Supporting Interoperability of Virtual Factories

The manufacturing industry is entering a new era. This emerging era starts with the integration of new ICT technologies and collaboration applications into traditional manufacturing practices and processes, such as manufacturing 2.0. Manufacturing 2.0 has been conceptualised as a system that goes beyond the factory floor, and paradigms of “manufacturing as an ecosystem” have emerged. The virtual factory is one of the important concepts and foundations central to the realization of future manufacturing. In this paper, we take a look into the current research on virtual factories and propose a new approach to improve interoperability through the integration of different proprietary, legacy and existing solutions

A Markov chain-based approach to model the variance of times-to-failure and times-to-repair in manufacturing systems

The development of manufacturing systems with high level of automation is thrust by high-volume demand for heterogeneous engineered products. The paper focuses on the usage of Phase-type distributions in the description of reliability parameters, both times-to-failure (TTFs) and times-to-repair (TTRs), for a workstation with several failure modes. Differently from classical analytical models based on exponential distributions, the variance of reliability parameters can be exactly captured, allowing a sounder performance evaluation of the production system in which the workstation operates. While state-of-the-art research works adopt single-station models accounting for variance of TTR and/or TTF of a single failure mode, the presented model framework can capture the variance of TTRs and TTFs of all workstation failure modes, or only a portion of them. The formalized approach has been validated against a simulator replicating the workstation behavior, grounding on data acquired from the field. The application on an industrial case study showed the numerical impact of accounting for the actual variance on performance evaluation, exploiting an asynchronous continuous model of two machines-one buffer line, with finite buffer capacity and deterministic processing times. Further developments may concern the integration of the model in Markov chain-based analytical models of longer manufacturing lines

Switching- and hedging- point policy for preventive maintenance with degrading machines: application to a two-machine line

Maintenance and production are frequently managed as separate activities although they do interact. Disruptive events such as machine failures may find the company unready to repair the machine immediately leading to time waste. Preventive Maintenance may be carried out and maintenance time reduced to the effective task duration, in order to prevent time waste. Companies and researchers have been focusing on policies able to mitigate the impact of Preventive Maintenance on system availability, by exploiting the knowledge about degradation profiles in machines and the joint information from the machine state and the buffer level. In this work, the mathematical proof of the optimal threshold-based control policy for Preventive Maintenance with inventory cost, maintenance cost, backlog cost is provided. The control policy is defined in terms of buffer thresholds and dependency of the thresholds on the degradation condition. The optimal control policy is proved to include a combination of switching points and hedging points, where the first ones activate the Preventive Maintenance for a given condition and the latter ones control the production rate in order to minimize the surplus. An extensive experimental campaign analyzes the impact of system parameters such as the Maintenance duration on the cost function. The results show that there exists cases in which the optimal policy is dominated by the effect of the hedging points or the switching points, alternatively. Therefore, the proposed method is used to provide suggestions to the management for operative decisions, in order to choose the policy fitting best the system

Data-driven deep learning approach for suggesting process parameters for the milling operations

This work introduces an approach that leverages neural networks to classify 3D models based on volumetric boundaries and systematically organizes machining knowledge into a library of operations to suggest suitable process parameters. The primary objective of this approach is to preserve valuable insights from past machining experiences, and digitally organize and analyze them to improve the efficiency of forthcoming machining processes. This leads to better decision-making in process planning for the new machining components. The methodology revolves around the extraction of geometric and operational parameters from industrial part programs for each tool movement. Formalizing the available process information and creating a knowledge database for all the tool passes in the operations library which contains the relevant information for each tool pass, provides intricate details of the machining process. The proposed approach utilizes the available machining knowledge while producing new and existing parts. Utilizing 3D convolutional neural networks, the approach classifies tool pass geometries within a dataset based on the cutting tool used. This allows further identification and recognition of the geometric and operational similarity amongst the classified volumes through autoencoders. This process leads to the development of a repository of operations that captures the essential process design knowledge, thus fostering the reuse of available machining parameters. The proposed approach demonstrates its effectiveness by implementing it on actual machining data and creating a machining database that assists in making propositions related to operational parameters for similar geometric features during the process planning phase

Defining a Collaborative Framework for Roadmapping Activities

Today, more than ever, it urges to increase effort for monitoring and investigating changes in environment, particularly in relation to events in the social, economic, political and ecological landscapes as well as new technologies. Roadmapping activities are based on techniques and practices to analyse the “state” of a system and to identify evolution of emerging drivers. Roadmapping methodologies can support in understanding the impact of drivers on the competitive position of system under consideration and on the advantage of answering to these drivers. This chapter proposes a collaborative framework, designing it with the aim to involve several stakeholders with an iterative approach to consult and validate the results collected from literature and state-of-the-art and to share knowledge in a context where system competitiveness is considered as a precondition for individual benefit. Overall, this work contributes to improve the effectiveness of strategic roadmapping and to increase its value added to the planning process of clusters and in general of large groups of interest, while providing helpful insight to public organizations that promote the competitiveness of related sector under consideration

Analysis of the Italian Manufacturing Sector

When creating a roadmap, it is important to contextualise the sector under consideration. The work in this chapter is based on identification of relevant indicators which are analysed with a comparative approach both along the time horizon and with other countries and sectors. For this reason, this chapter is based on the extraction of data from International, European, national and regional dataset and describes the Italian manufacturing industry, exploring which are the most relevant sectors, which is the position comparing with European and international countries, and a focus is made on the machine tools sector. The system competitiveness is also analysed in terms of capability to bring innovation to Italy and to the sustainable development goals. The chapter closes with an analysis of the reaction of manufacturing to disruptions like the pandemic crisis and a proposal for a systemic recovery

Digital supply chains for ecosystem resilience: a framework for the Italian case

Contingency where exogenous and dramatic factors (i.e. Covid) impact not only on political and social life but also on economy is changing the way business is managed. Grounded on recent works studying the relationship between digitalisation and resilience, this work aims to systematize the links between the two dimensions at Supply Chains (SC) and at ecosystem level. A conceptual framework for manufacturing companies and policy makers is proposed to cope with disruptions thanks to digital technology implementation. The work is based on the results of an explorative analysis held with the support of practitioners from the manufacturing sector, IT providers and policy makers in Italy to systematise results and to demonstrate that public–private partnership can help to face disruptions. This paper contributes to the theory of ecosystems to establish a systemic framework to go beyond the border of each SC proposing a cross-collaboration model

The Role of Industrial Policies: A Comparative Analysis

The aim of this chapter is to analyse and compare European, Italian and regional industrial policies aimed at promoting the research and innovation activities, with focus on manufacturing sector. The analysis is based on secondary data collected from websites, documents issued by related governmental bodies and grey literature which are compared along scientific topics of interest. Moreover, the chapter discusses how these policies are expected to have an impact on industrial competitiveness and how these policies are interconnected each other. A comparative analysis of the regional and national priorities is also proposed as the result of an iterative collaboration with regional actors. The chapter closes with the analysis of the role of the cluster in supporting industrial policies

Performance evaluation of multi-stage manufacturing systems operating under feedback and feedforward quality control loops

In manufacturing, the essential product characteristics are often created through multiple stages. Coupling product data obtained through inspection and controllers based on decision models with prediction capabilities enables quality control loops, enhancing both feedback and feedforward mechanisms. This paper proposes a methodology to merge the formulation of feedback and feedforward quality control loops into a performance evaluation model for multi-stage manufacturing systems. This approach evaluates quality control loop impacts system-wide, aiding in configuring and reconfiguring quality gates. A case study illustrates how allocating inspection technologies and efficient decision models improves overall system performance through effective feedback and feedforward control loops

Evaluation of Material Shortage Effect on Assembly Systems Considering Flexibility Levels

The global pandemic caused delays in global supply chains, and numerous manufacturing companies are experiencing a lack of materials and components. This material shortage affects assembly systems at various levels: process level (decreasing of the resource efficiency), system level (blocking or s tarvation of production entities), and company level (breaking the deadlines for the supplying of the products to customers or retailers). Flexible assembly systems allow dynamic reactions in such uncertain environments. However, online scheduling algorithms of current research are not considering reactions to material shortages. In the present research, we aim to evaluate the influence of material shortage on the assembly system performance. The paper presents a discrete event simulation of an assembly system. The system architecture, its behavior, the resources, their capacities, and product specific operations are included. The material shortage effect on the assembly system is compensated utilizing different system flexibility levels, characterized by operational and routing flexibility. An online control algorithm determines optimal production operation under material shortage uncertain conditions. With industrial data, different simulation scenarios evaluate the benefits of assembly systems with varying flexibility levels. Consideration of flexibility levels might facilitate exploration of the optimal flexibility level with the lowest production makespan that influence further supply chain, as makespan minimization cause reducing of delays for following supply chain entities