Early Phases Of Product Development Research Articles

A conceptual MCDA-based framework for machine learning algorithm selection in the early phase of product development

A conceptual MCDA-based framework for machine learning algorithm selection in the early phase of product development

Context-based Derivation of Holistic Sustainability Requirements in the Early Phase of Product Development

Gaining knowledge about the sustainable context is fundamental for the success of a product development, as a lack of knowledge limits the possibilities of innovation. The contextual boundaries of a system often do not adequately describe the influence on sustainability, so that product developers are unaware of problem shifts within and between the areas of impact on sustainability. For reasons of complexity, a therefore needed holistic captured system context is insufficiently taken into account in existing approaches. Within the scope of this article, a framework for Sustainable Context Engineering is developed, that provides a formalization of a holistic sustainable system context for the analysis and synthesis of relations in product development. Furthermore, a requirements methodology based on SPES 2020 is proposed, which supports the decomposition of requirements by model-based templates via a subtractive structure. This promotes communication and situational awareness among developers in order to influence the sustainability goals in the early phase of the product development process. Further research will be made in order to implement the approach in a model-based manner and to define a finite number of measurement parameters based on interdisciplinary sustainability models.

Supporting Sustainable Product Design with Engineering Data Management Capabilities

Today, innovations are no longer defined just by technical progress but also, in particular, by environmental aspects (such as carbon gas emissions). The issue of reuse is also becoming increasingly central to purchasing decisions and is forcing manufacturing companies to deal transparently with environmental impacts. This is also receiving a lot of political attention and is underlined by increasingly far-reaching regulations on sustainability, such as CSRD, Product Declarations, Transparent Supply Chains, Substance Compliance, etc. Since 80% of the environmental impact is already determined in the early product development phases, there is a high potential for improvement. This is where engineering data management approaches (such as PLM) come into play as a systematic approach to data, document, and information management. Many KPIs for determining the environmental impact are often already available in PLM. Still, it is necessary to bring them into context in a targeted way to support early qualitative and quantitative evaluations in the engineering phases regarding the current environmental impact of the design. The paper, therefore, presents a concept based on PLM-available data along the product engineering process for assessing ecological impact. This includes the product life cycle (and the supply chain) as well as the prototypical provision of environmental information and is demonstrated using platform technology by CONTACT Software.

Big data and machine learning in cost estimation: An automotive case study

This paper presents a case study on the applicability of machine learning and big data technology for product cost estimation, using data on the material cost of passenger cars. The study provides contributions to six research aspects. First, we show which machine learning algorithms are appropriate when dealing with product cost estimation for highly complex products with more than 2000 parts and hundreds of cost drivers. Second, our case study provides a novel approach to increasing the predictive accuracy of cost estimates for subsequent product generations. Third, we show that the accuracy is up to 3.5 times higher when using big data compared to an intermediate size of data. Fourth, machine learning can outperform cost estimates from cost experts during the early stage of new product development, even when dealing with highly complex products. Then, we evaluate the use cases, issues, and benefits of machine learning and big data from the perspective of cost experts. Specifically, the case study shows that machine learning can reliably select the most important cost drivers (fifth aspect) and calculate the average cost of cost drivers over thousands of product configurations (sixth aspect). However, cost experts must be knowledgeable about the product and remain careful when interpreting machine learning outcomes, as they can yield misleading outcomes in some exceptional cases. In conclusion, machine learning and big data empirically proved to be able to generate additional value in many aspects for managing costs during the early phase of new product development.

Resource conservation by means of lightweight design and design for circularity—A concept for decision making in the early phase of product development

Lightweight design can contribute to savings of consumed material in products and enhancing their energy efficiency during the use phase but also to a higher resource consumption at the beginning- and the end-of-life, challenging the implementation of a circular economy. Hence, this publication methodologically addresses the synergies and conflicts of lightweight design and design for circularity. The concept of the ‘functional life cycle energy analysis’ is presented, which foresees the division of a product architecture into functions with allocated energy consumptions as cross-stage indicator for the expected resource consumption along the entire product life cycle. Holistic optimization potentials within three life cycle stages can thus be derived as recommendations for action for future product generations. This allows engineers to rethink functional principles and supports decision making in the early design phases of implementing lightweight design and design for circularity. The methodology is illustrated by means of a robotics use case.

Data-driven product optimization capabilities to enhance sustainability and environmental compliance in a marine manufacturing context

This paper investigates concerns related to product data and digital data flow when aiming to automate company processes. Accurate data is necessary to create value by enabling improved decision-making in product development, including sustainability capabilities. The case analyzed is an engineer-to-order (ETO) company operating in a low-volume marine manufacturing context. A participatory research approach is used to study two projects that are part of the company’s digital business transformation, aiming to digitalize information and autogenerate downstream processes. Building on the strengths promised by digitalization requires precise and extensive product and process information. An important facilitation capability is to create a digital thread from design to finished product, including product documentation. This is necessary to establish capabilities both to autogenerate appropriate compliance reporting as part of the product development process and to conduct virtual testing and validation before the physical equipment is acquired, resulting in a manufacturing process that is ‘right first time’. In addition, data capabilities guide and enable sound-decision making for improved sustainable practices in the early phase of product development. It is found that the data quality required to utilize tools within the context of Industry 4.0 demands changes to existing product design practices and focus on the three pillars harmonization, integration and automation of data and systems.

Green and Safe “By Design”: A Conceptual Tool to Avoid Hazardous Substances in Early Phases of Product Development

Sustainability has to be managed in order to guarantee health, social equity, resource optimization and economic activities and this awareness is grown considerably. At the same time, this consciousness leads us to consider sustainability also as a social and a business driver on the economic side and a great opportunity by an environmental point of view. Further, according to several regulations, producers are responsible of their devices in terms of environmental pollution, from production and market launch to disposal activities when they become unusable. This fact changes the role of designers, which becomes wider in a multidisciplinary context. For this reason, this paper proposes an approach that supports designers during the development of more sustainable products with the aim to reduce the amount of hazardous substances (according to European RoHS regulations) and it allows firms and academic R&D departments to reduce risks of a missing compliance. The approach is structured according to a flowchart that addresses designers to more suitable product solutions in terms of environmental impact. The methodology applied to the case studies, in fact, show that an early assessment in first design phases allows developers to have safer and more sustainable product prototypes and its application can be proposed also during design courses in an educational context.

MODELLING TECHNICAL SYSTEMS IN THE EARLY PHASE: PROPOSING A FORMAL DEFINITION FOR THE SYSTEM CONCEPT

AbstractThe task of developing “concepts” is common in all fields of engineering, especially in the early phases of product development. However, an in-depth literature analysis showed that authors - often depending on different contexts in design research, education, and industry - define the term “concept” in differing ways. The aspect of reference-based development is rarely addressed in existing definitions. This indicates that there is a need for an updated and concise concept definition. In this paper, the authors propose a new definition of the term “system concept” within the context of SGE - System Generation Engineering that incorporates the findings from the literature analysis. The definition was reflected on in two case-studies. The first one contained the system concept for automotive display and operating systems, the second one the system concept for a kinesthetic-haptic VR interface. The proposed definition contains the relevant characteristics identified from the literature review and supports both current activity-based process models and reference-based development, as practical application has shown.

EXPANDING USER NEED FINDING THROUGH ABDUCTIVE REASONING

AbstractPrior research has shown the importance of latent user needs for enabling innovation in early product development phases. The success of a product is largely dependent on to what extent the product satisfies customer needs, and latent user needs play a significant role in impacting the way the product or service unexpectedly delights the user. Complications arise because traditional need finding methods are not able to account for the nuances of latent user needs. A user's need is multidimensional while traditional methods are built on deductive reasoning. The traditional method isolates parts of the user's needs, only pointing to what is deducible within its search space. To address this, we introduce abduction as a way to broaden traditional need finding methods. From a logic based argument it is shown that abduction accounts for the dimensionality of user needs by integrating various traditional need finding theories using design knowledge to isolate the latent need. This theoretical development shows that latent need finding must go beyond a deductive focus, to developing methods that are able to conjecture with the deduced facts in order to abduce the latent user need.

NEW OPPORTUNITIES AND BENEFITS IN THE PRODUCT DEVELOPMENT PROCESS USING THE MACHINE LEARNING BASED DIRECT INVERSE METHOD FOR MATERIAL PARAMETER IDENTIFICATION

AbstractFinite element (FE) simulations can be used both in the early product development phase to evaluate the performance of developed components as well as in later stages to verify the reliability of functions and components that would otherwise require a large number of physical prototype tests. This requires calibrated material cards that are capable of realistically representing the specific material behavior. The necessary material parameter identification process is usually time-consuming and resource-intensive, which is why the direct inverse method based on machine learning has recently become increasingly popular. Within the neural network (NN) the generated domain knowledge can be stored and retrieved within milliseconds, which is why this method is time and resource-efficient. This research paper describes advantages and potentials of the direct inverse method in the context of the product development process (PDP). Additionally, arising transformation opportunities of the PDP are discussed and an application scenario of the method is presented followed by possible linkage potentials with existing development methods such as shape optimization.

Challenges in Geometry Assurance of Megacasting in the Automotive Industry

Abstract Megacasting is a new concept in the automotive industry. A large number of sheet metal parts will be replaced with one large aluminum casting, i.e., a megacasting. This helps to reduce weight, opens up for larger design flexibility, allows for a more circular production, and takes away a large number of assembly steps in the production process. However, there are also challenges related to the use of megacastings. This position paper outlines challenges associated with the geometrical quality of the final product. It covers robust design and tolerancing in early product development phases as well as inspection preparation during pre-production and digital twin setup during full production to ensure the geometrical quality of a product containing a megacasting. Simulations of both part-level and assembly-level deviation and variation are discussed. The paper outlines a geometry assurance process for products containing megacastings in the automotive industry, and what research challenges that are the most important ones to address in this area. It is concluded that computer-aided tolerancing tools must be able to predict the dimensional effects from joining methods such as flow-drill fasteners or self-pierced riveting, to use casting simulation as input, and to handle combinations of solid and surface meshes. Furthermore, there might be a need for adjustments to the joining process based on digital twins to achieve proper quality at a reasonable price. Experiences in using megacastings in the body-in-white are lacking and a fast learning curve is required.

An Affordable Acoustic Measurement Campaign for Early Prototyping Applied to Electric Ducted Fan Units

New innovative green concepts in electrified vertical take-off and landing vehicles are currently emerging as a revolution in urban mobility going into the third dimension (vertically). The high population density of cities makes the market share highly attractive while posing an extraordinary challenge in terms of community acceptance due to the increasing and possibly noisier commuter traffic. In addition to passenger transport, package deliveries to customers by drones may enter the market. The new challenges associated with this increasing transportation need in urban, rural, and populated areas pose challenges for established companies and startups to deliver low-noise emission products. The article’s objective is to revisit the benefits and drawbacks of an affordable acoustic measurement campaign focused on early prototyping. In the very early phase of product development, available resources are often considerably limited. With this in mind, this article discusses the sound power results using the enveloping surface method in a typically available low-reflection room with a reflecting floor according to DIN EN ISO 3744:2011-02. The method is applied to a subsonic electric ducted fan (EDF) unit of a 1:2 scaled electrified vertical take-off and landing vehicle. The results show that considerable information at low costs can be gained for the early prototyping stage, despite this easy-to-use, easy-to-realize, and non-fine-tuned measurement setup. Furthermore, the limitations and improvements to a possible experimental setup are presented to discuss a potentially more ideal measurement environment. Measurements at discrete operating points and transient measurements across the total operating range were conducted to provide complete information on the EDF’s acoustic behavior. The rotor-self noise and the rotor–stator interaction were identified as primary tonal sound sources, along with the highest broadband noise sources located on the rotor. Based on engineering experience, a first acoustic improvement treatment was also quantified with a sound power level reduction of 4 dB(A). In conclusion, the presented method is a beneficial first measurement campaign to quantify the acoustic properties of an electric ducted fan unit under minimal resources in a reasonable time of several weeks when starting from scratch.

Sustainability Fingerprint - guiding companies in anticipating the sustainability direction in early design

During the early phases of design of new products, information necessary to guide and assess sustainability is typically incomplete. Manufacturers risk discovering the unfavorable sustainability impact too late to redirect the development. The purpose of this study is to search and review methods to anticipate the sustainability performance of new products in the early stages of the innovation process, and, together with a case company in the aeroengine manufacturing business, to develop and propose a systematic approach to anticipate sustainability performance. Following a literature review of 51 methods and research-based proposals, only five covered the social-, ecological-, and economic sustainability dimensions in the early phases of product development. Through an action research-based approach, a qualitative sustainability measurement tool, the Sustainability Fingerprint tool, was developed. This tool allows the development team in a company to systematically derive a sustainability performance from a set of criteria unique to the targeted product and/or product portfolio that is to be designed, from a set of scientifically well-established sustainability principles. The tool was applied in six different industry cases at the company, by the design teams and independent of any influence of the researchers. Semi-structured interviews were conducted to learn about the usefulness, usability, and applicability of the tool. It was concluded that the Sustainability Fingerprint tool has no equivalent in the literature and was considered useful for the case company during early phase design for two main reasons. Firstly, the tool is tailor-made with defined company-specific sustainability criteria that represent the most important sustainability aspects to assess. Secondly, the support tool can be used also in later phases, thereby supporting a continued innovation process. It allows designers to anticipate a sustainability performance of the solution in: the development of sustainable innovations; comparison and down-selection of alternative solutions; sustainability assessments of the product portfolio to identify improvement measures; and sustainability assessment of current products to identify sustainability hotspots. Further research will include companies from other industrial sectors and will focus on adding digital support, improved practical instructions, and, integration to existing processes and established support tools in companies today.

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.

A Designation Guide for Consistent Cross-System-Level Modeling of Embodiment Function Relations

In the early phase of product development, the possibility of goal-oriented discussion of complex systems is particularly important to avoid communication errors. In practice, misunderstandings occur repeatedly when discussing systems, since different contents are verbalized unconsciously. To address this problem, systems to be analyzed can be divided into subsystems and system elements can be considered. This problem can also be addressed by using approaches for the consideration of system elements and of embodiment function relationships (EFR). One of these approaches is the so-called C&C2 approach. Although this approach has been used successfully many times, its practical applications have so far shown differences in the designation of the elements. As a result, systems and models cannot always be compared with each other with sufficient accuracy. A further point is the constant change of the systems during the product engineering process (PEP), which up to now can hardly be consistently represented in the designations of the model elements. For this reason, a designation guideline was developed for the C&C2 approach, which enables the consistent designation of systems and model elements throughout the entire PEP. In addition, the approach consistently allows for any level of detail across the entire PEP. This designation guide has been validated very successfully in a case study in automotive engineering. In the future, the designation guide will allow many models to be compared, thus enabling the creation of a database. This can be a cornerstone for development with artificial intelligence in the future.

Design of a Multi-Fidelity Methodology for logistics process planning and Digital Twin integration in the early phases of product development

Design of a Multi-Fidelity Methodology for logistics process planning and Digital Twin integration in the early phases of product development

A Conceptual Framework for Data Sensemaking in Product Development—A Case Study

The industry acknowledges the value of using data and digitalization approaches to improve their systems. However, companies struggle to use data effectively in product development. This paper presents a conceptual framework for Data Sensemaking in Product Development, exemplified through a case study of an Automated Parking System. The work is grounded in systems engineering, human centered-design, and data science theory. The resulting framework applies to practitioners and researchers in the early phase of product development. The framework combines conceptual models and data analytics, facilitating the range from human judgment and decision-making to verifications. The case study and feedback from several industrial actors suggest that the framework is valuable, usable, and feasible for more effective use of data in product development.

A new, flexible tool concept for rotary die stretch bending

Mitigating manufacturing risks for profile-type parts formed using stretch bending operations is a considerable challenge in the industry, due to the investments required to manufacture the tooling for these processes. Traditionally such tooling has been made by solid steel blocks machined to the desired shape. This paper introduces a new tool concept for stretch bending processes, utilizing an adjustable multipoint supported flexible spring steel plate as a bend die, applied on a double rotary turntable stretch bender. The capability of the tool is investigated through a proof-of-concept experiment, assessing whether the tool design is capable of creating bends with the same quality as bends made using traditional solid steel dies. Overall, the experiment shows promising results as it allows for creating a large variation of bending shapes with good dimensional quality with low tooling investment, although the interface between the spring steel plate end and the aluminum profile creates some minor scuffing marks. The initial investigation found a pooled standard deviation of 0.25mm over 3 bend shapes with 3 samples for each, which is promising for further development. In industrial applications, the proposed tool would enable cost-efficient experimenting using production-intent tooling in the early product development phase, which could also be utilized for pre-production qualification processes, low volume manufacturing and multi-variant products.

Fundamentals of Circuit Failure Analysis Based on Optical Fault Isolation Techniques

Circuit failure analysis based on optical fault isolation (OFI) techniques is widely used for Si debugging at the early phase of product development or failure analysis to improve yield and reliability issues at mass production stage. Three of OFI techniques are usually used for failure analysis. Dynamic laser stimulation affects <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{th}$ </tex-math></inline-formula> shift due to thermal heating or electron-hole pair generation in local area depending on the wavelength of the laser used. Laser voltage image and probing provide toggle image of signal and direct waveform differences between good and failure circuits, respectively. This paper introduces the fundamentals of Si level circuit analysis including digital and analog circuit using optical fault isolation method based on theoretical and experimental results.

Conceptual Design for Assembly methodology formalization: systems installation analysis and manufacturing information integration in the design and development of aircraft architectures

In recent years, the air transport market has experienced strong growth, increasing the demand for new civil aircraft, challenging the actual production rate of aerospace industries. The bottleneck of the production for the aviation industry lies in the capability of the manufacturing and assembly facilities to fulfill the module arrangement in the current design. The development of optimized product architecture requires the implementation of design for assembly principles at the conceptual design phase closing the gap between the design and the production departments. The study proposes a Conceptual Design for Assembly (CDfA) methodology which aims at the assessment of aircraft systems installation and assembly at the early phase of product development (conceptual design). The CDfA methodology allows comparing assembly performance of different aircraft architectures identifying critical modules and interfaces as well as assembly/installation issues. The methodology is based on a specific framework (hierarchical structure) which is characterized by levels, domains, and attributes. Levels enable the analysis of product architectures at different levels of granularity, splitting the global analysis into sub-problems (problem discretization). Domains and attributes are defined with a knowledge-based engineering approach considering available information at the conceptual design phase and production criteria. A complex system (the nose fuselage of a commercial aircraft) was chosen as a case study to test the robustness of the methodology in relation to the assembly performance observed within the manufacturing facilities. Results revealed the architectural elements (modules and interfaces) that contribute to inefficient assembly operations, as well as the rationales enabling to elaborate alternative architectures for an improved product industrial efficiency.