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![Fig. 6. Product diversity cost [91].](profile/Filippo-Salustri/publication/3421819/figure/fig5/AS:349578504228869@1460357418616/Product-diversity-cost-91_Q320.jpg)
Product design engineering is undergoing a transformation from informal and largely experience-based discipline to a science-based domain. Computational intelligence (CI) offers models and algorithms that can contribute greatly to design formalization and automation. This paper surveys CI concepts and approaches applicable to product design enginee...
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... modeling involves two notions (see Fig. 3). 1) Horizontal. 2) Vertical. A process model is seldom developed at one level rather it is built vertically and horizontally. The top node in the hierarchy denotes the overall process that is decomposed into lower level components. The most granular model is usually a network of activities (the horizontal notion). To support the ...
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... Product design is particularly closely related to product innovation. In the era of big data, product design is evolving towards a data-driven approach, benefiting from industrial big data analytics (Kusiak & Salustri, 2007;Li et al., 2022). Innovative product features can emerge from integrating diverse data sources and using data-mining algorithms to reveal previously unseen value (Kusiak, 2009). ...
Sustainability is one of the greatest challenges for industry today. The purpose of this paper is to study the influence of sustainability orientation on product innovation in the European manufacturing sector, with a particular focus on the direct and mediating effect of industrial big data use, something that has been largely neglected so far. The data used for the purpose of the present study were collected from the European Manufacturing Survey (EMS) 2018 edition, consisting of 1,123 surveys administered in Austria, Spain, Croatia, Lithuania, Slovakia, Slovenia and Serbia. Binary logistic regressions and Hayes mediation models are used to test
the hypotheses. Results suggest that sustainability orientation practices and industrial big data use positively influence product innovation, and that industrial big data use mediates the relation between sustainability orientation and product innovation. The findings have implications for both theory and practice.
... The approach is modelled on a Knowledge-Base System (KBS) architecture (Tripathi 2011). A KBS is a form of Artificial Intelligence (AI) that aims to capture knowledge to support decisionmaking (Kusiak and Salustri 2007). DESMO-APP system architecture is also based on the Logical Layered Design as presented in Microsoft's Application Architecture Guide (Microsoft 2009). ...
... AI systems can encode knowledge as rules and draw logical conclusions based on these programmed rules. These are referred to as Reasoning Systems (Kusiak and Salustri 2007). Reasoning systems are programmed to follow a logical process and map conditions to actions, where the knowledge is captured by an expert (Hvam, Mortensen, and Riis 2008). ...
The physical construct of a dual ontological product is essential for those products which physically interact directly with humans, whereas the product's emotional construct interconnects with human cognition. Multitude of human factor aspects must be considered when designing dual ontological products. To increase the product's impact and reach, designers should also understand the requirements of potential users. Designers find it difficult to achieve the right compromise between these constructions. This research therefore contributes a novel harmonistic knowledge-based computational support tool which makes designers aware of design stage conflicts and consequences of commitments made on human factors in the use phase of the artefact. This paper describes in detail the machine learning and harmonistic knowledge-based system which exploits information collected directly from potential users to proactively assist, guide, and motivate product designers. The paper takes the motorcycle artefact as a case of dual ontological product. The prototype support tool has been evaluated with 28 motorcycle design engineers. The results obtained from this evaluation have shown that the approach and design computational-based tool meet their goals, are beneficial, and are required in design engineering practice.
... On the one hand, computer graphics technologies enable the geometric modelling of complex curved surfaces, e.g., the Bezier curves and surfaces [19], achieving digitalized design records from paper-based design record and realizing fewer human errors. On the other hand, computational analytics [20], such as data mining, evolutionary computation, and CBR, enable the simulation, evaluation, and validation of product models with higher efficiency and lower probability of quality failures during manufacturing. Besides, with the emergence of information management systems, the value of data is revealed by product lifecycle management (PLM) systems with a systematic view, which became the initial of the latter data-driven design. ...
Data-driven design (D3), a new design paradigm benefited from advanced data analytics and computational intelligence, has gradually promoted the research of data-driven product design (DDPD) ever since 2000 s. In today’s Intelligence Age, some theoretical and practical studies have tried to achieve the advanced intelligence capabilities in DDPD. However, to the authors' best knowledge, there is still a lack of a holistic summary of DDPD with chronological concern in the intelligence age. To bridge the gap, this research undertakes a literature review of DDPD publications from 2000 to date (19/09/2022), of which 172 relevant papers are discussed via bibliometric analysis and state-of-the-art analysis. The results shown that DDPD has vitality in the Intelligence Age by combining the cutting-edge digital technologies, such as AI, additive manufacturing, digital twin, and so on. Moreover, current DDPD studies could outperform the classical design methods on the well-defined tasks, but it still cannot master the creative/innovative design tasks which require the cognitive capability. This survey further highlights several future research potentials including cognitive intelligence-enabled design, end-to-end design integration, advanced design knowledge support, design for additive manufacturing, and sustainable smart product-service systems. It is hoped that this work can be regarded as a reference to understand the roadmap of DDPD and offer insights for the design practitioners to complete relevant tasks in today’s intelligence age.
... Reference [11] investigated the emerging perspectives in curriculum design, outlining that "program design and curation tool (and the associated workflows) that meets the requirements above will necessarily be based on digital computing technologies and will be data-driven". The use of data coming from different design-related sources is becoming more and more important to enhance the design process [12]. Many researchers and educational institutions are developing data-driven strategies for designing courses to support the decision-making process, e.g., [13]. ...
... Nowadays, design is recognized as a key element of the innovation process at-large, with a strong integration also with the domain of technology foresight (Anderson, 1997). Despite its long history, only in recent years has data-driven design become more prominent thanks to the recent developments in AI, focusing on the use of data coming from different design-related sources in order to make the design process more effective (Kusiak and Salustri, 2007). Furthermore, the influences of new societal challenges must be considered. ...
Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS.
We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines.
The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities.
... Design plays an important role in manufacturing process, which determines most of a product's performance and performance [12]. With the development of big data technology, product design is shifting towards data-driven design from subjective conceptual design [66]. Big data-driven product design analyzes the market demand through users' evaluation [67]. ...
With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the amount of data from manufacturing systems has been increasing rapidly. With massive industrial data, achievements beyond expectations have been made in the product design, manufacturing, and maintain process. Big data analytics (BDA) have been a core technology to empower intelligent manufacturing systems. In order to fully report BDA for intelligent manufacturing systems, this paper provides a comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies. The framework, development, key technologies, and applications of BDA for intelligent manufacturing systems are discussed. The challenges and opportunities for future research are highlighted. Through this work, it is hoped to spark new ideas in the effort to realize the BDA for intelligent manufacturing systems.
... Taxonomy of the surveyed literature is presented according to the generally recognized areas in both product design engineering and computational intelligence. Some research issues that arise from the broad perspective signaled but not fully pursued (Kusiak and Salustri 2007;. Delivering products that meet the requirements of individual customers complicates the production process, and diminishes the benefits of the economy of scale. ...
The problem of frequent pattern mining has been well studied and there exist numerous techniques in identifying a subset of pattern from large pattern set which represent the frequency of items. However, they suffer to identify the unique pattern which has higher frequency and importance throughout the data set. To handle this issue and to identify the optimal pattern in a relational database, an incremental utility based pattern mining algorithm is presented. The proposed frequent pattern utility incremental algorithm (FPUIA) uses the unstructured data and uses time series machine learning (TSL) approach to perform frequent analysis. The model is designed to identify the recurrent itemset and the pattern set is selected based on the support and confidence measures. Initially, frequent patterns are selected based on the minimum support and confidence where the next level pattern are generated based on the frequency of patterns in the selected set, which are measured iteratively. The proposed system produces high supportive measure to finding the relevance of frequent items from the real dataset as well as increasing the overall performance.
... El diseño del producto se basa en las preferencias y necesidades de los consumidores, pero también en la viabilidad de obtener ganancias con el producto. muchos modelos y algoritmos de inteligencia computacional y análisis multicriterio se han aplicado al dominio del diseño del pro ducto, contribuyendo en gran medida a su formalización e infor matización (Kusiak y Salustri, 2007;Figueroa et al., 2019). Estos modelos se han implementado en sistemas de soporte de decisio nes de marketing que ayudan a tomar decisiones complicadas so bre el diseño de productos y el marketing. ...
... And most importantly, the manually collected data cannot cover most of the product images [12]. Hence, it is difficult to grasp the current product design trend and design concept with only a small number of product data, resulting in the appearance of the same design and bad law consequences [15]. What's more, it not only limits the speed and quality of product development but also increases the cost and risk of design innovation. ...
Existing product images are very important references for designing a new scheme. However, the designers have to collect and organize the product image data manually without proper tools, which may be time-consuming, inefficient and expensive. The rapid growth of product design has called for a smart system to assist designers with a quick start in designing a new product. Therefore, we propose an advanced designing assistant system (ADAS) to help the designers handle the large-volume product images more efficiently and create better design. The ADAS utilizes big data and artificial intelligence technology to achieve mass product data acquisition, analysis, retrieval, and design scheme generation. The ADAS utilizes builds a product image dataset firstly to decrease high cost of time and money in images collection task. Furthermore, based on this dataset, the ADAS develops three applications: (1) image retrieval and infringement analysis, (2) multi-label semantic annotation, (3) automatic design scheme generation. Experiments are conducted to validate the merits of the proposed system. And the results show that the ADAS could support designers with high quality from initial data collection to image retrieval, infringement analysis, semantic learning, and design scheme generation throughout the entire flow of the design task, greatly shortening the design period and improving efficiency.
... A review article surveyed the computational intelligence techniques for new product development where computational intelligence was one of the mainstream approaches for machine learning (Kusiak and Salustri 2007). In the article, survey data was used by computational intelligence techniques for new product development. ...
Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today's competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design.
