New Perspectives in Manufacturing: An Assessment for an Advanced Reconfigurable Machining System

Traditionally manufacturing cycle involves several production processes that are carried out according to the required technologies tacking into account the constraint due to the production capacity provided by machine tools and the customers' orders time schedule In this paper, a new modular, reconfigurable and scalable machining centre is presented. The resulting system is characterized by the possibility of modifying the machining capacity as well as exchanging the role between workpieces and machining/operating resources. This augmented flexibility creates new opportunities for efficient manufacturing; however, the increased system complexity demands a new approach for the jobs scheduling and machining control. An architecture based on agents modelling is proposed and discussed

Unsulfated biotechnological chondroitin by itself as well as in combination with high molecular weight hyaluronan improves the inflammation profile in osteoarthritis in vitro model

Several studies suggest that inflammation has a pivotal role during the progression of osteoarthritis (OA) and cytokines have been identified as the main process mediators. This study aimed to explore the ability to modulate the main OA pro-inflammatory biomarkers of novel gels (H-HA/BC) based on high molecular weight hyaluronan (H-HA) and unsulfated biotechnological chondroitin (BC). For the first time, BC was tested also in combination with H-HA on human primary cells isolated from pathological knee joints. Specifically, the experiments were performed using an OA in vitro model based on human chondrocytes and synoviocytes. To evaluate the anti-inflammatory effects of H-HA/BC in comparison with H-HA and BC single gels, NF-kB, COMP-2, MyD88, MMP-13 and a wide range of cytokines, known to be specific biomarkers in OA (e.g., IL-6, IL-8, and TNF-α), were evaluated. In addition, cell morphology and proliferation occurring in the presence of either H-HA/BC or single components were assessed using time-lapse video microscopy. It was shown that synovial fluids and cells isolated from OA suffering patients, presented a cytokine pattern respondent to an ongoing inflammation status. H-HA and BC significantly reduced the levels of 23 biomarkers associated with cartilage damage. However, H-HA/BC decreased significantly 24 biological mediators and downregulated 19 of them more efficiently than the single components. In synoviocytes cultures, cytokine analyses proved that H-HA/BC gels re-established an extracellular environment more similar to a healthy condition reducing considerably the concentration of 11 analytes. Instead, H-HA and BC significantly modulated 7 (5 only with a longer treatment) and 8 biological cytokines, respectively. Our results suggest that H-HA/BC beyond the viscosupplementation effect typical for HA-based gels, can improve the inflammation status in joints and thus could be introduced as a valid protective and anti-inflammatory intraarticular device in the field of Class III medical devices for OA treatments

Catalysis over zinc-incorporated berlinite (ZnAlPO4) of the methoxycarbonylation of 1,6-hexanediamine with dimethyl carbonate to form dimethylhexane-1,6-dicarbamate

<p>Abstract</p> <p>Background</p> <p>The alkoxycarbonylation of diamines with dialkyl carbonates presents promising route for the synthesis of dicarbamates, one that is potentially 'greener' owing to the lack of a reliance on phosgene. While a few homogeneous catalysts have been reported, no heterogeneous catalyst could be found in the literature for use in the synthesis of dicarbamates from diamines and dialkyl carbonates. Because heterogeneous catalysts are more manageable than homogeneous catalysts as regards separation and recycling, in our study, we hydrothermally synthesized and used pure berlinite (AlPO₄) and zinc-incorporated berlinite (ZnAlPO₄) as heterogeneous catalysts in the production of dimethylhexane-1,6-dicarbamate from 1,6-hexanediamine (HDA) and dimethyl carbonate (DMC). The catalysts were characterized by means of XRD, FT-IR and XPS. Various influencing factors, such as the HDA/DMC molar ratio, reaction temperature, reaction time, and ZnAlPO₄/HDA ratio, were investigated systematically.</p> <p>Results</p> <p>The XRD characterization identified a berlinite structure associated with both the AlPO₄and ZnAlPO₄catalysts. The FT-IR result confirmed the incorporation of zinc into the berlinite framework for ZnAlPO₄. The XPS measurement revealed that the zinc ions in the ZnAlPO₄structure possessed a higher binding energy than those in ZnO, and as a result, a greater electron-attracting ability. It was found that ZnAlPO₄catalyzed the formation of dimethylhexane-1,6-dicarbamate from the methoxycarbonylation of HDA with DMC, while no activity was detected on using AlPO₄. Under optimum reaction conditions (i.e. a DMC/HDA molar ratio of 8:1, reaction temperature of 349 K, reaction time of 8 h, and ZnAlPO₄/HDA ratio of 5 (mg/mmol)), a yield of up to 92.5% of dimethylhexane-1,6-dicarbamate (with almost 100% conversion of HDA) was obtained. Based on these results, a possible mechanism for the methoxycarbonylation over ZnAlPO₄was also proposed.</p> <p>Conclusion</p> <p>As a heterogeneous catalyst ZnAlPO₄berlinite is highly active and selective for the methoxycarbonylation of HDA with DMC. We propose that dimethylhexane-1,6-dicarbamate is formed <it>via </it>a catalytic cycle, which involves activation of the DMC by a key active intermediate species, formed from the coordination of the carbonyl oxygen with Zn(II), as well as a reaction intermediate formed from the nucleophilic attack of the amino group on the carbonyl carbon.</p

Bees Algorithm Models for the Identification and Measurement of Tool Wear

Bio-inspired computing algorithms are emerging approaches that are based on the principles and vision of the biological evolution of nature to implement new and robust competing techniques. Recently, bio-inspired algorithms have been identified in machine learning to find the optimal solutions of problems in production processes. In this framework, swarm intelligence, which is a subfield of artificial intelligence concerning the intelligent actions of biological swarms by the relationship of individuals in such environments, is used to solve problems in the world by simulating such biological behaviours. Swarm intelligence is defined as the development of intelligent algorithms that mimick the behaviour of different animal societies. In particular, the Bees Algorithm displays the foraging behaviour of honeybees to solve optimisation and search problems. The algorithm performs a sort-of exploitative neighbourhood search combined with random explorative search. This chapter describes the use of the Bees Algorithm in its basic formulation for tool wear identification and measurement during turning operations

Selection of Best Printing Parameters of Fused Deposition modeling using VIKOR

Additive manufacturing process has a huge impact on today’s manufacturing technologies. Due to its design and manufacturing freedom they are very popular amongst many manufacturing plants. 3D printing is one such process which is used to manufacture products in many sectors like aerospace, automobile, construction and in many medical applications. Fused deposition modeling (FDM) is one of the common processes used for manufacturing 3D printed plastic and plastic composite components. FDM process is governed by many process parameters which can have a impact on the performance of this process. This paper focuses on optimization of process parameters of FDM using VIKOR technique

Electronic sensors for intraoral force monitoring: State-of-the-art and comparison

The aim of this work is to provide a comprehensive view on sensors technology for intraoral forces monitoring. State-of-the-art electronic sensors, exploitable for the application of measuring intraoral human forces are compared in this work. Furthermore, a possible configuration for data acquisition from multiple sensors, using Wheatstone bridges to detect the resistance variation and the force output of miniature strain gauges, is also provided

Microbial-based cutting fluids as bio-integration manufacturing solution for green and sustainable machining

Metal working fluids in machining operations, also called cutting fluids (CFs), accomplish the main functions of lubrication between the tool and the work material, cooling down the cutting zone, and washing away the chips from the cutting area. Traditional CFs are either entirely based on mineral oils or, for water-based CFs, contain up to 10% mineral oils. Over time, CFs become contaminated by foreign substances, including bacteria and fungi, causing rancid odour in the work environment and health hazards for the machinists; this contamination is countered by adding biocides, which in turn can be polluting and unhealthy. Conventional CFs, therefore, are potentially pathogenic for humans, deteriogenic for the environment, and costly to dispose of due to the mineral oil and biocide contents. As the global CF consumption amounts to over two million tons/year, the development of greener, more sustainable CFs is highly desired in the manufacturing industry. In this paper, the replacement of mineral oil in CFs with suitable microorganisms providing the lubrication function is studied within a bio-integrated manufacturing approach, with the aim to markedly reduce the negative impact conventional CFs on environment and human health. The turning trials were performed on AISI 1045 steel bars under Small Quantity Lubrication (SQL) conditions using a microbial-based CF containing a microalgae species as lubricant component. The viability and effectiveness of utilising the novel microbial-based CF was positively demonstrated. The cutting forces, tool wear level, surface finish and dimensional accuracy achieved with the microbial-based CF were comparable to or better than for dry cutting and conventional CF

Comparison of Machine Learning Models for Predictive Maintenance Applications

n the field of industry 4.0, one of the sectors in which research is particularly active is the area of Predictive Maintenance(PdM), the purpose of which is to improve the industrial production process. This type of maintenance aims to predict a possible failure event, reduce non-production times and increase the quality of the processing result. The objective of this paper is to select the best Machine Learning models for a PdM application. In particular, such a model should allow making a prediction based on a real dataset, obtained by monitoring a turning process, with the aim of making the classification of the chip shape. The criteria used to choose the best model are accuracy and prediction speed (to reduce the inference time). Indeed it is crucial to spot any potential machine fault in the shortest time possible, in order to intervene before the machine fails. Hence, our goal is to choose the ML models with a lesser inference time while still maintaining high accuracy

A cognitive approach for laser milled PMMA surface characteristics forecasting

In the present work, different Artificial Neural Networks (ANN) architectures were developed and applied to predict the surface characteristics (roughness and depth) of laser milled pockets, performed on poly-methyl-methacrylate (PMMA) sheets. The experimental data were obtained by adopting a 30 W CO2 laser source, fixing the average power at the maximum value and changing the wave mode (continuous or pulsed mode), the scan speed and the etching distance in large range. The depth and the roughness parameters (Ra and Rt), of machined surfaces were acquired by a 3D Surface Profiling System and adopted for the ANN training together with the process parameters. In order to allow network convergence, ANN training was executed by applying a random variable noise to the input data (Rn). The Mean Absolute Percentage Error (MAPE) was adopted to evaluate the ability of ANNs in surface characteristics forecasting. The results show a strong influence of the adopted ANN configuration on the forecasting ability. Nevertheless, a careful selection of the network architecture allows forecasting the roughness with a MAPE lower than 7%

Evaluation and Neural Network Prediction of the Wear Behaviour of SiC Microparticle-Filled epoxy Resins

One of the main advantageous characteristics of thermosetting resins, which enable to apply them as engineering plastics and as matrices for composite materials, is the possibility of optimising their properties in different ways. This work aims to improve the low abrasive wear resistance of an epoxy resin system by adding microscopic silicon carbide powders in different contents and varying particle sizes. Abrasive tests were carried out through a pin on disc apparatus on specimens from different samples and under different working conditions. The tests highlight that plain and reinforced resins’ wear increases both with the contact pressure between the counterparts and the counterface roughness. Moreover, the filled resins' wear resistance increases with the increase of content and dimensions of the filling particles. Finally, an intelligent method based on an artificial neural network was trained, using the experimental dataset, to represent a useful tool for predicting the wear behaviour of plain and filled resins under several working conditions