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The imperative shift toward sustainable development mandates a revolutionary transformation in the transportation sector. While the transition from internal combustion engines to electric motors represents a pivotal step, its ecological impact may fall short of expectations. This undertaking is motivated by the recognition that a truly impactful transition necessitates intricate strategies, addressing resource consumption during manufacturing and minimizing post-use waste. This literature review is dedicated to enhancing the framework of circular strategies within the realm of electric motors, with a specific focus on remanufacturing. The objective is to cultivate a conscientious approach to product development, primarily centered on the four key pillars of circularity: reuse, remanufacture, upgrade, and recycle. The primary goal is to furnish a comprehensive overview of the myriad possibilities associated with reusing products, either in parts or as a whole. Upon introducing the circular approach, this review underscores pivotal enabling technologies essential for a successful transition. These include inspection, disassembly, rewinding, and demagnetization. The ensuing discussion delves into the technological challenges faced and presents cutting-edge solutions poised to redefine the landscape. The concluding section extends the perspective to the future, introducing innovative studies and solutions still in their developmental stages. This review is tailored for decision-makers, policymakers, and researchers, providing them with a valuable overview and facilitating discussions to propel our society toward a sustainable and responsible future in electric motor technology.

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.

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.

Recent push towards sustainable development have lead machine tool builders to begin adopting novel business models which are usually peculiar in product-service-systems. In particular, business models as ‘pay-per-use’ introduced the issue of rental of production capacity integrated with the management of the life cycle of assets in a Circular Economy perspective. However, when it comes to machine tool builders, the high degree of customization of capital goods and in particular of multi-stage automated assembly systems that are strongly linked to the characteristics of the final product make this solution more difficult. Hence, the implementation of such business models represents a clear challenge for machine tool builders and manufacturing system suppliers from technological perspective. This work discusses preliminary results in the technological implications of such strategy. A real industrial case related to cross-sector multi-stage automated assembly systems is presented to highlight the technological challenges and potential research directions.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ijpe.2007.07.011 Byline: Marcello Colledani, Tullio Tolio Keywords: Statistical quality control; Performance evaluation; Decomposition methods; Inspections; Quality information feedback Abstract: Quality and productivity performance measures are very often considered in separate phases of the production system design process. However, the production system architecture affects the efficiency of the quality control system as well as the quality control configuration has an impact on the performance of the production system. The paper proposes a new analytical method for evaluating the performance of production systems in which statistical process control (SPC) techniques are implemented. Machines behaviour is monitored by measuring quality characteristics of the produced parts through off-line inspection devices and sampling inspections. The numerical results show the good accuracy of the proposed method, provide new insight in the relations among the two areas and pave the way to the joint design of production logistics and quality control systems. Author Affiliation: Dipartimento di Meccanica, Politecnico di Milano, Via Bonardi 9, Italy Article History: Received 23 March 2007; Accepted 27 July 2007

Surveillance cameras at traffic lights are saving lives in the U.S, but opponents are finding new issues to raise against them. However, it has long been argued that these are primarily used to increase collections or as an election-enforcement system.

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 starvation 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.

Remanufacturing of end-of-life products and parts is seen as a solution in the transition towards a circular economy. There are many proposed tools and methods to facilitate the application of this circular strategy, however, among them, there is a lack of support tools for practitioners that include multiple perspectives related to the value chain and circular economy. In fact, remanufacturing strategy, economic and environmental trade-offs, and circularity indicators are rarely integrated within one framework. In this paper, an approach is presented taking advantage of the state-of-the-art research on green profit model and circularity indicators; in other words, these tools are used together to unlock the circular potential in manufacturing practice. In this way, typical problems of production planning and control in remanufacturing processes are interconnected with the goals of sustainable development, also considering product design and end-of-life strategy choices. The presented framework represents a promising support to be used in industrial practice. A case study based on PV panel infrastructure allows a better comprehension of the research outputs and assesses the validity of the support provided by the framework in the deployment of circular economy strategies.

The work gives a classification of the various 'Single Shot heuristics' proposed in the literature for the solution of the Static Permutation Flowshop (SPF) problems. It then tries to compare their performances and draw a ranking. Since the ranking cannot be univocal, the aim is to show how it is affected by various elements that characterize a SPF problem (objective function, number of jobs, number of machines, processing time matrix). The final purpose is to give some guidelines for the choice of the proper algorithm on the basis of the specific 'environmental conditions' in which the algorithm will operate.

Mass customization requires frequent product changeovers thus leading to the need of manufacturing systems endowed with flexibility and reconfiguration capabilities, in order to be robust to changes in the production scenario. Therefore, manufacturing companies face a relevant risk when taking strategic decisions about which system resources should be acquired. This risk can be mitigated by exploiting performance evaluation models, such as analytical models and Discrete Event Simulation, that are effectively adopted to estimate the performance of possible system configurations. However, current decision-support tools for optimizing system configurations can be only loosely coupled with performance evaluation models, hence undermining the actual optimization of the system itself, even more if production requirements may evolve in the future.

This paper presents a Markov Chain approximation to model stations in manufacturing lines with general distributed processing times. The proposed Markov Chain approximation enables the use of continuous flow models for the performance evaluation of serial lines with finite buffers and mixed manual – automated operations. Each station in the line can consist of a highly automated machine with deterministic processing times, or of a human operator performing manual operations with general distributed processing times. Stations with random processing times are modelled through a continuous time – discrete state Markov Chain characterized by an operational state with a deterministic processing time, and by an auxiliary down state used to stochastically dilate the overall completion time of a part on the station. The Markov Chain parameters are defined through moments fitting of the probability distribution of the processing time of the original station. The resulting Markov Chain represents the behavior of the station in isolation and is then used as input in the decomposition techniques, based on continuous flow models, for the performance evaluation of serial lines. The model has been applied in the analysis of the production performances of a real assembly line.

Sustainability has become a key factor for competitiveness. Sustainable production strategies aim at the scrap reduction of in-process inventory. In some sectors such as food or pharmaceutical sector, in-process inventories must be scrapped after a certain time due to perishability. Therefore, local policies are usually applied to machines in order to mitigate the impact of disruptive events leading to scrap.

This study describes the lessons learned from designing, deploying and analysing the results from different travel satisfaction survey tools which measures the travellers’ door-to-door travel satisfaction. The travel satisfaction measurement survey tools tested consisted of two types of smartphone applications (a satellite navigation app and a game app), an on-line survey, a paper-based semi-structured questionnaire and a focus group questionnaire. Each of the measurement tools comprised the same set of basic questions, but in different formats, aimed at exploring the pros and cons of each tool among different groups of travellers. The data collection was carried out at eight different European cities and five FIA motorist networks. 5,275 valid responses were gathered from the survey. Further analysis results show that different survey methods performed better in different sites. The satisfaction that was gathered via main trip leg does not necessarily correspond with overall satisfaction of the door-to-door journey. The results of this study highlight the need for more inclusive, complete, door-to-door, travel survey measurements.

Identifying microbial strains and characterizing their functional potential is essential for pathogen discovery, epidemiology and population genomics. We present pangenome-based phylogenomic analysis (PanPhlAn; http://segatalab.cibio.unitn.it/tools/panphlan), a tool that uses metagenomic data to achieve strain-level microbial profiling resolution. PanPhlAn recognized outbreak strains, produced the largest strain-level population genomic study of human-associated bacteria and, in combination with metatranscriptomics, profiled the transcriptional activity of strains in complex communities.

According to both industrial practice and literature, multi-robot cell design and robot motion planning for vehicle spot welding are two sequential activities, managed by different functional units through different software tools. Due to this sequential computation, the whole process suffers from inherent inefficiency. In this work, a new methodology is proposed, that overcomes the above inefficiency through the simultaneous resolution of design and motion planning problems. Specifically, three mathematical models were introduced that (i) select and positions the resources, (ii) allocate the tasks to the resources and (iii) identify a coordinated robot motion plan. Based on the proposed methodology, we built three ad-hoc cases with the goal to highlight the relations between design, motion planning and environment complexity. These cases could be taken as reference cases so on. Moreover, results on an industrial case are presented.

The adoption of alternative process plans, that is, process plans that include alternative ways of machining a workpiece, can improve system performance through a better management of resource availability. Unfortunately even if this opportunity is deeply analysed in literature, it is not frequently adopted in real manufacturing practice. In order to fill this gap, this article presents the network part program (NPP) approach for the machining of multiple fixture pallets. The NPP approach is based on the STEP-NC data structure which supports nonlinear sequences of operations and process flexibility. In the NPP approach, a machining system supervisor defines the machining sequences and generates the related part programs just before the execution of the pallet. This article provides an approach with high scientific value and industrial applicability based on the integration of new and existing process planning methods. A real industrial case study is considered in order to show that in real applications the final quality is unaffected by the change of the sequence of the operations due to the employment of nonlinear process plans. Since the results appear very encouraging, the proposed approach is a possible solution to accelerate the adoption of nonlinear process planning in real manufacturing practice.

The multi-robot cell design for car-body spot welding is faced by industry as a sequence of tasks, where researches are focused on issues of the problem as a whole. In authors’ knowledge, none work in literature have suggested any formalization for the complete process. This paper tries to bridges the gap proposing coherent process formalization, and presenting a corresponding innovative architecture for the automatic optimal cell design. Specifically, the formalization involves the identification and allocation of the resources in terms of a set of decisional variables (e.g. robot model/positioning/number, welding gun models/allocation/number, welding point allocation etc.); then, the design optimization process minimizes the investment costs granting the cycle time. The multi-loop optimization architecture integrates both new algorithms and existent procedures from different fields. Test-bed showing its feasibility is reported.

De-Manufacturing Systems allow implementing optimized End-Of-Life strategies and are necessary to support a sustainable and competitive Manufacturing/De-Manufacturing integrated paradigm. However, available technologies, management methods and business models present several limitations that make landfill and, at a lower extent, materials recycling, the most diffused End-Of-Life practices. To overcome these limitations, this paper proposes an integrated multi-disciplinary research framework addressing single technologies improvement, system integration and business model coherency. The main challenges and research opportunities are presented that can boost the development of sustainable De-manufacturing Systems at industrial level.

Competitive manufacturing companies have to effectively deal with the concurrent evolution of products, processes and production systems. This problem, known as Co-evolution, can be addressed only through the integrated use of different methodologies, provided that the digital tools implementing these methodologies can interoperate properly and effectively. This paper presents the concept of an integrated framework to support the interoperability between digital factory tools and shows how it can benefit the business processes along the whole factory life-cycle.

The configuration phase of a production system is characterized by the need of evaluating a large number of system alternatives. In this phase, a fast and reliable performance evaluation tool able to analyze and explain the behaviour of the system is necessary. The paper proposes an approximate analytical method based on decomposition techniques for modelling and evaluating the performance of production systems involving split and merge of production flows, multiple products, buffers with finite capacity and manufacturing/assembly/disassembly operations. The paper also reports the application of the method to the performance evaluation and reconfiguration of a real system producing white goods.

One of the main problems faced while configuring or reconfiguring manufacturing systems is to rank alternative designs taking into account all the different aspects involved (both tangible and intangible). For this purpose the Analytic Hierarchy Process (AHP) is a well-known decision making support method that addresses this problem. A major drawback of AHP is that uncertainty in the judgments of the decision makers and the resulting impact on the ranking is not considered. In real situations, however, judgments based on perceived future scenarios are almost always uncertain. To solve this problem in this paper we present the first complete probabilistic extension to the AHP method. The new method provides the decision maker not only with information on the ranking of the alternatives but also the probability that the ranking remains stable even in presence of uncertainty in the judgements. We verified the validity of the new method in a real application developed for the Ferrari racing team.

This paper is about the automation of the pre-analytical phase in a biochemical laboratory, which performs more than 3.7 million tests per year. The paper presents how an investment in the laboratory can be evaluated considering both economic criteria, future performance and service quality. The study comprises a workflow analysis of biological materials and information, the corresponding data collection and the use of simulation for performance evaluation. Alternative scenarios have been considered in terms of personnel, pre-analytical devices and management policies. The final scenario has been chosen according to economic criteria among a set of feasible scenarios, able to satisfy constraints.

The decision on production system acquisition for the automotive industry is very critical, given the number of different aspects to be considered. Indeed different automated solutions are feasible and evaluation techniques that take into account all the critical issues are needed to make a selection. In this paper a complete, precise and value driven Decision Support System is presented to support the selection of the best Automated Manufacturing System. The evaluation problem is solved using a Fuzzy Analytic Hierarchy Process (AHP) method able to manage uncertainty and to consider productivity and flexibility issues. Economic and financial performance and the effects on human resources due to the investment decision are also investigated.

Loading in flexible manufacturing systems (FMSs) is affected by the characteristics of the FMS under analysis, by the type of plant where the FMS is introduced, and by the production planning hierarchy where the loading module operates. We propose an analysis of the various aspects that influence the problem formulation, identifying the alternatives available in real systems and possible future evolutions. We then provide a survey of different approaches proposed in the literature to tackle the loading problem. Articles are classified according to the type of FMS analyzed, the objective function, and the constraints. Finally, based on our analysis, we suggest some problem issues which need to be addressed, and also directions for future research.

The paper deals with the problem of tooling configuration in FMSs. In particular it presents an analytical solution to the problem of evaluating the performance of a FMS for a given tooling configuration. The method proposed can deal with situations in which the service time may have any probability distribution, considering its first two moments. In comparison with previous models, the method proposed can take into account the existing dependence among tools.

Tooling configuration deeply affects the performance of Integrated Manufacturing Systems both in terms of flexibility and productivity. The literature on tooling configuration is extremely scarce and due to the lack of formal methods oversizing is a common practice which leads to high investments in tools.

In order to find the optimal configuration of an automated flow line it is necessary to compare the performance of several alternative configurations. In this situation the use of simulation is normally not viable given the high modelling and computational effort it would require; on the other hand, the application of classical analytical tools would often entail the adoption of unrealistic assumptions. To solve this problem, the paper presents an approximate analytical method that overcomes many of the limitations of analytical tools while preserving good accuracy and low computational effort. In particular the method can deal with deterministic processing times, finite buffer capacity and multiple failure modes of the machines. This last feature represents an important improvement over existing techniques and is very useful when dealing with automated assembly lines where each station is composed by different devices that have different reliability and different ways to get repaired. The validation of the method has been carried out by means of extensive testing and comparison with simulation. An application to a real case is also reported.

The relation of cancer to ABO groups was studied in 2590 patients with malignant lesions of different sites (except for gastric tumours), in 171 cases of leukemia, and in 117 cases of Hodgkin's disease. 9607 Mood donors were kept as controls.A prevalence of the A group was observed in tumours of the esophagus (148 cases), of the uterus and ovary (188 cases), in myeloid leukemia (71 cases), and in Hodgkin's disease (117 cases). A prevalence of the 0 group was observed in the 37 patients with lymphatic leukemia. However, A and 0 group prevalence was not found to be statistically significant.No relation to ABO groups was demonstrated in tumours of the colon and rectum (489 cases), larynx (121), lung (806), prostata (111), urinary bladder (120), kidney (71), liver and biliary tract (200), and breast (344).The study of larger series of cases is advised for a better evaluation of the relation between cancer and ABO groups.

The association of groups ABO with gastric cancer (1333 cases), gastric ulcer (343 cases) and duodenal ulcer (740 cases) has been studied.No correlation has been observed between gastric cancer and blood groups, whereas an heterogenic distribution of blood groups in relation to tumor site could be noted: e. g., group A was found to be associated to antral cancer, and group O to the cancer of the body and of the fundus.A highly significant heterogeneity of distribution of the groups ABO in the different ages was observed in patients bearing gastric ulcer.The correlation between duodenal ulcer and group O has been confirmed. The relative disease incidence for the subjects belonging to this group was demonstrated to be 55 per cent higher than that of group A subjects.No sex influence on groups ABO distribution was observed in the three studied conditions.