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Purpose The purpose of this paper is to develop a multidimensional innovation index (MII) framework for measuring and benchmarking multidimensional innovation of small and medium enterprises (SMEs) and groups of SMEs. Design/methodology/approach A counting dual cut-off method is employed. First, output and input innovation profiles and composite scores of individual SMEs are computed. Second, a set of four composite innovation indices are generated characterizing the group of SMEs under analysis: MIIo and MIIi measure multidimensional output and input innovation, respectively; while MIIr and MIIa assess the ratio and average of MIIo and MIIi, respectively. To test the MII framework, a survey was conducted among SMEs of the metalworking industry in Portugal. Findings In 2012, about 28.9 percent (42.2 percent) SMEs of the Portuguese metalworking industry were determined to be multidimensional output (input) innovative. The average percentage of dimensions for which output (input) innovative SMEs were innovative was 65.0 percent (66.0 percent). Thus, the industry MII vector was (MIIo; MIIi; MIIr; MIIa)=(0.188, 0.279, 0.674; 0.233). Significant differences were found across the industry, individual SMEs’ multidimensional output and input innovation scores, enabling the identification of groups of SMEs, which can be characterized and compared by computing the corresponding and specific MII vectors. Research limitations/implications The research has limitations because of the small size of the sample and the benchmarking possibilities it provides. Originality/value The novelty of the MII framework lies in the counting dual cut-off method employed.

Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model.

Metalworking fluids (MWF) are water- or oil-based liquids to cool and lubricate tools, work pieces and machines, inhibit corrosion and remove swarf. One of the major problems in the MWF industry is bacterial growth as bacterial enzymes can cause MWF degradation. In addition, bacteria can form biofilms which hamper the functioning of machines. Last but not least, some bacterial by-products are toxic (e.g. endotoxins) and present potential health risks for metalworking machine operators via the formation of aerosols. Therefore, a novel fast yet accurate analytical method to evaluate and predict the antibacterial capacity of MWF would be an important asset. As such a tool is currently lacking, the present study aimed to develop a protocol based on flow cytometry (FCM) to assess the antibacterial potential of newly developed MWF independent of bacterial growth. Results of this novel method were compared to a biochallenge test currently used in MWF industry and also to traditional plate counts. Our results represent a proof-of-principle that FCM can reliably predict the antibacterial capacity of MWF already within one day of incubation with Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Proteus mirabilis, being substantially faster than the current growth-based methods.

The excavations at Tel Reḥov yielded hundreds of metal objects and numerous metallurgical remains that attest to on-site metalworking. The study of such remains and their distribution sheds light on metalworking practices at the site, indicating a considerable change between the Late Bronze Age and the Iron Age, which comprised both the choice of raw materials (bronze vs. iron), their origin, and metalworking traditions. Particularly significant is the evidence for an Egyptian metalworking tradition during the Late Bronze Age, indicating that the metal industry at Reḥov may have been tightly controlled by the neighboring Egyptian stronghold at Beth-Shean. Iron Age I metallurgical remains suggest that the smiths reverted to Canaanite metalworking practices after the Egyptians’ departure. Iron was introduced into common use during the Iron Age IIA, the tenth and ninth centuries BCE.

Metalworking fluids (MWFs) are widely used to cool and lubricate metal workpieces during processing to reduce heat and friction. Extending a MWF’s service life is of importance from both economical and ecological points of view. Knowledge about the effects of processing conditions on the aging behavior and reliable analytical procedures are required to properly characterize the aging phenomena. While so far no quantitative estimations of ageing effects on MWFs have been described in the literature other than univariate ones based on single parameter measurements, in the present study we present a simple spectroscopy-based set-up for the simultaneous monitoring of three quality parameters of MWF and a mathematical model relating them to the most influential process factors relevant during use. For this purpose, the effects of MWF concentration, pH and nitrite concentration on the droplet size during aging were investigated by means of a response surface modelling approach. Systematically varied model MWF fluids were characterized using simultaneous measurements of absorption coefficients µa and effective scattering coefficients µ’s. Droplet size was determined via dynamic light scattering (DLS) measurements. Droplet size showed non-linear dependence on MWF concentration and pH, but the nitrite concentration had no significant effect. pH and MWF concentration showed a strong synergistic effect, which indicates that MWF aging is a rather complex process. The observed effects were similar for the DLS and the µ’s values, which shows the comparability of the methodologies. The correlations of the methods were R2c = 0.928 and R2P = 0.927, as calculated by a partial least squares regression (PLS-R) model. Furthermore, using µa, it was possible to generate a predictive PLS-R model for MWF concentration (R2c = 0.890, R2P = 0.924). Simultaneous determination of the pH based on the µ’s is possible with good accuracy (R²c = 0.803, R²P = 0.732). With prior knowledge of the MWF concentration using the µa-PLS-R model, the predictive capability of the µ’s-PLS-R model for pH was refined (10 wt%: R²c = 0.998, R²p = 0.997). This highlights the relevance of the combined measurement of µa and µ’s. Recognizing the synergistic nature of the effects of MWF concentration and pH on the droplet size is an important prerequisite for extending the service life of an MWF in the metalworking industry. The presented method can be applied as an in-process analytical tool that allows one to compensate for ageing effects during use of the MWF by taking appropriate corrective measures, such as pH correction or adjustment of concentration.

ObjectiveTo estimate the reported incidence of occupational hypersensitivity pneumonitis (OHP) in the UK and to consider whether the pattern of attributed causation has changed over time.MethodsAll cases of OHP reported to the SWORD scheme between January 1996 and December 2015 were classified into 1 of 10 categories of the suspected agent. Cases were grouped into four 5-year time periods to examine any changing pattern in incidence or suspected causation. For each time period, the annual incidence was calculated using the estimated number of reported cases and the working population of the UK.ResultsBetween 1996 and 2015, there were 202 actual cases of OHP reported to SWORD, equating to an estimated 818 cases, when adjusting for the sampling ratio. Over this period, the annual UK incidence was 1.4 per million workers. The mean (SD) age of reported cases was 52 (13) years, and cases were four-times more likely to be men than women. Over the study period, there was a fall in the proportion of cases reported to be due to agricultural exposures (44–12%), and an increase in cases due to metalworking fluids (MWFs, 2–45%).ConclusionsOver the last 20 years, the incidence of OHP in the UK has been ∼1–2 cases per million workers per year. Working with water-based MWFs is now the most commonly suspected causative exposure for OHP cases reported to the SWORD scheme in the UK.

We measure the impact of a drastic new technology for producing steel—the minimill—on industry-wide productivity in the US steel industry, using unique plant-level data between 1963 and 2002. The sharp increase in the industry's productivity is linked to this new technology through two distinct mechanisms: (i) the mere displacement of the older technology (vertically integrated producers) was responsible for a third of the increase in the industry's productivity, and (ii) increased competition, due the minimill expansion, drove a productivity resurgence at the surviving vertical integrated producers and, consequently, the productivity of the industry as a whole.

Up to date, most metalworking fluids (MWFs) are emulsions made of petroleum-derived oil bases and sodium petroleum sulphonate emulsifiers. They are not readily biodegradable, and their waste is hazardous for users and the environment. Therefore, green MWFs are required for achieving cleaner production processes. Recently, various MWFs have been developed using vegetable oil bases to meet biodegradability to some extent. However, the emulsifier has been scarcely replaced by a green product. This research aims to produce and evaluate Pickering emulsions made of Jatropha oil (JO) and partially deacetylated and fibrillated chitin (PDFC) as emulsifiers at different concentrations. JO is a non-edible biodegradable oil with remarkable lubricity properties, while PDFC is produced by extracting chitin from waste heads and shells of the shrimp species Litopenaeus vannameii, followed by partial deacetylation and further fibrillation, which improves wettability and stabilization. The prepared emulsions were characterized in terms of creaming index and size of emulsion droplets and evaluated as MWFs in actual turning operations of AISI 1018 steel bars via minimum quantity lubrication (MQL) technique. The findings suggest PDFC as a potential eco-friendly emulsifier to form green MWFs with acceptable stability generating low cutting forces and significant workpiece finishing and chips quality.

With the growth of the metalworking industry, effective control of wastewater with phosphate has become a global concern. This study took advantage of the abundant supply of natural soybean hulls as an adsorbent for the direct treatment of wastewater, aiming to remove Ni, Zn, and Mn from real wastewater produced during the phosphating stage of the metalworking industry to address this issue. Soybean hulls presented a specific surface area of 0.31 m 2  g −1 , average diameter of 0.2705 mm, and a pH value for PCZ of 6.43 at 25 °C. Real wastewater was acidic (pH 3.68) with COD of 1270 mg L −1 and highly concentrated in Ni, Mn, and Zn (343.45 mg L −1 , 818.6 mg L −1 , and 953.85 mg L −1 , respectively). It was observed that the process depended on the adsorbent dosage, which can be linked to the low surface area of the material. The optimized pH value was found to be the natural pH of the effluent, which varied between 3 and 4. The average removal rates were 24.5% for Ni, 28.6% for Zn, and 16.5% for Mn, corresponding to the respective removal of 84.15, 135.07, and 272.80 mg L −1 in a ternary system. The maximum adsorption capacities were observed at 50 °C, estimated as 3.125 mg g −1 for Ni, 14.128 mg g −1 for Zn, and 7.8 mg g −1 for Mn. When evaluating the process kinetics, it was observed that adsorption capacity increased significantly during the initial 60 min, followed by a slower rate until saturation. The pseudo-first-order model provided the best fit for Ni adsorption, while Zn and Mn demonstrated the best fit with the pseudo-second-order model. This trend possibly occurred due to the different initial concentrations of each metal, which has shown to be a key factor in mass-driven adsorption mechanisms. Thus, using raw soybean hulls can be considered a viable alternative for coupling adsorption as a low-cost step to other treatment methods for metalworking wastewater.

This study aimed to identify latent variables that could better characterize an organization’sresilience potential within a sample predefined by nine attributes based on the ISO 22316:2017standard, and to assess the possibility of optimizing it. To achieve this, empirical research wasconducted among Polish foundry companies during the COVID-19 crisis. A questionnaire,consisting of 48 questions was designed to investigate these attributes. Each organizationalresilience attribute was examined using 4–6 questions (38 questions in total). Additionally, theresearch included questions about situational awareness and proactive attitude as importantcharacteristics closely related to organizational resilience, with five questions per factor (10questions in total). The study employed both exploratory and confirmatory factor analysis(EFA, CFA), along with structural equation modeling (SEM). The analysis outcomes allowedthe authors to construct a structural model of organizational resilience potential based onthree principal latent variables corresponding to the thematic areas of resilience potential:Variable F1 – Adaptation and Resourcefulness, Variable F2 – Leadership and Anticipation,and Variable F3 – Partnership and Communication. Through optimization, the numberof attributes was streamlined from nine to three, and the number of questions describing theseattributes was reduced from 38 to 19. Moreover, a substantial correlation was identified betweenproactive attitude, situational awareness, and organizational resilience potential. This studyprovides management personnel with valuable insights to formulate new decision-makingstrategies by focusing on the attributes of organizational resilience potential that exert the mostsignificant influence on its variability. Ultimately, these findings could enhance organizationalperformance during disruptions and foster the sustainable development of enterprises.