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The purpose of this paper is to present a novel Kriging meta-model assisted method for multi-objective optimal tolerance design of the mechanical assemblies based on the operating conditions under both systematic and random uncertainties.

In the proposed method, the performance, the quality loss and the manufacturing cost issues are formulated as the main criteria in terms of systematic and random uncertainties. To investigate the mechanical assembly under the operating conditions, the behavior of the assembly can be simulated based on the finite element analysis (FEA). The objective functions in terms of uncertainties at the operating conditions can be modeled through the Kriging-based metamodeling based on the obtained results from the FEA simulations. Then, the optimal tolerance allocation procedure is formulated as a multi-objective optimization framework. For solving the multi conflicting objectives optimization problem, the multi-objective particle swarm optimization method is used. Then, a Shannon’s entropy-based TOPSIS is used for selection of the best tolerances from the optimal Pareto solutions.

The proposed method can be used for optimal tolerance design of mechanical assemblies in the operating conditions with including both random and systematic uncertainties. To reach an accurate model of the design function at the operating conditions, the Kriging meta-modeling is used. The efficiency of the proposed method by considering a case study is illustrated and the method is verified by comparison to a conventional tolerance allocation method. The obtained results show that using the proposed method can lead to the product with a more robust efficiency in the performance and a higher quality in comparing to the conventional results.

The proposed method is limited to the dimensional tolerances of components with the normal distribution.

The proposed method is practically easy to be automated for computer-aided tolerance design in industrial applications.

In conventional approaches, regardless of systematic and random uncertainties due to operating conditions, tolerances are allocated based on the assembly conditions. As uncertainties can significantly affect the system’s performance at operating conditions, tolerance allocation without including these effects may be inefficient. This paper aims to fill this gap in the literature by considering both systematic and random uncertainties for multi-objective optimal tolerance design of mechanical assemblies under operating conditions.

The purpose of this paper is to study the impact of high‐power microwave interferences on electronic devices needs the simulation of semiconductor components. Although the semiconductor equations can be solved numerically by Newton method, the conventional Newton method exhibits significant speed degradation when the power of interference is high enough to cause device burnout. Therefore, this paper aims at speeding up the simulation of the semiconductor components under high‐power microwave interferences.

Some approximations in conventional Newton method works efficiently only when the electric field in the simulated semiconductor is relatively low. This is the reason of the formerly mentioned speed degradation problem. The proposed method speeds up the simulation by modifying these approximations to acquire enough precision in these high‐power situations.

The modified Newton method proposed in this paper shows an acceleration of 100‐150 percent compared to conventional method for typical applications. Moreover, the simulation speed becomes nearly independent of the power of the microwave interferences, which means the speed degradation phenomenon of the conventional method has almost been eliminated.

This paper proposes a modified Newton method to speed up the simulation of the semiconductor components under high‐power microwave interferences.

The construction industry has actively attempted to tackle the low-productivity issues arising from inefficient construction planning. It is imperative to understand how construction practitioners perceive technology integration in construction planning in light of emerging technologies. This study intended to uncover unique experimental findings by integrating 4D-building information modelling (BIM) to virtual reality (VR) technology during construction planning among construction professionals at light steel framing (LSF) projects.

The building industry participants were invited to provide inputs on two different construction planning methods: conventional and innovative methods. The conventional method involved the participants using traditional platforms such as 2D computer-aided design (CAD) and physical visualisation of paper-based construction drawings for the LSF assembly process with a Gantt Chart tool to complete construction planning-related tasks for the targeted project. Comparatively, participants are required to perform the same tasks using more innovative platforms like 4D-BIM in a VR environment.

A Charrette Test Method was used to validate the findings, highlighting an improvement in usability (+10.3%), accuracy (+89.1%) and speed (+30%) using 4D BIM with VR compared to the conventional paper-based method. The findings are also validated by a paired t-test, which is supported by the rationality of the same findings. This study posits positive results for construction planning through the utilisation of modern practices and technologies. These findings are significant for the global construction industry facing low productivity issues, delays and certainty in terms of building delivery timelines due to poor construction planning.

This new blend of technologies—combining 4D BIM and VR in industrialised construction projects—potentially directs future initiatives to drive the efficiency of construction planning in the building lifecycle. The interactive BIM-based virtual environment would purposefully transform construction planning practices in order to deliver modern and more certain building construction methods with a focus on prefabrication processes.

Sensorless passive lead-through programming (LTP) is a promising physical human-robot interaction technology that enables manual trajectory demonstrations based on gravity and friction compensation. The major difficulty lies in static friction compensation during LTP start-up. Instead of static friction compensation, conventional methods only compensate for Coulomb friction after the joint velocity exceeds a threshold. Therefore, conventional start-up external torques must overcome static friction. When the static friction is considerable, it is difficult for conventional LTP to start up and make small movements. This paper aims to decrease the start-up external torque and improve the small movement performance.

This paper reveals a novel usage of a high-gain position-loop in industrial robot applications aimed at sensitively detecting external torque during start-up. Then, the static friction is partly compensated by Coulomb friction to facilitate start-up. In addition, a detailed transition method between the proposed start-up and conventional passive LTP is proposed based on a finite state machine.

Experiments are implemented on the ROKAE XB4 robot to verify the effectiveness of the proposed external torque detection. Compared with the conventional LTP method, the proposed LTP method significantly decreases the start-up external torque and facilitates small movements.

This paper proposes and verifies a novel start-up method of sensorless LTP based on a start-up external torque detection and a transition method between start-up and conventional LTP. This research improves the LTP start-up performance, especially for industrial robots with large static friction.

Shape optimization using the level-set method is one of the most effective automatic design tools for electromagnetic machines. While level-set method has the advantage of being able to suppress unfeasible shape, it has a weakness of being unable to handle complex topology changes such as perforate at material region. With this method, it is only possible to define simple connected topology, and it is difficult to determine the optimal shape which has holes. Therefore, it is important to efficiently expand the searching area in the optimization process with level-set method.

In this paper, the authors introduce the newly defined hole sensitivity which is based on concept of topological derivatives, and combine it with level-set method to effectively create holes in the search process. Furthermore, they consider a variable bandwidth of gray scale, which indicates the transition width between air and magnetic body and combine it with the hole creation method described above. With these methods, the authors aim to expand the searching area in comparison with the conventional level-set method.

As a result of applying the proposed methods to a magnetic shielding problem, the multi-layered shielding which effectively reduces the magnetic flux in the target area, is successfully produced.

The proposed methods enable us to effectively create a hole and to expand the searching area in the topology optimization process unlike in the case of conventional level-set method.

The purpose of this paper is to provide a way to analyze satellite images using various clustering algorithms and refined bitplane methods with other supporting techniques to prove the superiority of RIFCM.

A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems. Four images were selected dealing with hills, freshwater, freshwatervally and drought satellite images.

The superiority of the proposed algorithm, RIFCM with refined bitplane towards other clustering techniques with other supporting methods clustering, has been found and as such the comparison, has been made by applying four metrics (Otsu (Max-Min), PSNR and RMSE (40%-60%-Min-Max), histogram analysis (Max-Max), DB index and D index (Max-Min)) and proved that the RIFCM algorithm with refined bitplane yielded robust results with efficient performance, reduction in the metrics and time complexity of depth computation of satellite images for further process of an image.

For better clustering of satellite images like lands, hills, freshwater, freshwatervalley, drought, etc. of satellite images is an achievement.

The existing system extends the novel framework to provide a more explicit way to analyze an image by removing distortions with refined bitplane slicing using the proposed algorithm of rough intuitionistic fuzzy c-means to show the superiority of RIFCM.

The purpose of this paper is to identify critical equipment by dynamically ranking them in interval-valued intuitionistic fuzzy (IVIF) circumstances. Accordingly, the main drawbacks of the conventional failure mode and effects analysis (FMEA) are eliminated. To this end, the authors have presented the interval-valued intuitionistic fuzzy condition-based dynamic weighing method (IVIF-CBDW).

To realize the objective, the authors used the IVIF power weight Heronian aggregation operator to integrate the data extracted from the experts’ opinions. Moreover, the multi-attributive border approximation area comparison (MABAC) method is applied to rank the choices and the IVIF-CBDW method to create dynamic weights appropriate to the conditions of each equipment/failure mode. The authors proposed a robust FMEA model where the main drawbacks of the conventional risk prioritization number were eliminated.

To prove its applicability, this model was used in a case study to rank the equipment of a HL5000 crane barge. Finally, the results are compared with the traditional FMEA methods. It is indicated that the proposed model is much more flexible and provides more rational results.

In this paper, the authors have improved and used the IVIF power weight Heronian aggregation operator to integrate information. Furthermore, to dynamically weigh each equipment (failure mode), they presented the IVIF-CBDW method to determine the weight of each equipment (failure mode) based on its equipment conditions in the O, S and D criteria and provide the basis for the calculation. IVIF-CBDW method is presented in this study for the first time. Moreover, the MABAC method has been performed, to rank the equipment and failure mode. To analyze the information, the authors encoded the model presented in the robust MATLAB software and used it in a real sample of the HL5000 crane barge. Finally, to evaluate the reliability of the model presented in the risk ranking and its rationality, this model was compared with the conventional FMEA, fuzzy TOPSIS method, the method of Liu and the modified method of Liu.

Recommender systems are techniques that allow companies to develop sales and marketing and as a result, attract more customers. There are several different types of recommender systems which collaborative filtering (CF) method is more popular and is used in various fields. However, similar to other recommender systems, this system has its own limitations. Nowadays, recommender systems are combined with other systems to enhance the quality and precision. The purpose of this paper is to present a new method to increase the accuracy and quality of recommendations associated with filtering systems.

First, the recency, frequency, and monetary (RFM) variables of the clients are extracted and variables’ weights are calculated. Then, using weighted RFM and expectation maximization clustering algorithms and their combination with the closest K-neighbors, recommendations for each cluster is independently extracted. Finally, the results are compared with the outcome of conventional CF techniques. Remarkably, sale transactions of a big distribution and sale of goods centers are used in this study.

The results indicated that the proposed method has higher accuracy compared to the conventional CF method. Likewise, the clusters which have higher values were received more accurate recommendations. Another point was that the proposed method was faster on obtaining the results than the conventional method as the recommendations were performed with respect to the customers of the same cluster, while all clients were assessed in the conventional method and as a result, the calculation speed is reduced as the number of customers increases in this method.

The results indicated that the proposed method has higher accuracy compared to the conventional CF method. Likewise, the clusters which have higher values were received more accurate recommendations. This is very important for businesses and trade centers as more than 80 percent of their profits come from valued customers and hence, recommendations with higher accuracy to these valued customers lead to more profits to sales centers. Since the valued customers were calculated in the proposed method and the value of each customer was distinguished for sales representatives, the accomplished recommendations can be coordinated with sales’ strategies to make it more targeted.

The purpose of this paper is to present a new numerical technique, called adaptive level set method, for use with the finite element method.

A conventional level set method using the smeared Heaviside function has been employed for shape and topology optimizations. The smeared Heaviside function yields an indistinct interface boundary, and so can increase computational time and cause numerical errors. The adaptive level set method does not use the smeared Heaviside function. To coincide with the material interface, it processes the zero level as the boundary data of element meshing.

Usefulness and accuracy of shape optimization using the adaptive level set method are shown by comparison to the conventional level set method. A shape optimization procedure using the adaptive level set method is introduced. Numerical examples are employed to explain how the adaptive level set method is applied.

The adaptive level set method is proposed to relieve the interface problem of the conventional level set method. Shape variation in the optimization is calculated from the continuum sensitivity analysis.

Conventional level-set method tends to fall into local optima because optimization is conducted based on gradient method. The purpose of this paper is to develop a novel topology optimization in which simulated annealing (SA) is introduced to overcome the difficulties in level-set method.

Level-set based topology optimization for two-dimensional optimization problem.

It is shown in the numerical examples, where conventional and present methods are applied to shape optimization of ferrite inductor and Interior Permanent Magnetic (IPM)-motor, the present method can find solutions with better performance than those obtained by the conventional method.

SA is introduced to improve the search performances of level-set method.