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In order to improve the robustness to noise in point cloud plane fitting, a combined model of improved Cook’s distance (ICOOK) and WTLS is proposed by setting a modified Cook’s increment, which could help adaptively remove the noise points that exceeds the threshold.

This paper proposes a robust point cloud plane fitting method based on ICOOK and WTLS to improve the robustness to noise in point cloud fitting. The ICOOK to denoise the initial point cloud was set and verified with experiments. In the meanwhile, weighted total least squares method (WTLS) was adopted to perform plane fitting on the denoised point cloud set to obtain the plane equation.

(a) A threshold-adaptive Cook’s distance method is designed, which can automatically match a suitable threshold. (b) The ICOOK is fused with the WTLS method, and the simulation experiments and the actual fitting of the surface of the DD motor are carried out to verify the actual application. (c) The results shows that the plane fitting accuracy and unit weight variance of the algorithm in this paper are substantially enhanced.

The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed. The existing point cloud plane fitting methods are not robust to noise, so a robust point cloud plane fitting method based on a combined model of ICOOK and WTLS is proposed.

A common pipeline of apparel design and simulation is adjusting 2D apparel patterns, putting them onto a virtual human model and performing 3D physically based simulation. However, manually adjusting 2D apparel patterns and performing simulations require repetitive adjustments and trials in order to achieve satisfactory results. To support future made-to-fit apparel design and manufacturing, efficient tools for fast custom design purposes are desired. The purpose of this paper is to propose a method to automatically adjust 2D apparel patterns and rapidly generate acustom apparel style for a given human model.

The authors first pre-define a set of constraints using feature points, feature lines and ease allowance for existing apparels and human models. The authors formulate the apparel fitting to a human model, as a process of optimization using these predefined constraints. Then, the authors iteratively solve the problem by minimizing the total fitting metric.

The authors observed that through reusing existing apparel styles, the process of designing apparels can be greatly simplified. The authors used a new fitting function to measure the geometric fitting of corresponding feature points/lines between apparels and a human model. Then, the optimized 2D patterns are automatically obtained by minimizing the matching function. The authors’ experiments show that the authors’ approach can increase the reusability of existing apparel styles and improve apparel design efficiency.

There are some limitations. First, in order to achieve interactive performance, the authors’ current 3D simulation does not detect collision within or between adjacent apparel surfaces. Second, the authors’ did not consider multiple layer apparels. It is non-trivial to define ease allowance between multiple layers.

The authors use a set of constraints such as ease allowance, feature points, feature lines, etc. for existing apparels and human models. The authors define a few new fitting functions using these pre-specified constraints. During physics-driven simulation, the authors iteratively minimize these fitting functions.

In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer physically fitting products. Digital product fitting is an emerging operational practice in retail that uses digital models of products and customers to match product supply to customer requirements. This paper aims to explore potential supply chain outcomes of digitalizing the operational practice of product fitting. The purpose is to explore and propose the potential of the practice to improve responsiveness to customer requirements and the utilization of existing variety in mass-produced products.

A maturity model of product fitting is developed to specify three levels of digitalization and potential outcomes for each level. Potential outcomes are developed based on empirical data from a case survey of three technology-developing companies, 13 retail cases and a review of academic literature.

With increasing maturity of digital product fitting, the practice can be used for more purposes. Besides matching product supply to customer demand, the practice can improve material flows, customer relationship management, assortment planning and product development. The practice of digital product fitting is most relevant for products where the final product configuration is difficult to make to order, product and customer attributes are easily measurable and tacit knowledge of customers and products can be formalized using digital modeling.

Potential outcomes are conceptualized and proposed. Further research is needed to observe actual outcomes and understand the mechanisms for both proposed and surprising outcomes in specific contexts.

The maturity model helps companies assess how their operations can benefit from digital product fitting and the efforts required to achieve beneficial outcomes.

This paper is a first attempt to describe the potential outcomes of introducing digital product fitting in retail supply chains.

The purpose of this paper is to present an efficient algorithm for multi-layer garment fitting simulation based on the geometric method to solve the low time cost problem during penetration detection and processing. This is more practical to design a CAD system to preview the multi-layer garment fitting effect in daily life.

The construction of a multi-layer garment based on existing 3D garments is a suitable method because this method is similar to the daily method of multi-layer dressing. The major problem is the penetration phenomenon between different garments because these 3D garment’s geometric shapes are constructed in different situations. In this paper, an efficient algorithm of multi-layer garment simulation is reported. A face-face intersection detection algorithm is designed to detect the penetration region between multi-layer garments fast and a geometric penetration processing algorithm is presented to solve the penetration phenomenon during multi-layer garment simulation.

This method can quickly detect the penetration between faces, and then deal with the penetration for multi-layer garment construction. Experimental results show that this method can not only remove the penetration but basically maintain the trend of wrinkles efficiently. At the same time, the garments used in the experiment have almost more than 5,800 faces, but the resolving time is under five seconds.

The main originalities of the multi-layer garment virtual fitting algorithm based on the geometric method are highly efficient both in terms of time cost and fitting effect. Based on this method, the technology of multi-layer garment virtual fitting can be used to design a novel CAD system to preview the multi-layer garment fitting effect in real time. This is a pressing requirement of virtual garment applications.

Fitting evenness is one key characteristic for three-dimensional objects' optimal fit. The weighted Gaussian imaging method is developed for fitting evenness of auto body taillight fitting optimization.

Fitting boundary contours are extracted from scanning data points. Optimal fitting target is represented with gap and flushness between taillight and auto body. By optimizing the fitting position of the projected boundary contours on the Gaussian sphere, the weighted Gaussian imaging method accomplishes optimal requirements of gap and flushness. A scanning system is established, and the fitting contour of the taillight assembly model is extracted to analyse the quality of the fitting process.

The proposed method accomplishes the fitting optimization for taillight fitting with higher efficiency.

The weighted Gaussian imaging method is used to optimize the taillight fitting. The proposed method optimized the fitting objects' 3-D space, while the traditional fitting methods are based on 2-D algorithm. Its time complexity is O(n3), while those of the traditional methods are O(n5). The results of this research will enhance the understanding of the 3-D optimal fitting and help in systematically improving the productivity and the fitting quality in automotive industry.

This paper seeks to propose an auto body taillight assembly model using finite element analysis. Fitting variation induced by tighten‐up sequence is analyzed by the model to control the dimensional quality of auto body.

The taillight assembly model is constructed with finite elements to depict the assembly process of the taillight. The validity of the simulation model is proved by the consistence between the pressures obtained from the finite elements simulation results and the pressure sensitive paper. The fitting variation induced by tighten‐up sequences is analyzed with the proposed model. GAGE R&R method is employed to check the significant difference among four different sequences and a rational conclusion is obtained.

The proposed finite element model could be used for taillight fitting quality control. The results have shown that as far as the car taillight assembly structure discussed in the paper is concerned, the taillight final fitting quality has nothing to do with the tighten‐up sequence of connecting bolts.

The taillight assembly model is first constructed with finite elements to depict the assembly process of the taillight. For the first time, GAGE R&R method is employed to check the significant difference among the fitting qualities obtained by different sequences. The results of this research will enhance the understanding of the optimal fitting of car taillight, and help to systematically improve the productivity and the fitting quality in the automotive industry.

Cylindrical components are common in industry assembly areas. It is necessary to obtain their precise positions and orientations for their assemblies. But some measurement approaches relying on measuring targets are not allowed, as they may not meet the efficiency requirement of on-line measurement or may cause surface damages to the components. Thus, this paper aims to provide a precise on-line non-target scanning method based on 3D vision.

First, a laser profile sensor is used to acquire point cloud of the side surface of the measured cylindrical component. Then a composite process is conducted to estimate the pose and position of the axis. Aiming at this purpose, two fitting approaches, i.e., axis fitting and generatrix fitting, are tried respectively to estimate the pose parameters from the point cloud.

The results of Monte Carlo simulations demonstrate that neither the axis fitting nor the generatrix fitting could solely obtain the needed accuracy and precisions roundly. Thus, a new synthesis method is presented. And the results of prototype experiments validate the excellent accuracy and precision of the synthesis method.

This proposed new synthesis method combines the advantages of both the above fitting methods and can be easily integrated into the assembly line to guide the automation assembly process of the cylindrical components precisely.

This paper investigate how fit uncertainty impacts product return costs in online retailing and how digital product fitting, a pre-sales fitting practice, can reduce fit uncertainty.

The paper analyzes the current performance of a retailer's e-commerce and return operations by estimating costs generated by product returns, including product handling costs, tied-up capital, inventory holding costs, transportation costs, and order-picking costs. The estimated costs were built on 2,229 return transactions from a Scandinavian fashion footwear retailer. A digital product fitting technology was tested with the retailer’s products and resulted in estimations on how such technology could affect product returns.

The cost of a return is approximately 17% of the prime cost. The major cost elements are product handling costs and transportation costs, which together amount to 72% of the total costs. If well calibrated, the fitting technology can cut fit-related return costs by up to 80%. The findings show how customers reacted to the fitting technology: it was unable to verify fit every time, but it serves as a useful and effective support tool for customers when placing orders.

Virtual fit verification using digital product fitting is key to retailers to reduce fit-related returns. Digital product fitting using three-dimensional scanning is more appropriate for some products, but it is unsuitable for products that are difficult to measure and scan.

The paper contributes an empirical estimate of retail supply chain costs associated with fit uncertainty, as well as theoretical understanding of the role of pre-sales fit verification in avoiding product returns.

The purpose of this paper is to ascertain the effect of age, gender and educational level on customer evaluations of the design characteristics of fitting rooms/dressing rooms, such as size, levels of illumination, number of hangers, materials and opening types of doors in retail clothing stores. In the light of these results, the study aims to increase the satisfaction of the customers in retail stores.

These evaluations were analysed according to the demographic characteristics of consumers, such as age, gender and educational level. Since activities in fitting rooms require a certain level of privacy, the features that affected privacy were also considered in this study. The analysis was carried out with research designed for users of fitting rooms in Ankara, Turkey.

Results indicated that demographic characteristics of the customers affected their evaluation of fitting rooms. The statistically significant results between evaluations of customers and their demographic backgrounds were determined. In these analyses, problems emerged due to usage of fitting rooms. Most of the problems complained about were an insufficient number of hangers, lack of mirrors, lack of sitting units, small-sized rooms and poorly illuminated rooms. Moreover, the results showed that customers preferred a totally closed panel door for privacy.

This paper reveals a significant relationship between design characteristics of fitting rooms and customer evaluations of fitting rooms. The results of the study suggest that retailers and designers may be able to easily make stores more attractive for customers when installing fitting rooms.

This research aims to develop an effective and efficient algorithm for solving the curve fitting problem arising in automated manufacturing systems.

This paper takes curve fitting as an optimization problem of a set of data points. Expressing the data as a function will be very effective to the data analysis and application. This paper will develop the stochastic optimization method to apply to curve fitting. The proposed method is a combination optimization method based on pattern search (PS) and simulated annealing algorithm (SA).

The proposed method is used to solve a nonlinear optimization problem and then to implement it to solve three circular arc‐fitting problems of curve fitting. Based on the analysis performed in the experimental study, the proposed algorithm has been found to be suitable for curve fitting.

Curve fitting is one of the basic form errors encountered in circular features. The proposed algorithm is tested and implemented by using nonlinear problem and circular data to determine the circular parameters.

The developed machine vision‐based approach can be an online tool for measurement of circular components in automated manufacturing systems.