decision nodes with comparatively more interdependencies with other subsets and high influence on LCA results. We believe that a transparent decision making in early design stages can be valuable to both the design team as well as clients and contractors and bear potential for an increased mutual awareness minimizing late and expensive redesigns.

Large Interferometer for Exoplanets: VIII. Where Is the Phosphine? Observing Exoplanetary PH3 with a Space-Based Mid-Infrared Nulling Interferometer.

Phosphine could be a key molecule in the understanding of exotic chemistry that occurs in (exo)planetary atmospheres. While phosphine has been detected in the Solar System's giant planets, it has not been observed in exoplanets to date. In the exoplanetary context, however, it has been theorized to be a potential biosignature molecule. The goal of our study was to identify which illustrative science cases for PH3 chemistry are observable with a space-based mid-infrared nulling interferometric observatory like the Large Interferometer for Exoplanets (LIFE) concept. We identified a representative set of scenarios for PH3 detections in exoplanetary atmospheres that vary over the whole dynamic range of the LIFE mission. We used chemical kinetics and radiative transfer calculations to produce forward models of these informative, prototypical observational cases for LIFEsim, our observation simulator software for LIFE. In a detailed, yet first order approximation, it takes a mission like LIFE: (i) about 1 h to find phosphine in a warm giant around a G star at 10 pc, (ii) about 10 h in H2 or CO2 dominated temperate super-Earths around M star hosts at 5 pc, (iii) and even in 100 h it seems very unlikely that phosphine would be detectable in a Venus-Twin with extreme PH3 concentrations at 5 pc. Phosphine in concentrations previously discussed in the literature is detectable in 2 out of the 3 cases, and it is detected about an order of magnitude faster than in comparable cases with James Webb Space Telescope. We show that there is a significant number of objects accessible for these classes of observations. These results will be used to prioritize the parameter range for the next steps with more detailed retrieval simulations. They will also inform timely questions in the early design phase of a mission like LIFE and guide the community by providing easy-to-scale first estimates for a large part of detection space of such a mission.

Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction

Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the prediction of building energy efficiency. However, the existing models are based on classical machine learning approaches and small datasets. Using a small dataset and inefficient models may lead to poor generalization. In addition, it is not common to see studies examining the suitability of machine learning methods for forecasting the energy consumption of buildings during the early design phase so that more energy-efficient buildings can be constructed. Hence, for these purposes, we propose a multilayer extreme learning machine (MLELM) for the prediction of annual building energy consumption. Our MLELM fuses stacks of autoencoders (AEs) with an extreme learning machine (ELM). We designed the autoencoder based on the ELM concept, and it is used for feature extraction. Moreover, the autoencoders were trained in a layer-wise manner, employed to extract efficient features from the input data, and the extreme learning machine model was trained using the least squares technique for a fast learning speed. In addition, the ELM was used for decision making. In this research, we used a large dataset of residential buildings to capture various building sizes. We compared the proposed MLELM with other machine learning models commonly used for predicting building energy consumption. From the results, we validated that the proposed MLELM outperformed other comparison methods commonly used in building energy consumption prediction. From several experiments in this study, the proposed MLELM was identified as the most efficient predictive model for energy use before construction, which can be used to make informed decisions about, manage, and optimize building design before construction.

Transformation of Architectural Design Concepts During the Early Design Phase

The most difficult objective for students in an architectural design studio during the early design phase is to produce a design concept satisfyingly. This is due not just to the difficulty of selecting an appropriate method of approaching concepts, but also because of the many transformations associated with the concept's evolution over time.
 This research examines the modifications that occur throughout the conceptual phase of a design session, which typically lasts a few weeks. We hypothesize that while students' conception methods have been more abstract in creating a concept, they are subject to more alterations during the early phase of design. The research conducts concept generation and transformations by monitoring a group of fourth-grade architecture students through their design process. It also tries to link those transformations to the various methods utilized by students to create their concepts. It appears that the transformations that occurred throughout the design sessions were either formal or functional-oriented. Observations clarify that those transformations are linked to the sort of concept generating, which is either abstract or concrete. By observing the design sessions, the research was able to confirm the previous hypothesis. The modifications implemented to the design concepts were obviously tied to the nature of their selection of methods of production. When compared to concrete nature concepts, abstract nature concepts were more vulnerable to transformations throughout the conceptual phase of the design task on the scope of form and function.

Development of a Design Thinking Pedagogical Model for Secondary Schools Science Teachers in Malaysia: A Needs Analysis

In line with UNESCO's goal of enhancing quality and innovation through various enhancements to Science, Technology, Engineering, and Mathematics (STEM) education, the Malaysian Ministry of Education also places a premium on information enhancement through STEM education and the Malaysia Education Blueprint (MEB) 2013-2025. Design thinking is an essential component of STEM education in modern schooling. The significance of design thinking, particularly in STEM education, fosters creativity and innovation in problem-solving among students. The needs analysis research is the early phase of Design and Development Research (DDR). This study aims to determine the requirements for developing a Design Thinking Pedagogical Model for secondary school science teachers in Malaysia. The interview method was used to perform the qualitative research. Thus, semi-structured interviews with three science teachers were conducted to explore teachers' perspectives on the need to develop a Design Thinking Pedagogical Model. Purposive sampling was used to choose these experts, and they all matched the criterion. Thematic techniques such as verbatim transcription, coding, and themes were used to examine the interview data. The findings of this study indicate that researchers need to develop a Design Thinking Pedagogical Model of Science by a panel of expert teachers in teaching sciences in secondary schools. As such, the development of this Design Thinking Pedagogical Model will serve as a practical guide for teachers in terms of training and utilizing design in students and practicing the skills necessary for the 21st century through learning and facilitation.

Scrutinizing the Adoption of Integrated Project Delivery in the Kingdom of Saudi Arabia Construction Sector

The Integrated Project Delivery (IPD) approach has been acquiring applause worldwide attributable to its certified and attested outcomes in efficiently sharing threats and costs confronting the construction project. Regardless of its popularity and rationality, no substantial studies have been conducted into the Kingdom of Saudi Arabia’s (KSA) construction. Hence, the novelty of this paper is probing the Saudi Arabia government tender process and procurement regulation for IPD theme deployment, reliant on a thorough literature assessment to bestow construction parties with the barriers of incorporating the IPD paradigm in KSA. The research objectives are attained via a questionnaire survey steered toward (1) pondering respondents’ cognizance of IPD eventuality and deployment in the KSA’s construction sector, (2) stipulating the survey’s participants’ preparedness for IPD implementation in KSA’s construction market, (3) specifying the project phase in which the contractor should be entangled as part of the IPD method, and (4) scrutinizing the respondents’ knowledge to classify the anticipated barriers to IPD from the global market in KSA. Findings unearth that the KSA construction sector still entails being more conscientious and adequate, pointing out the difficulties triggered by a dearth of awareness, apprehension, and pragmatic implementation. Further, respondents showed impartiality towards construction project stakeholders’ prescience and exuberance, the existing government procurement and tendering laws, and revolutionary technological infrastructure and competency for IPD implementations in the KSA construction industry. Additionally, the contractor should be implicated and embroiled in the construction project from the early design phase. Addedly, the hurdles to deploying IPD in KSA are ranked as follows: technological, knowledge, financial, legal, and cultural barriers.