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Review Article

J. Comput. Inf. Sci. Eng. 2018;19(1):010801-010801-19. doi:10.1115/1.4041087.

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):010802-010802-16. doi:10.1115/1.4041476.

Computer-aided tolerancing (CAT) aims to predict and control geometrical and dimensional deviations in the early design stage. Former simulation models based on the translation and rotation of nominal features cannot fulfill engineering demands or cover the product lifecycle. Nonideal feature-based simulation methods are, therefore, drawing a great deal of research attention. Two general problems for non-ideal feature-based methods are how to simulate manufacturing defects and how to integrate these defects into tolerance analysis. In this paper, we focus on the first problem. There are already many manufacturing defect simulation methods. Although they are derived from different fields and have different names, they share common characteristics in application. In this study, we collected different simulation methods and classified them as random noise methods, mesh morphing methods, and mode-based methods. The theoretical backgrounds of these methods are introduced, and the simulation examples are conducted on a consistency model to show their differences. Criteria such as multiscale, surface complexity, measurement data integration, parametric control, and calculation complexity are proposed to compare these methods. Based on these analyses, the advantages and drawbacks of each method are pointed out, which may help researchers and engineers to choose suitable methods for their work.

Commentary by Dr. Valentin Fuster

Research Papers

J. Comput. Inf. Sci. Eng. 2018;19(1):011001-011001-12. doi:10.1115/1.4041475.

Analyzing product online reviews has drawn much interest in the academic field. In this research, a new probabilistic topic model, called tag sentiment aspect models (TSA), is proposed on the basis of Latent Dirichlet allocation (LDA), which aims to reveal latent aspects and corresponding sentiment in a review simultaneously. Unlike other topic models which consider words in online reviews only, syntax tags are taken as visual information and, in this research, as a kind of widely used syntax information, part-of-speech (POS) tags are first reckoned. Specifically, POS tags are integrated into three versions of implementation in consideration of the fact that words with different POS tags might be utilized to express consumers' opinions. Also, the proposed TSA is one unsupervised approach and only a small number of positive and negative words are required to confine different priors for training. Finally, two big datasets regarding digital SLR and laptop are utilized to evaluate the performance of the proposed model in terms of sentiment classification and aspect extraction. Comparative experiments show that the new model can not only achieve promising results on sentiment classification but also leverage the performance on aspect extraction.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011002-011002-12. doi:10.1115/1.4041226.

This paper addresses some important theoretical issues for constrained least-squares fitting of planes and parallel planes to a set of points. In particular, it addresses the convexity of the objective function and the combinatorial characterizations of the optimality conditions. These problems arise in establishing planar datums and systems of planar datums in digital manufacturing. It is shown that even when the set of points (i.e., the input points) are in general position, (1) a primary planar datum can contact 1, 2, or 3 input points, (2) a secondary planar datum can contact 1 or 2 input points, and (3) two parallel planes can each contact 1, 2, or 3 input points, but there are some constraints to these combinatorial counts. In addition, it is shown that the objective functions are convex over the domains of interest. The optimality conditions and convexity of objective functions proved in this paper will enable one to verify whether a given solution is a feasible solution, and to design efficient algorithms to find the global optimum solution.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011003-011003-19. doi:10.1115/1.4041474.

Utilizing the enterprise capital related the knowledge of design processes has become crucial to improve enterprise agility and respond to shifts or changes in markets. The complexity and uncertainty of design raise the challenge of capturing tacit knowledge and the ability to aid in designing design processes. In this paper, ontology is proposed for capturing, representing, and documenting the knowledge related to hierarchical decision workflows in the meta-design of complex engineered systems. The ontology is developed in the context of decision support problem technique (DSPT), considering the requirements being able to guide assistance in designing design workflows, and integrating problem, product, and process information in a design decision-making process. Then, the approach for building procedure of process templates is presented to facilitate the reuse of the populated template instances in future design. Finally, the meta-design of the heat exchanger in a small thermal system is presented as an example to illustrate the effectiveness of this approach.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011004-011004-10. doi:10.1115/1.4041704.

The paper describes the design of a wearable and wireless system that allows the real-time identification of some gestures performed by basketball players. This system is specifically designed as a support for coaches to track the activity of two or more players simultaneously. Each wearable device is composed of two separate units, positioned on the wrists of the user, connected to a personal computer (PC) via Bluetooth. Each unit comprises a triaxial accelerometer and gyroscope, a microcontroller, installed on a TinyDuino platform, and a battery. The concept of activity recognition chain is investigated and used as a reference for the gesture recognition process. A sliding window allows the system to extract relevant features from the incoming data streams: mean values, standard deviations, maximum values, minimum values, energy, and correlations between homologous axes are calculated to identify and differentiate the performed actions. Machine learning algorithms are implemented to handle the recognition phase.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011005-011005-13. doi:10.1115/1.4041705.

Optimal layouts for structural design have been generated using topology optimization approach with a wide variety of objectives and constraints. Minimization of compliance is the most common objective but the resultant structures often have stress concentrations. Two new objective functions, constructed using an upper bound of von Mises stress, are presented here for computing design concepts that avoid stress concentration. The first objective function can be used to minimize mass while ensuring that the design is conservative and avoids stress concentrations. The second objective can be used to tradeoff between maximizing stiffness versus minimizing the maximum stress to avoid stress concentration. The use of the upper bound of von Mises stress is shown to avoid singularity problems associated with stress-based topology optimization. A penalty approach is used for eliminating stress concentration and stress limit violations which ensures conservative designs while avoiding the need for special algorithms for handling stress localization. In this work, shape and topology are represented using a density function with the density interpolated piecewise over the elements to obtain a continuous density field. A few widely used examples are utilized to study these objective functions.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011006-011006-16. doi:10.1115/1.4041829.

In this paper, the authors present Chebyshev finite element (CFE) method for the analysis of Reissner–Mindlin (RM) plates and shells. Chebyshev polynomials are a sequence of orthogonal polynomials that are defined recursively. The values of the polynomials belong to the interval [1,1] and vanish at the Gauss points (GPs). Therefore, high-order shape functions, which satisfy the interpolation condition at the points, can be performed with Chebyshev polynomials. Full gauss quadrature rule was used for stiffness matrix, mass matrix and load vector calculations. Static and free vibration analyses of thick and thin plates and shells of different shapes subjected to different boundary conditions were conducted. Both regular and irregular meshes were considered. The results showed that by increasing the order of the shape functions, CFE automatically overcomes shear locking without the formation of spurious zero energy modes. Moreover, the results of CFE are in close agreement with the exact solutions even for coarse and irregular meshes.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011007-011007-7. doi:10.1115/1.4041706.

During product development one important aspect is the geometric robustness of the design. This is due to the fact that all manufacturing processes lead to products with variation. Failing to properly account for the variability of the process in the design phase may lead to expensive redesign. One important tool during the design phase in many industries is variation simulation, which makes it possible to predict and optimize the geometric quality of the design. However, despite the increase in computer power, calculation time is still an obstacle for the wider use of variation simulation. In this article, we propose a new method for efficient compliant variation simulation of spot-welded sheet metal assemblies. The method is exact, and we show that the method leads to time savings in simulation of approximately 40–50% compared to current state-of-the-art variation simulation.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011008-011008-13. doi:10.1115/1.4041776.

In this work, a soft competitive learning fuzzy adaptive resonance theory (SFART) diagnosis model based on multifeature domain selection for the single symptom domain and the single-target model is proposed. In order to solve the problem that the performance of traditional fuzzy ART (FART) is affected by the order of sample input, the similarity criterion of YU norm is introduced into the fuzzy ART network. In the meanwhile, the lateral inhibition theory is introduced to solve the wasteful problem of fuzzy ART mode node. By combining YU norm and lateral inhibition theory with fuzzy ART network, a soft competitive learning ART neural network diagnosis model that allows multiple mode nodes to learn simultaneously is designed. The feature parameters are extracted from the perspectives of time domain, frequency domain, time series model, wavelet analysis, and wavelet packet energy spectrum analysis, respectively. To further improve the diagnostic accuracy, the selective weighted majority voting method is integrated into the diagnosis model. Finally, the selected feature parameters are inputted to the integrated model to complete the fault classification and diagnosis. Finally, the proposed method is verified with a gearbox fault diagnosis test.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011009-011009-11. doi:10.1115/1.4041777.

The lightweight representation of three-dimensional computer-aided design (3D CAD) models has drawn much attention from researchers as its usefulness in collaborative product development is vast. Existing approaches are mostly based on feature depression or mesh-based simplification. In this article, a new approach for 3D CAD lightweight representation based on combining triangular mesh representation and boundary representation (B-rep) is proposed. The corresponding data structure as well as the conversion method from original data given in B-rep was developed. Considered as an essential application in collaborative product development, a case study on the visualization process of large-scale assembly models represented in the proposed lightweight representation was also conducted. The validation of the approach was performed via experiments with 3D CAD models in SAT format and by benchmarking with the conventional all-faceted approach with the same level of mesh resolution.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2018;19(1):011010-011010-11. doi:10.1115/1.4041418.

User requirements play an important role in product design activities. Customer satisfaction has a direct bearing on the acquisition of user requirements for product design. However, these implicit requirements are equipped with the attributes of potentiality, fuzziness, and subjectivity. In this paper, a new implicit user requirement processing method based on a cloud service platform is proposed to resolve the difficulty of acquiring implicit requirements. Initially, this method collects user requirement data via a metaphor extraction technique using a cloud service platform. Then, the requirement data are clustered and mapped with product attributes. Finally, the mapping results are visualized to intuitively instruct product design and optimization. Overall, the method is a user-centered innovation paradigm implemented on a cloud service platform to realize collaborative design and resource sharing. Finally, an application case is presented to illustrate the method, and the results indicate that the method is effective and could serve as a reference for product design.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Comput. Inf. Sci. Eng. 2018;19(1):014501-014501-5. doi:10.1115/1.4041566.

Fourier descriptor (FD)-based path synthesis algorithms for generation of planar four-bar mechanisms require assigning time parameter values to the given points along the path. An improper selection of time parameters leads to poor fitting of the given path and suboptimal four-bar mechanisms while also ignoring a host of mechanisms that could be potentially generated otherwise. A common approach taken is to use uniform time parameter values, which does not take into account the unique harmonic properties of the coupler path. In this paper, we are presenting a nonuniform parametrization scheme in conjunction with an objective function that provides a better fit, leverages the harmonics of the four-bar coupler, and allows imposing additional user-specified constraints.

Commentary by Dr. Valentin Fuster

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