Reliability Of Manufacturing Systems Research Articles

Reliability oriented key quality characteristics driven integrated built-in reliability activity chain and approach for manufacturing process

Product reliability is determined at the design phase and built into the product through manufacturing process. To effectively implement reliability activities at each stage, reliability data in product life cycle is integrated with KQC (key quality characteristics), and the concept of R-KQC (reliability-oriented KQC) is proposed. Based on the R-KQCs, this study presented an integrated Built-in reliability (BIR) approach to ensure that the product reliability formed losslessly conforms to the reliability goal determined in the design phase. First, the connotations of BIR and BIR-oriented RQR (manufacturing system Reliability, manufacturing process Quality, and product Reliability) chain are proposed by considering the evolution mechanism of product reliability in the life cycle driven by the R-KQCs. Second, a product BIR activity chain consisting of plan, synthesis, and improvement based on R-KQC is presented by the BIR-oriented RQR chain. Third, an R-KQC-driven BIR assurance technology framework based on the BIR activity chain is proposed, and a product reliability modeling considering the deviation of R-KQCs is established based on the framework. Finally, the validity of the proposed approach is verified using the reliability assurance for an automobile engine cylinder head as an example.

Reliability evaluation of flexible manufacturing systems based on three-layer index system with time-varying weights

With the development of industrial manufacturing, flexible manufacturing system provides an effective way to improve the machining quality and efficiency, as well as reduce the manufacturing cost. Fflexible manufacturing system reliability has been one focus of recent studies. But a systematic, effective and executable reliability evaluation method has not yet proposed in existing researches. This work firstly presented a three-layer index system for flexible manufacturing system reliability evaluation, which is composed of the whole layer indexes, subsystem layer indexes and equipment layer indexes determined by system composition and running features analysis. Time-varying weights of each layer are calculated through the modelling of the reliability and reliability importance based on the classical two-state failure hypothesis. Then, considering multi-state failure modes of elements, a fuzzy evaluation model of flexible manufacturing system reliability is established based on fault grading and statistics, considering grade and number distributions of faults within a certain statistical period. Moreover, through a simulation case of a box-parts finishing flexible manufacturing system, the influence of failure rates and service time on the index weights distribution is firstly revealed, which can provide theoretical basis for the improvement of flexible manufacturing system reliability; then the effectiveness of the proposed method is verified through the comparison of results obtained by the previous model based on two-state failure and the proposed model based on multi-state failure.

An Optimization Method of Flexible Manufacturing System Reliability Allocation Based on Two Dimension-Reduction Strategies

As increasingly extensive applications of flexible manufacturing systems (FMSs) arise, their reliability allocation has been a research hot spot. But, since FMSs are always composed of transfer and buffer devices, production machines, and complex control systems, the large number of basic elements makes the number of variables and constraints of reliability-allocation optimization increase greatly, which leads to the difficulty and inefficiency of optimization. To solve the above problem, two dimension-reduction strategies are proposed for the FMS reliability optimization with low cost and a high level of reliability as the objectives, and they are the reliability-weight double-threshold qualification strategy (RWTS) and the bi-level optimization strategy (BLOS), respectively. Based on these two strategies, an overall reliability-allocation optimization model and a bi-level reliability-allocation optimization model are established based on the FMS reliability evaluation presented in our previous work, and their algorithms based on particle swarm optimization (PSO) are presented. In terms of their contributions, for the RWTS, thresholds of reliability and the weight index of each basic element are set to dynamically reduce the number of variables in each iteration of the optimization; for the BLOS, the overall reliability-allocation optimization problem for transitioning from the FMS to basic elements can be transformed into simpler allocation optimizations from the FMS to subsystems and from subsystems to basic elements, which have fewer variables, and this can largely improve the optimization convergence performance. Through applying this to a box-parts finishing FMS, compared with the traditional optimization method, the high efficiency and the good allocation effect of the optimization based on these two strategies for improving convergence speed are verified by the simulation results. The proposed method has great significance for FMS design due to its limited cost but high-reliability requirement.

Binary decision diagram-based reliability modeling of phased-mission manufacturing system processing multi-type products

ABSTRACT The development of science and technology drives phased-mission manufacturing systems to be increasingly complex, which poses significant challenges to system reliability analysis. Although it has been considered that machine degradation is the key factor affecting system reliability, very little has been done to explore the effect of multi-type products on machine degradation, where the dependency of phases processing multi-type products further increases the difficulty in reliability modeling. Focusing on the phased-mission manufacturing system processing multi-type products, this paper presents a novel machine reliability model to investigate the mixed degradation process caused by multi-type products, which characterizes the dynamics of machine reliability. Further, given the proposed dynamic machine reliability model dealing with phase dependency, a binary-decision-diagram-model-based algorithm is proposed to analyze the reliability of phased-mission manufacturing systems when processing multi-type products. Finally, the experimental result verifies the effectiveness of the proposed method, and the accuracy is validated by the traditional Monte Carlo method. Also, the proposed method shows great potential to provide decision support for the operation management of phased-mission manufacturing systems.

Joint optimization of fleet-level sequential selective maintenance and repairpersons assignment for multi-state manufacturing systems

Due to limited maintenance resources, the joint optimization of fleet-level selective maintenance and repairpersons assignment aims to determine the optimal subset of maintenance actions for each repairperson to ensure that manufacturing systems in the fleet complete subsequent missions. However, related studies ignore the uncertain maintenance duration and flow dependency (a stochastic dependency caused by dynamic material flow) resulting in overestimation of system reliability and total profit. Therefore, this paper proposes a novel joint optimization model of fleet-level sequential selective maintenance and repairpersons assignment under flow dependency and uncertain maintenance duration. The model requires identifying a subset of maintenance actions, assigning the selected maintenance actions to repairpersons, and planning sequence of maintenance actions executed by each repairperson. Moreover, the degradation model of multi-state machines under flow dependency is established by analyzing dynamic characteristics of production processes. Further, the reliability of multi-state manufacturing systems considering the interaction among repairpersons, flow dependency, and uncertain maintenance duration is incorporated into the optimization model. The objective is to maximize the total profit under predetermined system reliability thresholds. Then, the optimization problem is solved by a tailored genetic algorithm with segment-based evolution strategy. Finally, numerical examples are given to verify the effectiveness of the proposed method.

Complexity Model for Predicting Oil Displacement by Imbibition after Fracturing in Tight-Oil Reservoirs

With the increasing difficulty of conventional oil and gas exploration and development, oil and gas resources have developed from conventional to unconventional, and the exploration and development of tight-oil reservoirs are highly valued. In view of the complexity of the influencing factors of oil-water spontaneous seepage after fracturing and the instability of reservoir recovery, this paper takes the tight sandstone reservoir of Yanchang Formation in the southern Ordos Basin as the research object. Based on the micro-nano pore throat characteristics of tight sandstone, the seepage experiment is carried out, and the theoretical model of seepage suction is constructed. The mechanism and influencing factors of suction and oil displacement after fracturing in tight reservoirs are analyzed. Based on the analysis of fluid buoyancy and gravity, a mathematical model of the oil-water spontaneous flow after fracturing was established, and its influencing factors were analyzed. The experimental results show that the pore throats of tight sandstone are mainly in micron- and submicron scale, and the reservoir permeability is related to the pore throat structure, oil-water interfacial tension, and wettability. After fracturing, with the increase of the fracture length, the seepage velocity gradually decreases. With the increase of fracture opening, the influence of buoyancy and gravity on seepage velocity increases. With the increase of the fracture number, seepage velocity also increases. The fracture helps to reduce the adsorption of oil droplets on the core surface and improve the efficiency of spontaneous imbibition and oil displacement of the core. The research results provide theoretical data support for enhancing oil recovery and have important application guiding significance for the operational reliability of manufacturing systems with complex topology and the complexity and operability of production operations in manufacturing systems.

Data-Based Estimation of the Dynamic Reliability and Performance Indicator of an Industrial Manufacturing System

The aim is to develop a more simple and effective method's performance and dynamic reliability assessment for complex industrial systems. By using the operating data of the industrial system characterized by a strong desynchronization and applying to it prediction algorithms of artificial intelligence applied to the time series, the model will have learned from the behavior of the complex manufacturing system allowing the operator or decision-maker to better orientate the maintenance, production, and quality policies. Furthermore, we propose this approach to avoid tedious mathematical methods related to dynamic reliability calculations and performance evaluation to make forecasts of the company's operation over a long period by identifying future bottlenecks in the system's behavior. The low-performance indicators and irrelevant reliability presented by many third-generation industries are due to the lack of efficient and simple tools for reliability assessment taking into account the dynamic aspect of the different elements of the production chain, maintenance department, production department, and quality department. We propose to develop a model that will abstract from conventional, complex, and inefficient mathematical methods for systems subject to combinatorial explosion problems in the manufacturing industry.

Mission reliability–centered maintenance approach based on quality stochastic flow network for multistate manufacturing systems

Previous studies of reliability centered maintenance (RCM) rarely consider the maintenance quality for the operation condition monitoring of manufacturing system. Therefore, a quality-oriented maintenance approach for the multistate manufacturing system with the aid of mission reliability is proposed. First, connotations of the mission reliability and maintenance quality of the multistate manufacturing system are expounded on the basis of the operational mechanism. Second, a quality stochastic flow network (QSFN) model of the multistate manufacturing system is established, and a novel mission reliability model is presented. Third, a quality-oriented mission reliability–centered maintenance framework for multistate manufacturing systems is proposed, and the optimal integrated maintenance strategy is obtained by minimizing the total cost. Finally, an industrial example of subway flow receiver is presented to verify the proposed method. Results show that the proposed method can simultaneously balance the maintenance cost and maintenance quality of the multistate manufacturing system.

Integrated mission reliability modeling based on extended quality state task network for intelligent multistate manufacturing systems

The quality of produced WIP(work-in-process) is the direct indicator of operational reliability of manufacturing system. To assure the predictability of the reliability of finished products is the paramount goal for the operation and maintenance of intelligent manufacturing systems. Therefore, a novel integrated mission reliability modeling approach based on extended quality state task network (EQSTN) for intelligent multistate manufacturing systems is proposed in this paper. First, to guarantee the reliability of final produced products, the relationship between the manufacturing system reliability and the produced product reliability is explained from the systematic perspective. The connotation of integrated mission reliability of intelligent multistate manufacturing systems is also provided. Second, an EQSTN is proposed based on operational quality data by extending the traditional dimensional conformance quality to functional fitness quality. Third, based on the established EQSTN, an integrated mission reliability modeling approach is proposed to quantitate the operational healthy state of the intelligent manufacturing system dynamically. Finally, a case study of an intelligent multistate manufacturing system for a ball screw pair is conducted to verify the proposed approach.

5G-Enabled IIoT Framework Architecture Towards Sustainable Smart Manufacturing

With the rapid growth of advanced technologies in the manufacturing sector, a revolution in manufacturing systems is underway and smart manufacturing and its sustainability are becoming the key components towards the fourth industrial revolution. In this context, the IIoT (industrial internet of things), represents a bridge between the digital and physical environment by providing an interactive relation between smart devices and machines also through data sharing. Therefore, it creates a working environment where decisions are made in real-time.The huge data amount generated through the manufacturing system, the high reliability, low latency, and high connectivity demands of IIoT-enabled intelligent manufacturing system requires an advanced wireless transmission technology that goes far beyond the 3rd and 4th generation mobile network. 5G is the most appropriate communication technology for this new IIoT enabled smart manufacturing system’s requirements.Based on the requirements of sustainable smart manufacturing and the characteristics of the 5G wireless communication, this paper proposes a 5G-enabled IIoT framework architecture towards a sustainable smart manufacturing environment, that will allow the support of manufacturers and smart factories in the industrial 4.0 revolution. This is by improving while enhancing efficiency, process and product quality, and sustainability in the whole manufacturing system. Besides, the security threats and challenges of the 5G-IIoT enabled smart manufacturing are also analyzed.

Towards Data-Driven Reliability Modeling for Cyber-Physical Production Systems

Abstract Reliability is one of the most important performance indicators in contemporary production facilities. Increasing reliability of manufacturing systems results in their prolonged lifetimes, and reduced maintenance and repair costs. Reliability modeling is a common technique for deriving reliability measurements and illustrating relevant fault-dependencies. There is a significant body of research focusing on hardware- and software reliability models, such as Fault Trees, Petri Nets and Markov Chains. Up until now, development of reliability models has been a labor-intensive and expert-knowledge-driven process. To remedy that, through the prevalence of data stemming from the new and technologically advanced manufacturing systems, we propose that data generated in modern manufacturing lines could be used to either automate or at least to support development of reliability models. In this paper, we elaborate on the details of our proposed framework for data-driven reliability assessment of cyber-physical production systems. We, furthermore, introduce a case study that will aid the development and testing of the proposed novel data-driven approach.

CHALLENGES AND CONCEPTUAL FRAMEWORK TO DEVELOP HEAVY-LOAD MANIPULATORS FOR SMART FACTORIES

Industry 4.0 has been one of the emerging topics in recent years, covering a wide range of concepts and applications as well as political, economic and technological views. Manufacturing is becoming smarter and smarter at all levels, moving toward the concept of Smart Factory (SF), based on the advancements of digital transformation technologies, including Artificial Intelligence (AI) and bigdata analytics, and abilities to learn, configure and execute with cognitive intelligence of smart machines and automation systems. However, the SF adoption in practice, especially in Small and Medium-sized Enterprises (SMEs), is still in the early stage. In addition, there are growing demands of product personalisation, mass-customisation and diversification. Therefore, the involvement of humans is still importantly required in many production processes in SF models, where smart machines, smart manipulators, collaborative robots and Automated guided vehicles (AGVs) are required to co-work with humans, leading to an important concern of safety, reliability, productivity and quality of smart manufacturing systems. In this paper, challenges and a proposed conceptual framework to develop smart heavy-load manipulators are presented, with the focus on the cost-effectiveness and applicability in industrial practices of SF for SMEs.

Prognostic study of ball screws by ensemble data-driven particle filters

The prognostic study of the ball screw is critical to increase the reliability of manufacturing system, which has drawn great attention in the field of Prognostics and Health Management (PHM). The particle filters (PF) method is a powerful tool for prognostic study because of its capability of robustly predicting the future behavior. However, lack of analytical ball screw measurement model limits the application of PF. In this paper, an ensemble GRU network is designed to extend PF to the case where the analytical measurement equation is not available. The proposed hybrid GRU-PF method integrates the data-driven model and the physical model into the particle filters network to realize the prognostic and remaining useful life (RUL) prediction of the ball screw. The effectiveness of the proposed method is validated by designing a ball screw accelerated degradation test (ADT), and the results of this experimental study demonstrate the satisfactory performances in terms of prognostic sensibility.

The role of human factor in manufacturing safety management

The article focuses on researching of influencing the human factor on the level of modern manufacture safety. The principles of the manufacture organization are changing rapidly; as a result, there is an active change in requirements to personal. Requirements to individual characteristics of a person are brought to the first place because they are the resource that directly affects safety and production efficiency. Therefore, the manufacturing system reliability and safety is directly related to the human factor. This fact causes the necessity of a fundamentally new approach to the manufacturing system organization and functioning in the field of safety. In this regard, researching the role of the human factor in the manufacturing safety management system is the primary goal of the work. A literature analysis shows the multidimensional of the human factor problem. It causes the necessity of application of an integrated approach to the problem solution. For this purpose, it was analysed the modern technologies of human factor management in manufacturing. It was established the most effective actions for human factor management are the combination of traditional approaches to ensuring employee safety (ergonomic methods) with modern achievements (information and cognitive technologies). It was researched the concept of "risky behaviour" and the systemic causes of such behaviour. The programming of safe human behaviour model in the ever-changing manufacturing environment allows improving manufacturing safety. It was shown through the modified Heinrich's pyramid. According to the pyramid, the basis for creating a safe working environment is the ability of the employee to learn the concept "human factor" and its importance for ensuring individual safety. Accounting the human factor in safety issues makes it possible to increase the efficiency of forecasting the hazardous events occurrence and choose the most effective measures to ensure safety at manufacturing. The introducing of the "Human Factor on Manufacturing" discipline in the training system for employees proposed as a practical measure to improve safety in the workplace. The available positive experience in the application of such an event suggests that it is possible in the future to develop an individual approach to the formation of the skills and abilities of a person who makes decisions in the working environment.

Dynamic Modeling Method of Mission Reliability of a Multi-State Manufacturing System With Multiple Production Lines

Mission reliability could systematically characterize the running state of a manufacturing system and the rationality of production task setting in advance. However, few studies can realize the mission reliability modeling, which integrates production scheduling based on fully considering the complexity, dynamics, and multi-state characteristics of a manufacturing system. Thus, a dynamic method for modeling the mission reliability of a multi-state manufacturing system with multiple production lines is proposed in this paper. First, the multi-state characteristic of manufacturing systems operation is analyzed. On this basis, considering the composition of manufacturing systems, the connotation and modeling mechanism of the mission reliability of manufacturing systems are proposed. Second, combining the discretization of machine performance states and dynamic continuity of the performance degradation process, the dynamic modeling method of multi-state machine performance and buffer usage are respectively discussed. The transfer evolution of task demands in the production line is analyzed, and the decomposition method of task scheduling and load mapping in multiple production lines is provided. Third, considering the characteristics of multiple production lines and the buffer of a manufacturing system, taking production tasks as the core and integrating production scheduling, a modeling method for the mission reliability of manufacturing systems is proposed. Finally, the effectiveness of the proposed approach is verified using a case study on the modeling of the mission reliability of a manufacturing system of aerospace parts.

Tolerance & Time margin

Abstract This paper deals with production variabilities and their formalization: Geometrical variability that affects the product quality and Time variability that affects the productivity. Due to the manufacturing imperfections, the manufacturing system faults, the human factors, ... it is not possible to attain the theoretical dimensions and the theoretical time in a repetitive manner. This causes degradations of the product & production system performances. In order to ensure the desired behavior and the performance of the engineering system in spite of uncertainty, the system features are assigned a permissible tolerance or a margin. Time margin allocation consists in establishing a certain level of work flexibility, which gives the operator the possibility to develop different ways of working to meet production targets, without having adverse effects on his or her health. The concepts of geometric tolerance and time margin are very similar; they directly have an impact on the Overall Equipment Effectiveness (OEE): An OEE of 100% means that only good parts are produced (100% quality), at the maximum speed (100% performance), and without interruption (100% availability). The human or manufacturing system fatigue and reliability affect directly the respect of the time margin and indirectly the product conformance. There exists a strong link between tolerance and time margin.

Modelling and evaluation of multi-state reliability of repairable non-series manufacturing system with finite buffers

The capacity and capability of flexible manufacturing system varies with different market demands. To satisfy the requirements of performance expressions, avoid the problem of combinatorial explosion and consider the influence of intermediate buffer stations, a new reliability modelling and evaluating methodology for repairable non-series hybrid flexible manufacturing systems with finite buffers is proposed using an extended vector universal generating function technique. For repairable modular machines, the Markov models of modular machines are established using stochastic process analysis and the corresponding theoretical steady-state probability in various states is obtained. Furthermore, the original system in combination with multi-state reliability measures of buffer stations is equivalent to a system with independent machines which can be expressed by vector u-functions. Based on the probability distributions of the states of subsystems, the composition operators for series connections and parallel connections are defined. Consequently, the entire system is simplified to one component represented by the polynomial universal generating function. In particular, reliability indicators and measurement models are given to assess the system’s reliability through promoting the basic ones. Finally, a practical case of engine head machining line is utilized to verify the effectiveness of the method. The results demonstrate that the use of vector universal generating functions can describe the system structure and states more appropriately while providing efficient assessment.

Blockchain-based reputation management for custom manufacturing service in the peer-to-peer networking environment

Blockchain enables users to be autonomous without the need of trust by using a digital ledger of decentralized consensus. Blockchain-based IoT allows service providers to transfer the security and maintenance responsibility to self-maintaining customers. Adopting peer-to-peer (P2P) networking and computing for more than billions of transactions can reduce the costs arising from the installation and maintenance of centralized systems. Small manufacturers providing industrial IoT (IIoT) services can participate more actively in blockchain-based IIoT applications with three-dimensional printing and digital manufacturing technologies, but the measure to maintain privacy on a blockchain is not robust. Here, we propose a P2P networking-based custom manufacturing service, which is an order-driven trading service between a manufacturer and a customer in the blockchain-based IIoT architecture. The proposed system consists of reputation management and service architecture. We propose a new reputation assessment method customized to increase the reliability and accuracy of industrial manufacturing systems. We also propose a manufacturer rating classification to guide the customers’ decision making in a reliable manner and a malicious evaluator identification to exclude feedbacks from malicious evaluators. The proposed service architecture is composed of trustless P2P protocols designed for preserving privacy and providing non-repudiation. We used a cryptographic algorithm for ensuring transaction privacy and the digital signing of blockchain for non-repudiation. We also analyzed the proposed service architecture and the possible attack scenarios to verify the security requirements. We verified that the reputation management system was influenced by each feedback dynamically and guided the customer’s present decision making with reliable and classified manners, by simulating reputation classification and malicious evaluator identification. Further, we have summarized the originality and the characteristics of the proposed approach by comparing closely related studies and concluded with a future research guide.

Reliability Assessment in the Context of Industry 4.0: Data as a Game Changer

Reliability is the measure of the likelihood that a product, system or service will perform its intended function adequately for a specified period of time. Low reliability of a manufacturing system, besides the costly repairs and replacements, also implies reduced production, and consequently, significantly reduced profits. Therefore, it is very important to have a way to assess reliability, as a key performance metric for manufacturing systems, and cyber-physical systems in general. The newly developed information and communication technologies that are increasingly becoming part of the current and future manufacturing systems, both allow and invite for more sophisticated approaches to assessing reliability of manufacturing systems as opposed to the traditional expert knowledge-based approaches. In this paper, we describe the significance of evaluating reliability for the progress and acceptance of the Industry 4.0 technologies, as well as the new directions and possibilities for enhanced reliability analysis that these new technologies can provide. Finally, we provide an overview of the implications of these novel ways of analyzing reliability in the context of Industry 4.0.

Business Process Modeling in the Digital Age

Newly emerged in the Russian society trend towards digitalization is gaining increasing number of applications. Business processes modeling based on modern IT approaches has a large potential for improving the efficiency and reliability of various manufacturing systems. Unfortunately, studies that offer specific tools for formal description of manufacturing processes are rare. In this paper we propose a four-stage system for modeling business processes: design, visualization, construction of a logical model and, finally, generation of a computer code (proprietary approach to the CASE method). This technology for business processes modeling, as well as specific products based on it, can be widely used in manufacturing processes, from document workflow automation to the operation of numerically controlled machines. The proposed tool can help to increase the productivity of personnel of organizations and optimize manufacturing processes. The automatic code generation eliminates one of the main drawbacks of software development — the code does not contain input errors.