Optimization Of Assembly Process Research Articles

Automotive Grade WLCSP for Radar Applications

Wafer-Level Chip Scale Packages (WLCSPs) are becoming commonplace in the industry due to their small form factor. Applications include industrial and automotive which demand high reliability performance. Additionally, WLCSPs may be superior in some implementations to other package options for RF performance in the mmWave spectrum, which is desired for automotive radar application. In this study the industry’s first auto grade 1 capable large WLCSP package. (~ 72 mm2 body size, 18x15 BGA array, 0.5 mm pitch) will be presented. It will be demonstrated how extensive design enhancements, predictive simulations, careful package materials and process selection and assembly process optimization studies are required to develop a robust package solution. Multiple assembly process experiments and corner studies were conducted to demonstrate robustness of WLCSP package solution to meet automotive grade quality and reliability requirements. Both component/unit level and board level reliability was evaluated as per AECQ100 standards. Robustness margins were confirmed through reliability testing performed up to 2-3x reliability requirements.

Implementation of Digital Twin in Actual Production: Intelligent Assembly Paradigm for Large-Scale Industrial Equipment

The assembly process of large-scale and non-standard industrial equipment poses significant challenges due to its inherent scale-related complexity and proneness to errors, making it difficult to ensure process cost, production cycle, and assembly accuracy. In response to the limitations of traditional ineffective production models, this paper aims to explore and propose a digital twin (DT)-based technology paradigm for the intelligent assembly of large-scale and non-standard industrial equipment, focusing on both the equipment structure and assembly process levels. The paradigm incorporates key technologies that facilitate the integration of virtual and physical information, including the establishment and updating of DT models for assembly structures using actual data, the assessment of structural assemblability based on DT models, the planning and simulation of assembly processes, and the implementation of virtual commissioning technology tailored to the actual assembly process. The effectiveness of the proposed paradigm is demonstrated through a case study involving the actual assembly of a large-scale aerodynamic experimental equipment. The results confirm its ability to provide valuable technical support for the design, evaluation, and optimization of industrial equipment assembly processes. By leveraging the DT-based methodological system proposed in this paper, significant improvements in the transparency and intelligence of industrial equipment production processes can be achieved.

Stress-based optimization of assembly surface mechanical properties oriented to stress-uniform assembly

Stress-uniform assembly is favorable for ensuring the precision and performance stability of precision electromechanical systems. Process compensation, assembly process optimization and assembly surface topography optimization are three typical ways to regulate the assembly stress distribution. Assembly surface mechanical properties optimization is a new and promising technique for achieving uniform stress distribution in assembled parts. This study takes that direction and investigates how variation in assembly surface material stiffness (i.e., Young's modulus) give rise to the variation in von Mises stress in assembled parts. To homogenize the von Mises stress distribution in assembled parts, the stress-uniform assembly-oriented material stiffness optimization model is constructed on the basis of finite element discretization. A stress-based material stiffness optimization method, which does not require sensitivity analysis, is developed by directly linking the amount of change in material stiffness to the value of von Mises stress. The feasibility and effectiveness of the stress-based assembly surface material stiffness optimization are demonstrated by a design case containing three elastic contact bodies, where both single-load and multi-loads conditions are considered. Some new phenomena during the assembly surface material stiffness optimization are also observed and explained.

Aero-Engine Rotor Assembly Process Optimization Based on Improved Harris Hawk Algorithm

Multi-stage disc rotor assembly is an important part of the aero-engine rotor manufacturing process. To solve the problem that excessive unbalance of assembly affects the vibration of the whole machine, this paper presents an optimization method for aero-engine rotor assembly balance based on an improved Harris Hawk algorithm. Firstly, the assembly sequence model of the single-stage disc blade and the phase assembly model of a multi-stage disc of the engine rotor is established. Secondly, by using the initial population generation based on dynamic opposing learning and the escape energy function of the non-linear logarithmic convergence factor, the search mechanism of the whale optimization algorithm is introduced in the global exploration, and the adaptive weight strategy and mutation strategy of the genetic algorithm is introduced in the development to improve the algorithm. Then, the effectiveness of the algorithm is verified by experiments and compared with particle swarm optimization, genetic algorithm, and Harris Hawk algorithm, the unbalance of the optimal blade assembly sequence is reduced by 91.75%, 99.82%, and 83.39%, respectively. The algorithm comparison and analysis are carried out for all disc-blade assembly optimization of the rotor. The optimal unbalance of the improved Harris Hawk optimization algorithm is reduced by 79.71%, 99.48%, and 54.92% on average. The unbalance of the algorithm in this paper is the best. Finally, the improved Harris Hawk algorithm is used to find the best assembly phase, and the optimized unbalanced force and moment are reduced by 84.22% and 98.05%, respectively. The results of this study prove that the improved Harris Hawk algorithm for aero-engine rotor assembly balance optimization can effectively reduce the unbalance of rotor disc blade assembly and rotor unbalance and provide a powerful solution for solving engine vibration.

Geometric modeling analysis method for stacking assembly deviation of aero engine rotor

Purpose Assembly errors of aeroengine rotor must be controlled to improve the aeroengine efficiency. However, current method cannot truly reflect assembly errors of the rotor in working state owing to difficulties in error analysis. Therefore, the purpose of this study is to establish an optimization method for aeroengine rotor stacking assembly. Design/methodology/approach The assembly structure of aeroengine rotor is featured. Rotor eccentricity is optimized based on Jacobian–Torsor model. Then, an optimization method for assembly work is proposed. The assembly process of the high-pressure compressor rotor and the high-pressure turbine rotor as the rotor core assembly is mainly considered. Findings An aeroengine rotor is assembled to verify the method. The results show that the predicted eccentricity differed from the measured eccentricity by 6.1%, with a comprehensive error of 8.1%. Thus, the optimization method has certain significance for rotor assembly error analysis and assembly process optimization. Originality/value In view of the error analysis in the stacking assembly of aeroengine rotor, an innovative optimization method is proposed. The method provides a novel approach for the aeroengine rotor assembly optimization and is applicable for the assembly of high-pressure compressor rotor and high-pressure turbine rotor as the rotor core assembly.

Application of Neural Networks for Water Meter Body Assembly Process Optimization

The proposed model of the neural network (NN) describes the optimization task of the water meter body assembly process, based on 18 selected production parameters. The aim of this network was to obtain a certain value of radial runout after the assembly. The tolerance field for this parameter is 0.2 mm. The repeatability of this value is difficult to achieve during production. To find the most effective networks, 1000 of their models were made (using various training methods). Ten NN with lowest errors of the output value prediction were chosen for further analysis. During model validation the best network achieved the efficiency of 93%, and the sum of squared residuals (SSR) was 0.007. The example of the prediction of the value of radial runout of machine parts presented in this paper confirms the adopted statement about the usefulness of the presented method for industrial conditions and is based on the analysis of hundreds of thousands of parametric and descriptive data on the process flow collected to create an effective network model.

Semi-Physical Simulation of Fan Rotor Assembly Process Optimization for Unbalance Based on Reinforcement Learning

An aero engine fan rotor is composed of a multi-stage disk and multi-stage blades. Excessive unbalance of the aero engine fan rotor after assembly is the main cause of aero engine vibration. In the rotor assembly process, blade sequencing optimization and multi-stage blade set assembly phase optimization are important for reducing the overall rotor unbalance. To address this problem, this paper proposes a semi-physical simulation method based on reinforcement learning to optimize the balance in the fan rotor assembly process. Firstly, based on the mass moments of individual blades, the diagonal mass moment difference is introduced as a constraint to build a single-stage blade sorting optimization model, and reinforcement learning is used to find the optimal sorting path so that the balance of the single-stage blade after sorting is optimal. Then, on the basis of the initial unbalance of the disk and the unbalance of the single-stage blade set, a multi-stage blade assembly phase optimization model is established, and reinforcement learning is used to find the optimal assembly phase so that the overall balance of the rotor is optimal. Finally, based on the collection of data during the assembly of the rotor, the least-squares method is used to fit and calculate the real-time assembly unbalance to achieve a semi-physical simulation of the optimization of balance during the assembly process. The feasibility and effectiveness of the proposed method are verified by experiments.

Multidimensional entropy evaluation of non-uniform distribution of assembly features in precision instruments

The non-uniform distribution of assembly features such as form error and stress has a significant influence on the accuracy and stability of precision instruments. Therefore, to improve assembly precision, non-uniformly distributed assembly features must be evaluated in terms of value non-uniformity as well as location non-uniformity. However, the current effective non-uniformity evaluation methods for assembly features only consider the value non-uniformity without considering the influence of the data location. This study proposes a multidimensional entropy evaluation method (MDEEM) and establishes multidimensional entropy evaluation indexes (MDEEIs) to evaluate the non-uniformity of assembly features in a plane region considering both their value and location. First, the mathematical model and MDEEIs for non-uniform data in a one-dimensional (1D) geometric space were developed and verified, wherein the concept of control distance was introduced and the information entropy theory was adopted. Then, the MDEEIs for non-uniform data in a two-dimensional (2D) geometric space were established and verified based on those in the 1D space. Subsequently, the MDEEM was proposed to demonstrate the non-uniformity evaluation process for assembly features in a 2D plane region. Finally, the proposed MDEEM was applied to reveal the distribution characteristics of two types of assembly features that mainly affect the assembly quality, namely form error and stress. This study provides an evaluation method for the non-uniform distribution of various assembly factors in a plane region and aids in the optimization of precision assembly processes.

Assembly process optimization of electromechanical meters based on robust design

The assembly precision of the magnetic system of an electromechanical meter is an important guarantee for the measurement accuracy of the meter. The large gap between the two poles of the magnetic system is the key point that affects the assembly precision and is also the difficulty that electromechanical meter manufacturers face. In this paper, the controllable factors that affect the gap of a magnetic system are selected by performing an orthogonal experiment, the assembly process is analyzed, and the noise factors are selected; the orthogonal design achieved by using inner and outer array is used to design the experiment scheme, and the optimal combination of process parameters with a large signal-noise ratio (SNR) and good robustness is found through the experiment. Through experimental verification, the improved process capability index reaches 1.34, and the optimization effect is remarkable.

Online performance optimization for complex robotic assembly processes

The variations and uncertainties in complex assembly processes could cause conventional industrial robots failure to perform assemblies. Therefore, intelligent robots must be developed which can adapt to these variations and uncertainties; moreover, the assembly process parameters must be optimized to satisfy the performance requirements such as First Time Throughput (FTT) rate and cycle time. However, it is challenging to optimize the performance of complex robotic assembly systems. The existing solutions are not efficient and human knowledge is needed to perform experiments and explore optimal parameters. These requirements greatly limit the applications of robotic assembly in manufacturing automation. In this paper, a robotic assembly process optimization method is developed for complex assembly processes. We propose a modeling method based on Gaussian Process Regression (GPR) to construct a model to build the relationship between the process parameters (input) and system performance (output). The GPR surrogated Bayesian Optimization Algorithm (GPRBOA) is improved to iteratively optimize the robotic assembly performance. Two industrial assembly processes (a tight-tolerance peg-in-hole assembly process and a torque converter assembly process) are used to verify the proposed method. The experiments were performed many times and the results demonstrate that the proposed GPRBOA method can optimize the complex assembly process parameters effectively and efficiently. The proposed complex assembly process optimization method opens a door for online manufacturing process optimization and will greatly reduce the manufacturing cost.

Designing highly selective and stable water transport channel through graphene oxide membranes functionalized with polyhedral oligomeric silsesquioxane for ethanol dehydration

Graphene oxide (GO) has opened a new era of membranes and shown exciting separation properties for molecules and ions. However, in terms of precise aqueous separations such as water/ethanol, how to enhance flux when designing GO membranes with high structural robustness and separation accuracy remains a pendent challenge. Herein, we report a facile and tunable way to tackle this issue via incorporating a kind of multifunctional polyhedral oligomeric silsesquioxane (POSS). After optimization of assembly process, the GO channels with low transport resistance, high separation accuracy and robust framework can be constructed, which arise from the rigid molecular-size siloxane cage, the hydrophilic-hydrophobic hybrid structure, and the affluent covalent cross-linking sites on POSS, respectively. As a result, the membrane achieves exceptionally high separation performance with a flux of 3.16 kg/m2 h and water/ethanol separation factor of 1303, which are 50 % and 31-fold higher than pristine GO membrane, and exhibits outstanding structural robustness in harsh operating conditions, such as high temperature, ultrahigh water concentration, acid/alkali environment and long-term operation. The strategy proposed here holds great potential to produce high-efficiency two-dimensional-material membranes for precise aqueous separations.

Optimization of distribution cabinet assembly process based on value stream mapping technology

Aiming at the problems of excessive work-in-process, unbalanced production, and unreasonable production methods in the assembly process of S company’s low-voltage switch gear A cabinet, the value stream mapping technology was used to propose an improvement plan for the assembly process, and finally the Flexsim simulation technology The optimization effect of the scheme was tested, and the simulation results showed that the production balance rate, delivery cycle, number of products in process, and fatigue strength of workers have all been effectively improved, which also reduced production costs for the company.

Towards Large-Scale Evaluation of Mental Stress and Biomechanical Strain in Manufacturing Environments Using 3D-Referenced Gaze and Wearable-based Analytics

In future manufacturing human-machine interaction will evolve towards flexible and smart collaboration. It will meet requirements from the optimization of assembly processes as well as from motivated and skilled human behavior. Recently, human factors engineering has substantially progressed by means of detailed task analysis. However, there is still a lack in precise measuring cognitive and sensorimotor patterns for the analysis of long-term mental and physical strain. This work presents a novel methodology that enables real-time measurement of cognitive load based on executive function analyses as well as biomechanical strain from non-obtrusive wearable sensors. The methodology works on 3D information recovery of the working cell using a precise stereo measurement device. The worker is equipped with eye tracking glasses and a set of wearable accelerometers. Wireless connectivity transmits the sensor-based data to a nearby PC for monitoring. Data analytics then recovers the 3D geometry of gaze and viewing frustum within the working cell and furthermore extracts the worker's task switching rate as well as a skeleton-based approximation of worker's posture associated with an estimation of biomechanical strain of muscles and joints. First results enhanced by AI-based estimators demonstrate a good match with the results of an activity analysis performed by occupational therapists.

Convex optimization techniques in compliant assembly simulation

A special class of quadratic programming (QP) problems is considered in this paper. This class emerges in simulation of assembly of large-scale compliant parts, which involves the formulation and solution of contact problems. The considered QP problems can have up to 20,000 unknowns, the Hessian matrix is fully populated and ill-conditioned, while the matrix of constraints is sparse. Variation analysis and optimization of assembly process usually require massive computations of QP problems with slightly different input data. The following optimization methods are adapted to account for the particular features of the assembly problem: an interior point method, an active-set method, a Newton projection method, and a pivotal algorithm for the linear complementarity problems. Equivalent formulations of the QP problem are proposed with the intent of them being more amenable to the considered methods. The methods are tested and results are compared for a number of aircraft assembly simulation problems.

Impact of Positional Isomerism on Pathway Complexity in Aqueous Media.

Pathway complexity has become an important topic in recent years due to its relevance in the optimization of molecular assembly processes, which typically require precise sample preparation protocols. Alternatively, competing aggregation pathways can be controlled by molecular design, which primarily rely on geometrical changes of the building blocks. However, understanding how to control pathway complexity by molecular design remains elusive and new approaches are needed. Herein, we exploit positional isomerism as a new molecular design strategy for pathway control in aqueous self‐assembly. We compare the self‐assembly of two carboxyl‐functionalized amphiphilic BODIPY dyes that solely differ in the relative position of functional groups. Placement of the carboxyl group at the 2‐position enables efficient pairwise H‐bonding interactions into a single thermodynamic species, whereas meso‐substitution induces pathway complexity due to competing hydrophobic and hydrogen bonding interactions. Our results show the importance of positional engineering for pathway control in aqueous self‐assembly.

Impact of Positional Isomerism on Pathway Complexity in Aqueous Media

AbstractPathway complexity has become an important topic in recent years due to its relevance in the optimization of molecular assembly processes, which typically require precise sample preparation protocols. Alternatively, competing aggregation pathways can be controlled by molecular design, which primarily rely on geometrical changes of the building blocks. However, understanding how to control pathway complexity by molecular design remains elusive and new approaches are needed. Herein, we exploit positional isomerism as a new molecular design strategy for pathway control in aqueous self‐assembly. We compare the self‐assembly of two carboxyl‐functionalized amphiphilic BODIPY dyes that solely differ in the relative position of functional groups. Placement of the carboxyl group at the 2‐position enables efficient pairwise H‐bonding interactions into a single thermodynamic species, whereas meso‐substitution induces pathway complexity due to competing hydrophobic and hydrogen bonding interactions. Our results show the importance of positional engineering for pathway control in aqueous self‐assembly.

Modeling and optimization of assembly process of flexible accelerometer based on Petri net

With the continuous improvement of micro-machining technology, sensors such as flexible accelerometers are developing in the direction of miniaturization, integration and complexity. At present, the assembly of flexible accelerometers is mainly done by manual, which cannot meet the requirements of product consistency, and the low efficiency of the production cannot match the requirements. Therefore, based on the modular design method, this paper proposes an automatic assembly system for flexible accelerometer, then uses Petri net to display the assembly process of flexible accelerometer visually, and finds the constraints which restrict the improvement of production efficiency.

Design an effective solution for commercial production of 18/20 mesh diamond grits using large volume cubic high pressure apparatus

The recent developments in the mass production of high-grade large diamond grits utilizing the China-type large-volume, cubic-anvil, high-pressure apparatus (LV-CHPA) are discussed. Some new technologies for the synthesis of large diamond grits have been proposed. The temperature distribution in high-pressure synthetic cell is optimized by adjusting the sample assembly and the technical process. The finite element (FE) modeling is used to visualize the temperature fields. Compared with running experimental methods or testing prototypes, FE modeling allows for quicker and often more efficient and accurate optimization of assembly and technical process. With this improvement, the large diamond grits will become inexpensive benefiting the global construction industry in the near future.

The Method for Risk Evaluation in Assembly Process based on the Discrete-Time SIRS Epidemic Model and Information Entropy

In the past decade years, much attention has been attached on assembly process reliability in manufacturing system, because the quality and cost of product are highly determined by assembly process. However, existing research on reliability in assembly are mainly focused on study of size deviation propagation. In this paper, the method for risk evaluation in assembly process based on the discrete-time SIRS epidemic model and information entropy was proposed. Firstly, aiming at the issue of assembly process optimization, innovative solutions are proposed from the perspectives of reliability and cost by decomposing the assembly into general path and rework path. Secondly, the propagation mechanism of defects in optimal assembly approach were studied through combining the infectious disease model and information entropy. According to the bifurcation phenomenon in the SIRS model, the entropy increment of assembly process when defect emergence occurs is calculated. Thirdly, the information entropy increment of optimal assembly approach is used to evaluate the assembly risk by comparing with the . Finally, a case study of assembly risk evaluation for the oil pump was presented to verify the advantage of this method.

An assembly process parameters optimization method for precision assembly performance

There are only few works in literature that suggest an assembly process optimization method based on manufacturing errors in the precision manufacturing area. A multi-objective assembly process parameters evaluation and optimization method for precision assembly performance of microstructures with manufacturing errors has been proposed in this paper. Based on the model with manufacturing errors, the ABAQUS software is used for simulation and calculation, and the assembly performance evaluation indexes of the microstructures under different assembly process parameters, such as stress value, stress distribution value and pose offset, are obtained. The mapping model of the key assembly process parameters and assembly performance is established based on BP neural network. Finally, the best assembly process parameters for the optimal assembly performance are solved based on the genetic algorithm, and the method has been verified by the optimization results of preload forces of the 3D mechanism, which can be used to guide and monitor the assembly process quantitatively in the precision manufacturing area.