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The purpose of this paper is to present an optimal posture evaluation model to control the assembly gaps in aircraft wing assembly. The gaps between two mating surfaces should be strictly controlled in precision manufacturing. Oversizing of gaps will decrease the dimensional accuracy and may reduce the fatigue life of a mechanical product. To reduce the gaps and keep them within tolerance, the relative posture (orientation and position) of two components should be optimized in the assembly process.

Based on the step alignment strategy, i.e. preliminary alignment and refined alignment, the concept of a small posture transformation (SPT) is introduced. In the preliminary alignment, an initial posture is estimated by a set of auxiliary locating points, with which the components can be quickly aligned near each other. In the refined alignment, the assembly gaps are calculated and the formulation of the gaps with component posture is derived by the SPT. A comprehensive weighted minimization model with gap tolerance constraints is established for redistributing the gaps in multi-regions. Powell-Hestenes-Rockafellar optimization, Singular Value Decomposition and K-Dimensional tree searching are introduced for the solution of the optimal posture for localization.

Using the SPT, the trigonometric posture transformation is linearized, which benefits the iterative solution process. Through the constrained model, overall gaps are minimized and excess gaps are controlled within tolerance.

This method has been tested with simulated model data and real product data, the results of which have shown efficient coordination of mating components.

This paper proposed an optimal posture evaluation method for minimizing the gaps between mating surfaces through component adjustments. This will promote the assembly automation and variation control in aircraft wing assembly.

Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.

First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.

During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.

In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

Time to market is the essential aim of any new product introduction process. Performance measures are simple quantities that indicate the state of manufacturing organisations and are used as the basis of decision‐making at this crucial early stage of the process. Fuzzy set theory is a method for using qualitative data and subjective opinion. Fuzzy sets have been used extensively in manufacturing for applications including control, decision‐making, and estimation. Type‐2 fuzzy sets are a novel extension of type‐1 fuzzy sets. Aims to examine this subject.

This research explores the increased use of type‐2 fuzzy sets in manufacturing. In particular, type‐2 fuzzy sets are used to model “the words that mean different things to different people”.

A model that can leverage design process knowledge and predict time to market from performance measures is a potentially valuable tool for decision making and continuous improvement. A number of data sources, such as process maps, from previous research into time to market in a high technology products company, are used to structure and build a type‐2 fuzzy logic model for the prediction of time to market.

This paper presents a demonstration of how the type‐2 fuzzy logic model works and provides directions for further research into the design process for time to market.

This work reports on a developing method time measurement system for measuring manufacturing and assembly processes automatically. This automatic system enables the production engineers and management to detect, process, and display concise and accurate information about the operations in real time.

This system is based on Internet of things technology and RFID-antenna. This methodology consists of seven main steps and one final optimization step. Mainly, the operator is equipped by RFID reader, and the work station tools and devices are provided by RFID tags. Responding the RFID tags to the reader will refer to the certain operations, the difference time between start and end of the operations will be collected immediately and calculated by the microprocessor of the system.

This automatic system is promising, considering the accurate time measurements and recommendations that obtained from the case study which includes measuring manual assembly operations to be followed in order to overcome the limitations which are not only technical but also managerial, legal and organizational.

The acquired data about timing and duration of individual operations are anonymized to guarantee the compliance with respect to the privacy laws (GDPR and Italian work's laws).

This work presents a unique system to measure the time instead of traditional methods in the factories environment and satisfies the requirements to study the recommendations in order to overcome the challenges.

The purpose of this literature review paper is to explore the concept of organization agility and its relevance in today's dynamic business environment. By conducting an in-depth review of existing academic and industry literature on organization agility, this study aims to identify the key factors that influence an organization's agility and the benefits and drawbacks associated with fostering agility.

Through the technique of bibliometric analysis, we provide the growth trajectory of the field by identifying the publication trends, prominent authors and countries and most prolific journal publishing in the concerned domain. We also provide the intellectual structure of the organization agility research by identifying the prominent themes that have been worked upon till date. In addition, with the backing of the theories, contexts, characteristics and methodology (TCCM) framework, we identify the most frequently applied theories, constructs and methods in organization agility research and provide new avenues for future research by analyzing the most frequently used theories, methods, constructs and research contexts.

With the ever-increasing ambiguity and need for change (why), organization agility serves as the organization's backbone. It acts as a springboard for the organization, an anchor point that remains constant while other functional aspects constantly fluctuate and change. Organization agility can be defined (what) as the ability of organizations to quickly respond to market needs by sensing, renewing, adapting and succeeding in a turbulent market. To summarize, organizational agility matters at three fundamental aspects (where): strategic level or the market capitalizing level, internal operational level and individual level.

This paper is unique in the sense that it is the first comprehensive literature review in the field of organization agility research to use a hybrid methodology (bibliometric review with TCCMs).

To investigate the influence of different process parameters of the laminated object manufacturing (LOM) process on the roughness of vertical surfaces along Z‐axis on ZX‐plane of parts produced by LOM.

The process parameters tested were layer thickness, heater temperature, platform retract, heater speed, laser speed, feeder speed and platform speed. A typical test part has been used, and matrix experiments were carried out based on Taguchi design. Optimal process parameter values were identified and finally, a regression model was applied onto the experimental results, and compared with bibliography models, using arbitrary experiments.

The statistical analysis of the experimental results showed that the surface roughness depends mainly on the heater temperature, layer thickness, and laser speed. Moreover, the regression model gave good predictions when heater temperature values were within the initial experimental area and inaccurate predictions when heater temperature takes the value 200°C.

Future work should involve extensive matrix experiments using parameters such as dimensions of test part (X_max, Y_max, Z_max), hatch spacing in X and Y directions, and delay time between sequential layers.

Using the extracted regression model, vertical surface roughness can be predicted and selected proprietary process parameter values. This means minimization of post processing time, easier disengagement between supporting frame and part, easier decubing, process optimization, and less finishing.

This methodology could be easily applied on different materials and initial conditions for optimisation of LOM processes.

This study aims to propose a novel method based on model learning with sparsity inducing norms for estimating dynamic gravity terms of the serial manipulators. This method is realized by operating the robot, acquiring data and filtering the features in signal acquisition to adapt to the dynamic gravity parameters.

The core principle of the method is to analyze the dictionary composition of the basis function of the model based on the dynamic equation and the Jacobian matrix of an arm. According to the structure of the basis function and the sparsity of the features, combined with joint-angle and driving-torque data acquisition, the effective features of dynamic gravity parameters are screened out using L1-norm optimization and learning algorithms.

The theoretical analysis revealed that training data obtained based on joint angles and driving torques could rapidly update dynamic gravity parameters. The simulation experiment was carried out by using the publicly available robot model and compared with the previous disassembly method to evaluate the feasibility and performance. The real 7-degree of freedom (DOF) industrial manipulator was used to further discuss the effects of the feature selection. The results show that this estimation method can be fully operational and efficient in industrial applications.

This approach is applicable to most serial robots with multi-DOF and the dynamic gravity parameters of the robot are estimated through learning and optimization. The method does not require prior knowledge of the robot arm structure and only requires joint-angle and driving-torque data acquisition under low-speed motion. Furthermore, as it is a data-driven-based method, it can be applied to gravity parameters updating.

Different from previous general robot dynamic modelling methods, the sparsity of the analytical form of dynamic equations was exploited and model learning was formulated as a convex optimization problem to achieve effective gravity parameters screening. The novelty of this estimation approach is that the method does not only require any prior knowledge but also does not require a specifically designed trajectory. Thus, this method can avoid the laborious work of parameter calibration and the induced modelling errors. By using a data-driven learning approach, the new parameter updating process can be completed conveniently when the robot carries additional mass or the end-effector changes for different tasks.

To investigate laminated object manufacturing (LOM) process quality, using a design of experiments approach.

The quality characteristics measured were in‐plane dimensional accuracy, actual layer thickness (ALT), and mean time per layer. The process parameters tested were nominal layer thickness (LT), heater temperature (HT), platform retract (PR), heater speed (HS), laser speed (LS), feeder speed (FS) and platform speed (PS). A typical test part has been used, and matrix experiments were carried out based on Taguchi design. Optimal process parameter values were identified and finally, additive and regression models were applied to the experimental results and tested using evaluation experiments.

The statistical analysis of the experimental results shows that error in X direction was higher than error in Y direction. Dimensional accuracy in X direction depends mainly on the HS (89 percent) and HT (5 percent), and in Y direction on HS (50 percent), LT (31 percent), LS (9 percent), PS (6 percent), and HT (3 percent). On the other hand, ALT depends mainly on the nominal ALT (96 percent), HS (2 percent), HT (1 percent), and PR (1 percent). Finally, mean time per layer depends mainly on HS (59 percent), LS (17 percent), FS (17 percent), and PS (4 percent).

Future work should involve extensive matrix experiments using parameters such as dimensions of test part (X_max, Y_max, Z_max), hatch spacing in X and Y directions, and delay time between sequential layers.

Using the extracted models, the quality of LOM parts can be predicted and appropriate process parameter values selected. This means minimization of post processing time, easier disengagement between supporting frame and part, easier decubing, process optimization, less finishing and satisfactory final LOM parts or tools. Also, ALT prediction and mean time per layer analysis could be used to improve LOM build time predictions.

The above analysis is useful for LOM users when predictions of part quality, paper consumption, and build time are needed. This methodology could be easily applied to different materials and initial conditions for optimisation of other LOM‐type processes.

The purpose of this study is to test a flexible polymer with different characteristics compared to other classical polymers mostly used in the additive manufacturing process, and to improve its mechanical properties and microstructure, by modifying different printing parameters, to make it more suitable for various industrial applications.

Seven parameters were tested, namely, nozzle temperature, bed temperature, layer thickness, printing speed, flow rate, printing time gap between two successive printed layers and raster orientation. Rheological characterizations were conducted to evaluate the influence of nozzle temperature on the melt viscosity of thermoplastic polyurethane (TPU). The effect of thermal printing parameters on the crystallinity behavior was explored. Tomographic characterizations were realized to measure the porosity and evaluate the internal structure quality of printed specimens.

Increases of the nozzle temperature, bed temperature, layer thickness and flow rate had a positive influence on the tensile strength properties of TPU with a reduction of porosity. Higher printing speeds created defects and negatively influenced the strength properties of TPU. An increase in the printing time gap between layers led to poor interlayer adhesion and decreased the tensile strength. Specimens with layers all oriented parallel to the loading direction exhibited superior mechanical properties compared to other raster orientations.

Thermoplastic elastomers are a unique class of polymers characterized by the combined thermal, chemical and mechanical properties of their elastomer and thermoplastic parts. TPU elastomer, as one of the elastomer families, has found an important position in the bioengineering and three-dimensional printing industry. This study reports a comprehensive study of the impact of additive manufacturing parameters on the properties of TPU.

An issue when printing multi-material objects is understanding how different materials will perform together, especially because interfaces between them are always created. This paper aims to address this interface from a mechanical perspective and evaluates how it should be designed for a better mechanical performance.

Different interface mechanisms were considered, namely, microscopic interfaces that are based on chemical bonding and were represented with a U-shape interface; a macroscopic interface characterized by a mechanical interlocking mechanism, represented by a T-shape interface; and a mesoscopic interface that sits between other interface systems and that was represented by a dovetail shape geometry. All these different interfaces were tested in two different material sets, namely, poly (lactic acid)–poly (lactic acid) and poly (lactic acid)–thermoplastic polyurethane material pairs. These two sets represent high- and low-compatibility materials sets, respectively.

The results showed, despite the materials’ compatibility level, multi-material objects will have a better mechanical performance through a macroscopic interface, as it is based on a mechanical interlocking system, of which performance cannot be achieved by a simple face-to-face interface even when considering the same material.

The paper investigates the importance of interface design in multi-material 3D prints by fused filament fabrication. Especially, for parts intended to be subjected to mechanical efforts, simple face-to-face interfaces are not sufficient and more robust and macroscopic-based interface geometries (based on mechanical interlocking systems) are advised. Moreover, such interfaces do not raise esthetic problems because of their working principle; the 3D printing technology can hide the interface geometries, if required.