Journal of Intelligent Manufacturing

Blockchain has the characteristic of being tamper-proof, which can make product information transparent and ensure data authenticity. The adoption of blockchain can improve consumer trust and expand the market. However, blockchain adoption will lead to fairness concern. We consider a supply chain comprising a manufacturer and a retailer responsible for introducing and publicising blockchain, respectively. To study the impacts of implementing and promoting blockchain on secondary supply chain decision-making and coordination under retailer’s fairness concern, we construct one centralised and two decentralised decision-making supply chain models where the retailer may have fairness concern. After solving and analysing the optimal decision-making of the three models, we design a two-part tariff contract to ensure that the supply chain is stable and coordinated. Our major findings are as follows: (1) Blockchain-based consumers’ perception of authenticity and preference for the retailer’s publicity is conducive to blockchain adoption. As consumers’ trust in products using blockchain increases, the manufacturer will increase the blockchain level, the retailer will actively promote blockchain, product pricing will increase, and supply chain system profit will increase. (2) The retailer’s excessive focus on fair profit distribution can lead to systemic damage, decrease the blockchain level and retailer publicity, and hinder the use and publicity of blockchain. As the manufacturer becomes less cost-sensitive to blockchain, the retailer’s concern for fairness hurts its own profit. (3) The sales price under centralized decision-making and decentralized decision-making is related to the marginal cost of blockchain. A high marginal cost of blockchain is the main factor hindering the popularization of blockchain. (4) The two-part tariff contract can achieve Pareto optimization where the retailer is concerned about fairness in profit distribution. In coordination under this contract, the manufacturer’s wholesale price is consistent with its own cost. The fixed technology fee can re-distribute the supply chain’s profit. The more significant the effect of blockchain on improving consumers’ product trust is, the higher is the technology fee that the manufacturer charges the retailer. Blockchain will increase the fixed fee charged by the manufacturer and raise its voice in coordination; however, fairness concern may reduce the feasibility of the coordination mechanism. In coordination under the two-part tariff contract, the manufacturer’s blockchain level has the most significant growth, and the retailer’s publicity effort and system profit also increase significantly, and the product has a more price advantage.

Condition monitoring is of vital importance in order to assess the state of tool wear in unattended manufacturing. Various methods have been attempted, and it is considered that fuzzy clustering techniques may provide a realistic solution to the classification of tool wear states. Unlike fuzzy clustering methods used previously, which postulate cutting condition parameters as constants and define clustering centres subjectively, this paper presents a fuzzy clustering method based on filtered features for the monitoring of tool wear under different cutting conditions. The method uses partial factorial experimental design and regression analysis for the determination of coefficients of a filter, then calculates clustering centres for filtering the effect of various cutting conditions, and finally uses a developed mathematical model of membership functions for fuzzy classification. The validity and reliability of the method are experimentally illustrated using a CNC machining centre for milling.

The use of simulation technology as a tool for planning and control is of increasing significance in most fields of production. The main part of the expenditure concerning simulation analyses is the modelling of the considered production. Despite the use of modern building-block-oriented modelling technology, this modelling can often not be done by the user, but only by external experts. Against this backdrop, an adaptive simulation system is being developed by the Institute for Industrial Manufacturing and Management (IFF) at the University of Stuttgart. It independently adapts to real production processes, i.e. it learns about the interdependencies of production processes, and, in this way, supports the user in constructing and maintaining the model. In terms of information technology, the research in the field of artificial intelligence, especially in the subdomain of machine learning, is the basis for the realization of such adaptive systems.

As a cost saving and profit-making strategy, a modular design is being employed in developing complex products and systems (CoPS) in recent decades. At the early stage of design, the reliability of a product can be improved by identifying the influential function modules based on the modular function architecture. In this study, the weighted LeaderRank algorithm and susceptible-infected-recovered (SIR) model of weighted and directed complex networks (WDCNs) are employed to identify the influential function modules of modular CoPS at the conceptual design stage. First, the structure of the function module is obtained and is mapped into a WDCN. Second, based on the similarity between the behaviors of nodes in the WDCN and function modules in the CoPS, a node-identification approach based on the weighted LeaderRank algorithm is employed to identify the influential function modules, whose influences are then verified through the SIR model. The influential function modules of a modular large tonnage crawler crane are determined as a case study to demonstrate the effectiveness and validity of the developed method.

In recent years, most companies have resorted to multi-site or supply-chain organization in order to improve their competitiveness and adapt to existing real conditions. In this article, a model for adaptive scheduling in multi-site companies is proposed. To do this, a multi-agent approach is adopted in which intelligent agents have reactive learning capabilities based on reinforcement learning. This reactive learning technique allows the agents to make accurate short-term decisions and to adapt these decisions to environmental fluctuations. The proposed model is implemented on a 3-tier architecture that ensures the security of the data exchanged between the various company sites. The proposed approach is compared to a genetic algorithm and a mixed integer linear program algorithm to prove its feasibility and especially, its reactivity. Experimentations on a real case study demonstrate the applicability and the effectiveness of the model in terms of both optimality and reactivity.

A growing awareness is brought that the safety and security of industrial control systems cannot be dealt with in isolation, and the safety and security of industrial control protocols (ICPs) should be considered jointly. Fuzz testing (fuzzing) for the ICP is a common way to discover whether the ICP itself is designed and implemented with flaws and network security vulnerability. Traditional fuzzing methods promote the safety and security testing of ICPs, and many of them have practical applications. However, most traditional fuzzing methods rely heavily on the specification of ICPs, which makes the test process a costly, time-consuming, troublesome and boring task. And the task is hard to repeat if the specification does not exist. In this study, we propose a smart and automated protocol fuzzing methodology based on improved deep convolution generative adversarial network and give a series of performance metrics. An automated and intelligent fuzzing framework BLSTM-DCNNFuzz for application is designed. Several typical ICPs, including Modbus and EtherCAT, are applied to test the effectiveness and efficiency of our framework. Experiment results show that our methodology outperforms the existing ones like General Purpose Fuzzer and other deep learning based fuzzing methods in convenience, effectiveness, and efficiency.

As manufacturing processes become increasingly automated, so should tool condition monitoring (TCM) as it is impractical to have human workers monitor the state of the tools continuously. Tool condition is crucial to ensure the good quality of products—worn tools affect not only the surface quality but also the dimensional accuracy, which means higher reject rate of the products. Therefore, there is an urgent need to identify tool failures before it occurs on the fly. While various versions of intelligent tool condition monitoring have been proposed, most of them suffer from a cognitive nature of traditional machine learning algorithms. They focus on the how-to-learn process without paying attention to other two crucial issues—what-to-learn, and when-to-learn. The what-to-learn and the when-to-learn provide self-regulating mechanisms to select the training samples and to determine time instants to train a model. A novel TCM approach based on a psychologically plausible concept, namely the metacognitive scaffolding theory, is proposed and built upon a recently published algorithm—recurrent classifier (rClass). The learning process consists of three phases: what-to-learn, how-to-learn, when-to-learn and makes use of a generalized recurrent network structure as a cognitive component. Experimental studies with real-world manufacturing data streams were conducted where rClass demonstrated the highest accuracy while retaining the lowest complexity over its counterparts.

Cutting force is the fundamental parameter determining the productivity and quality of the milling operation. The development of a generic cutting force model for end milling operation necessitates a large number of experiments. The experimental data contains multiple outliers due to noise and process disturbances lowering prediction accuracy of the model. This paper presents a novel approach combining the mechanistic model and the supervised neural network (NN) model to predict instantaneous cutting force variation during the end milling operation. The approach proposes training of an NN model using datasets generated from the mechanistic force model instead of using experimental data. The methodology generates a large number of datasets for the training of an NN model without conducting rigorous experimentation. A set of NN architectures were developed, and an appropriate network was derived by comparing performance parameters. A series of end milling experiments were conducted to examine the efficacy of the proposed approach in predicting cutting forces over a wide range of cutting conditions.

Active compliance control of robotic manipulators is useful in making robots perform precision assembly operations. The essential requirement here is mechanical isotropy of the robot end-point. In this paper the problem of how to achieve this kind of mechanical behaviour is considered from aspects of impedance control and fuzzy set theory. The new fuzzy–impedance control law, which is suitable for real-time applications, is proposed for a two degree-of-freedom (2-d.o.f.) robotic manipulator. According to simulation results, the proposed control law can provide approximately isotropic behaviour of the robot end-point in the whole workspace.