The Opportunities and Challenges of SME Manufacturing Automation: Safety and Ergonomics in Human–Robot Collaboration

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Somatostatin subtype-2 receptor-targeted metal-based anticancer complexes

Conjugates of a dicarba analogue of octreotide, a potent somatostatin agonist whose receptors are overexpressed on tumor cells, with [PtCl 2(dap)] (dap = 1-(carboxylic acid)-1,2-diaminoethane) (3), [(η 6-bip)Os(4-CO 2-pico)Cl] (bip = biphenyl, pico = picolinate) (4), [(η 6-p-cym)RuCl(dap)] + (p-cym = p-cymene) (5), and [(η 6-p-cym)RuCl(imidazole-CO 2H)(PPh 3)] + (6), were synthesized by using a solid-phase approach. Conjugates 3-5 readily underwent hydrolysis and DNA binding, whereas conjugate 6 was inert to ligand substitution. NMR spectroscopy and molecular dynamics calculations showed that conjugate formation does not perturb the overall peptide structure. Only 6 exhibited antiproliferative activity in human tumor cells (IC 50 = 63 ± 2 μ in MCF-7 cells and IC 50 = 26 ± 3 μ in DU-145 cells) with active participation of somatostatin receptors in cellular uptake. Similar cytotoxic activity was found in a normal cell line (IC 50 = 45 ± 2.6 μ in CHO cells), which can be attributed to a similar level of expression of somatostatin subtype-2 receptor. These studies provide new insights into the effect of receptor-binding peptide conjugation on the activity of metal-based anticancer drugs, and demonstrate the potential of such hybrid compounds to target tumor cells specifically. © 2012 American Chemical Society

Formal Tools for Management of Manufacturing Systems: A Multi Agents System Approach

To overcome the consequences of the 2008 crisis on the real sector, especially manufacturing, Industry 4.0 gives guidelines to drive production by emphasizing technological innovations, such as industrial internet, cloud manufacturing, etc. The proposed paper focuses on cognitive manufacturing within the framework of the emergent synthesis paradigm. Specifically, the structuring process by which the manufacturing assets are organized to provide the finished goods is analyzed. The study is carried out by considering the analogies between manufacturing and other inventive processes supported by formal tools such as formal languages, semantic webs, and multi agent system

Abrasive grains micro geometry: a comparison between two acquisition methods

One of the aspects that makes difficult grinding processes modelling is the non-deterministic nature of the cutting tool, in particular the abrasive grains of the grinding wheel have a random distribution and an undefined geometry that influences the grinding forces. In order to develop a reliable 3D model of the grinding process the actual microgeometry of abrasive grains must be acquired. This paper compares the results of two different acquisition methods: the geometry acquired via a laser non-contact instrument is confronted with the one acquired using a computer tomography; the accuracy of the grain micro geometry provided by the two approaches is discussed

Dressing tool condition monitoring through impedance-based sensors: Part 2—neural networks and K-nearest neighbor classifier approach

This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (k-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation

Dressing tool condition monitoring through impedance-based sensors: Part 1—pzt diaphragm transducer response and emi sensing technique

Low-cost piezoelectric lead zirconate titanate (PZT) diaphragm transducers have attracted increasing attention as effective sensing devices, based on the electromechanical impedance (EMI) principle, for applications in many engineering sectors. Due to the considerable potential of PZT diaphragm transducers in terms of excellent electromechanical coupling properties, low implementation cost and wide-band frequency response, this technique provides a new alternative approach for tool condition monitoring in grinding processes competing with the conventional and expensive indirect sensor monitoring methods. This paper aims at assessing the structural changes caused by wear in single-point dressers during their lifetime, in order to ensure the reliable monitoring of the tool condition during dressing operations. Experimental dressing tests were conducted on aluminum oxide grinding wheels, which are highly relevant for industrial grinding processes. From the results obtained, it was verified that the dresser tip diamond material and the position of the PZT diaphragm transducer mounted on the dressing tool holder have a significant effect on the sensitivity of damage detection. This paper contributes to the realization of an effective monitoring system of dressing operations capable to avoid catastrophic tool failures as the proposed sensing approach can identify different stages of the dressing tool lifetime based on representative damage indices

Feasibility study of using microorganisms as lubricant component in cutting fluids

This work intends to lay the basis for a Biological Transformation in Manufacturing (BTM) industrial breakthrough aimed at developing new sustainable microbial-based cutting fluids for greener machining processes. The point of departure is that conventional cutting fluids for machining are either entirely based on mineral oils or, in the case of water-based cutting fluids, contain significant percentages of mineral oils. The world annual consumption of cutting fluid concentrate amounts to several billion litres, highlighting their importance for the manufacturing industry and their criticality in terms of environmental impact. Furthermore, the depletion of mineral oil reserves worldwide is already driving cutting fluid industries to search for new renewable raw materials. A contribution to the solutions of these problems can be provided by the development of microbial-based cutting fluids, incorporated in the machine tool, that contain microorganisms with significant lubricating properties in order to substitute mineral oil-based cutting fluids for use in metal alloy machining

An improved impedance-based damage classification using Self-Organizing Maps

The identification and severity of structural damages, especially in the early stage, is critical in structural health monitoring (SHM) systems. Among several approaches used to accomplish this goal, the electromechanical impedance (EMI) technique has taken place within non-destructive evaluation (NDE) methods. On the other hand, neural networks (NN) based on self-organizing maps (SOM) has been a promising tool in many engineering classification problems. However, there is a gap of application regarding the combination of the EMI technique and SOM NN. To encourage this, an enhanced EMI-based damage classification method using self-organizing features is proposed in the present research paper. A SOM NN architecture was implemented whose inputs were derived from representative features of the impedance signatures. As a result, self-organizing maps can be used as an effective tool to enhance the damage classification in EMI-based SHM applications. For the present application, the results indicated a promising and useful contribution to the grinding field