Risk-oriented assembly quality analysing approach considering product reliability degradation

Assembly process is a critical stage in the formation of product quality and reliability, but related consideration of the produced product reliability and accident risk has not attracted deserved attention in the most assembly quality analysis frameworks. To this end, this paper enhances risk analysis in assembly process quality control, which is advocated by ISO 9001:2015, and presents a risk-oriented assembly quality analysis approach considering the effects of assembly variations on the produced product reliability degradation and accident risk. First, a conceptual QRR chain is presented to illustrate the relationship among assembly process quality (Q), product reliability (R), and failure accident risk (R). Second, a risk-oriented and bidirectional framework for the analysis of assembly process quality is established based on the presented QRR chain aiming to quantitatively identify the risk sources in the assembly process and reduce the risk of failure accidents. Third, an assembly process quality risk model with key function reliability as its core is presented to establish the quantitative relationship between assembly variation and product failure accident risk. Finally, the presented approach is verified through a case study of an assembling quality risk analysing for acid-resistant grinder.     

Developing a selective assembly technique for sheet metal assemblies

Applying the concept of Digital Twin in production processes supports the manufacturing of products of optimal geometry quality. This concept can be further supported by a strategy of finding the optimal combination of individual parts to maximise the geometrical quality of the final product, known as selective assembly technique. However, application of this technique has been limited to assemblies where the final dimensions are just function of the mating parts' dimensions and this is not applicable in sheet metal assemblies. This paper develops a selective assembly technique for sheet metal assemblies and investigates the effect of batch size on the improvements. The presented method utilises a variation simulation tool (Computer-Aided Tolerancing tool) and an optimisation algorithm to find the optimal combination of the mating parts. The approach presented is applied to three industrial cases of sheet metal assemblies. The results show that using this technique leads to a considerable reduction of the final geometrical variation and mean deviation for these kinds of assemblies. Moreover, increasing the batch size reduces the amount of achievable improvement in variation but increases the amount of achievable improvement in the mean deviation.     

Maximising manufacturing system efficiency for multi-characteristic linear assembly by using particle swarm optimisation in batch selective assembly

Quality of an assembly is mainly based on the quality of mating parts. Due to random variation in sources such as materials, machines, operators and measurements, even those mating parts manufactured by the same process vary in their dimensions. When mating parts are assembled linearly, the resulting variation will be the sum of the mating part tolerances. Many assemblies are not able to meet the assembly specification in the available assembly methods. This will decrease the manufacturing system efficiency. Batch selective assembly is helpful to keep the assembly requirement and also to increase the manufacturing system efficiency. In traditional selective assembly, the mating part population is partitioned to form selective groups, and the parts of corresponding selective groups are assembled interchangeably. After the invention of advanced dimension measuring devices and the computer, today batch selective assembly plays a vital role in the manufacturing system. In batch selective assembly, all dimensions of a batch of mating parts are measured and stored in a computer. Instead of forming selective groups, each and every part is assigned to its best matching part. In this work, a particle swarm optimisation based algorithm is proposed by applying the batch selective assembly methodology to a multi-characteristic assembly environment, to maximise the assembly efficiency and thereby maximising the manufacturing system efficiency. The proposed algorithm is tested with a set of experimental problem data sets and is found to outperform the traditional selective assembly and sequential assembly methods, in producing solutions with higher manufacturing system efficiency.     

An economic off-line quality control approach for unstable production processes

An economic off-line inspection/disposition approach is proposed, which incorporates manufacturing variation. This approach includes a new inspection algorithm for inspections based on cost minimization and utilizing a specified confidence level for identifying in-control items. This approach addresses two situations not addressed in previous papers: (i) the last unit in the batch is conforming; (ii) inspection time is non-negligible. Advantages are that there is no need to record the original position of each unit, the impact of manufacturing variation is reduced and non-negligible inspection times can be easily incorporated. Comparisons of this approach to other approaches are provided.     

Product data quality validation system for product development processes in high-tech industry

Among various product data, 3D CAD data plays a key role in current product design and manufacturing processes including industrial design, detail design, CAE, inspection, mould-making, production, and so on. If 3D CAD data has geometrical or topological errors by user mistakes or modelling software bugs and those errors are not cleaned by the data creator in an early stage, the data will be transferred to the downstream operators and they have to fix the errors before starting their own work. Because 3D data is quite complex, it is very difficult to recognise the data errors manually in a modelling system before a big problem is encountered which blocks the next operation. In this case, it generally causes time delay and high cost for data correction and the effect will be bigger when the process is close to the back-end. In this research, we develop a fully automated product data quality validation and management system to support the product development processes of high-tech products like televisions, camcorders, mobile telephones, home appliances, etc. The system automatically validates the 3D data in real time and gives 3D error reports to the creator to correct modelling failures in their steps. Also, project managers can check or control the data delivery based on the data quality for each step.     

应用熵学对产品综合质量控制方法的改进

随着社会生产的不断进步,用单一指标反映产品质量的控制生产过程的传统质量控制图已经不能满足现代化生产的需要。某些产品的质量必须用综合指标来反映,比如化工产品和钢铁行业。熵权法是一种在综合考虑了各种因素所提供的信息量的基础上计算一个综合指标的数学方法。基于这样一种客观的综合指标,传统的产品质量控制图能得到进一步的改进,并能对产品生产过程进行更好的控制。     

Proactive assessment of basic complexity in manual assembly: development of a tool to predict and control operator-induced quality errors

A major challenge for manufacturing companies today is to manage a huge amount of product variants and build options at the same time in manufacturing engineering and in production. The overall complexity and risk of quality errors in manual assembly will increase placing high demands on the operators who must manage many different tasks in current production. Therefore, methods for decreasing and controlling assembly complexity are urgent because managing complex product and installation conditions will result in distinct competitive advantages. The objective of this paper is to present a method for predictive assessment of basic manual assembly complexity and explain how included complexity criteria were arrived at. The verified method includes 16 high complexity and 16 low complexity criteria to aid designers in preventing costly errors during assembly and create good basic assembly conditions in early design phases of new manufacturing concepts.     

Product complexity and design of inspection strategies for assembly manufacturing processes

In the manufacturing field, the assembly process heavily affects product final quality and cost. Specific studies, concerning the causes of the assembly defects, showed that operator errors account for high percentage of the total defects. Also, models linking the assembly complexity with the operator-induced defect rate were developed. Basing on these models, the present paper proposes a new paradigm for designing inspection strategies in case of short-run productions, for which traditional approaches may not be carried out. Specifically, defect generation models are developed to get a priori predictions of the probability of occurrence of defects, which are useful for designing effective inspection procedures. The proposed methodology is applied to a case study concerning the assembly of mechanical components in the manufacturing of hardness testing machines.     

Developing assembly line layout for delayed product differentiation using phylogenetic networks

Effective formation of product platforms helps adapt to product demand changes and decrease time-to-market and lead time. The product platform groups the core elements of product family members into a common module used to derive different product variants by combining it with different components. A new delayed product differentiation (DPD) platform network model, which applies median-joining phylogenetic networks (MJPN), is proposed. It is used for forming product platforms and determining the assembly line layout of modular product families. The MJPN is traditionally used for DNA sequences’ mapping, analysis, clustering and tracing evolutionary trends. The concept of assembly/disassembly modular platforms, whereby both assembly and disassembly of components are used to derive the final product variants from the platform, is utilised. The proposed model determines the required number and composition of a product platform and defines the DPD points. The developed dynamic assembly/disassembly platforms enhance routing and product mix flexibility due to having different platforms that can be used to produce the same product variant. A family of household kettles is used to demonstrate the application of the proposed model. A metric is presented for determining the effectiveness of a given platform in delaying the product differentiation, hence increasing the efficiency of mass customisation. The proposed metric, applied to the case study, demonstrated that the proposed platform formation model using MJPN is more capable of postponing the product differentiation point.     

QCs-linkage model based quality characteristic variation propagation analysis and control in product development

A QCs-linkage model based framework of quality characteristic variation analysis and control was proposed. Firstly the definition of quality characteristic variation propagation was clarified and the concept of linkage was introduced, based on which, QCs-linkage network and QCs-linkage matrix were constructed. Then the framework of variation propagation analysis and control consisting of 4 phases of quality characteristic variation identification, variation source-tracing, variation mitigation and variation prevention was given. Some typical variation propagation effects were concluded and the variation source-tracing process was studied, in which the algorithms of variation propagation probabilities in different linkage combination patters were discussed. In the following, the variation mitigation process and mitigation methods were discussed in detail and the variation mitigation principle based on minimum mitigation risk was given. A case of internal combustion engine study presented in this paper indicated that the framework was effective for quality control and improvement in product development.     

Time estimation method for manual assembly using MODAPTS technique in the product design stage

This paper aims to propose an accurate and quick assembly time estimation method using the modular arrangement of predetermined time standards in the product design stage. It describes a classification of 2382 assembly operations that are incurred in manually assembling consumer electronics such as air conditioners, washing machines and refrigerators, and a method of choosing representative motions comprising work elements by examining the frequency distribution of the assembly operation’s motions. It then presents criteria for assigning time values associated with the movement of the representative motions using the design factors employed in design for assembly and the layout factors of an assembly line. A case study then presents the practicality of the method, the statistical results of which indicate that the proposed method would be accurate enough for practical purposes.     

Improved method of estimating the product quality after multiple inspections

A complex product is often inspected more than once in a sequential manner to ensure the product’s quality. Based on the number of defects discovered during each round of inspection process, we can estimate the number of defects still remaining in the product. For each defect, the probability that the defect will be detected during each inspection cycle is usually assumed to be a known ‘constant’. However, in many practical situations, some defects are easily detected, while others are much more difficult to identify. In this paper, we propose a ‘beta-geometric’ inspection model in which the heterogeneity in detection probability is described by a beta distribution. In a numerical study, we show that our more realistic inspection model clearly outperforms traditional estimation methods that are based on the assumption of a constant detection probability.     

Fuzzy-based analysis of process capability for assembly quality assessment in mechanical assemblies

Process capability indices are useful tools for evaluating the ability of a process to produce products that meet certain specifications. The assembly quality is dependent on the distribution of variations of assembly dimensions, which is in turn dependent on mating conditions in the mechanical assembly. Since it is often difficult to measure the assembly dimensions in the production stages, they are not considered as a direct inspection objective. Rather, the inspection and evaluation of quality is carried out by specifying whether the assembly requirements satisfy the specified limits. Therefore, we can basethe process capability indices on the assembly dimensions. In most real life cases, the observations are fuzzy. In this paper, a novel method based on fuzzy concepts for process capability analysis of assembly dimensions in mechanical assemblies is presented. According to this scheme, sample observations of manufactured variables are described as fuzzy numbers. The proposed method is able to estimate the ability of the manufacturing process in satisfying the assembly quality in the mechanical assemblies with asymmetric tolerances which have non-normal distributions. In this paper, a proper criterion based on the probability of fuzzy set to interpret the computed fuzzy results is proposed, so these results are converted to the interpretable results for making a decision to evaluate the assembly quality. Furthermore, a new fuzzy-based quantity factor for expressing the percent contributions of effective manufacturing variables on the assembly quality is presented. The application of the presented method is demonstrated through an example and its results are discussed.     

面向产品生命周期的全面质量管理

综述了全面质量管理所面临的挑战,提出了在新形势下整合TQM运作模式的必要性,构建了融合标准化、定量化、持续改进与追求突变等先进管理思想的TQM运作模式的总体框架,并从活动单元、管理模式和过程方式等不同的角度对其进行了详细的分析与讨论.     

离子选择电极法测定氟化物过程中的质量控制

文章从氟离子选择电极的维护及其性能的判断、相应极限、空白电位值、参比电极的维护及其对电位值的影响、滤纸的使用以及溶液的pH值、温度、搅拌、电极浸入深度与位置、测定顺序等影响离子选择电极法测定氟化物的有关因素进行了研究和探讨，并提出对较复杂样品的测定易采用标准加入法以提高测定准确度和精密度。     

Optimal process design in selective assembly when components with smaller variance are manufactured at three shifted means

Selective assembly is an effective approach for improving the quality of a product that is composed of two mating components, when the quality characteristic of the product is the clearance between the mating components. In this approach, the components are sorted into several groups according to their dimensions, and the product is assembled by randomly selecting mating components from corresponding groups. A number of previous studies focused on equal width partitioning schemes, in which the dimensional distributions of the two components are partitioned so that all groups have equal widths. When there is a large difference between the variances of the two component dimensions, equal width partitioning will result in a large number of surplus components due to differences between the numbers of components in corresponding groups. Some authors have proposed a method of manufacturing the component with smaller variance at three shifted means to cope with this difficulty. In the present paper, an optimal manufacturing mean design that minimises the number of surplus components is derived. It is shown that the use of the optimal design considerably reduces the number of surplus components compared with using another previously proposed manufacturing mean design and the no-shift design.     

A three-stage decomposition approach for energy-aware scheduling with processing-time-dependent product quality

Due to increasing concerns about energy and environmental demands, decision-makers in industrial companies have developed awareness about energy use and energy efficiency when engaging in short-term production scheduling and planning. This paper studied a flow-shop scheduling problem consisting of a series of processing stages and one final quality check stage with the aim of minimising energy consumption. In particular, the product quality in the problem depends on its processing time at each stage, and the energy consumption is related to the processing speed, equipment state and product quality. A novel three-stage decomposition approach is presented to solve the proposed energy-aware scheduling (EAS) problem. The decomposition approach can drastically reduce the search space and provide reliable solutions for the EAS problem. The numerical experiments show that the computational results can achieve an optimality gap of less than 4% when compared to the global optimal solutions. The parameter analysis demonstrates the managerial implications of the proposed problem. For example, increasing the number of alternative processing speeds or relaxing the delivery date will increase energy efficiency. The energy-saving potential is illustrated by comparing the scheduling results using the proposed approach and human experience.     

A control theoretical modelling for velocity tuning of the conveyor belt in a dynamic mixed-model assembly line

This paper proposes a control theoretical modelling to study dynamic behaviour of a mixed-model assembly line. First, an open-loop model is developed for the system, then examined via different conveyor’s velocity values. It is realised that the performance of the system is very sensitive to the velocity; therefore, a closed-loop (CL) model is developed taking feedback from the system. By the use of proportional-integral-derivative (PID) controller and SIMULINK, some interesting results are obtained applying CL model: regardless of the sequence of the products in the line, the total work-overload and idleness always equals to zero. Moreover, less working area within the workstation is required. Based on the statistical analysis, it is found that no significant increase in makespan is imposed by CL model. It is also shown that PID controller is robust not only to the disturbances of the velocity, also to the uncertainties in the assembly operation times. These results are supported by many numerical experiments dealing with different test problems, line configurations and sequences. Finally, using a discrete event simulation model, the proposed approach is applied into a seru production mode. Simulation results show that the feedback PID controller can deal with real-world assembly line problems, successfully.     

A new grouping method to minimize surplus parts in selective assembly for complex assemblies

Selective assembly is the method of obtaining high precision assemblies from relatively low precision components. The mating parts are manufactured with wide tolerances. The mating part population is then partitioned to form selective groups. The corresponding selective groups are then assembled interchangeably. The mating parts are manufactured in different processes and in different machines. The standard deviation of the mating parts will be different. The probability of the number of parts in the selective group cannot be the same. A large number of surplus parts is expected according to the difference in standard deviations of the mating parts. In this paper, a complex assembly with three mating parts (as in a ball bearing: an inner race, ball and outer race) is considered for analysis. A new method is proposed for partitioning the lots to form selective groups. By this method, the number of surplus parts is reduced to a large extent. The variation in clearance is minimized and the total number of groups is also less when compared with traditional methods.     

Modular design of product families for quality and cost

The purpose of this article is to help managers early in the design of new product families. Based on product structures, sales forecasts, and constraints imposed by the marketplace, like quality and cost, the proposed method selects the product modules that meet customer requirements for the products, while respecting those constraints. The proposal includes a single-level module design formulation that considers quality and cost simultaneously. The method for testing the proposed algorithm is based on a case study of an electro–mechanical assembly device (headlamp). The performance of the algorithm is compared to that of the zero module case, where often the constraint problem cannot be resolved. The main result is a model and an algorithm that optimise quality and cost under the constraints of quality and cost. It shows what modules to manufacture, in what quantities, and in which products to use them. The output also provides the predicted quality and cost, based on improvements made to the modules. To conclude, this research enables the joint optimisation of quality and cost by defining the modules to be manufactured. It provides input for managers seeking modules designed for their supply chain. The algorithm provides key input for managing production ramp-up.