The performance characterization of the manufacturing processes for additive manufacturing (AM) systems is a significant task for their standardization and implementation in the industry. Also, there is a large diversity of materials used in different AM processes. In the present paper, a methodology is proposed to evaluate, in different directions, the performance of an AM process and material characterization in terms of surface quality. This methodology consists of eight steps, based on a new surface inspection artifact and basic artifact orientations. The proposed artifact with several design configurations fits different AM systems sizes and meets the needs of customers. The effects of main factors on the surface roughness of up-facing platens of the artifacts are investigated using the statistical design of experiments. The proposed methodology is validated by a case study focused on PolyJet material jetting technology. Samples are manufactured of photopolymer resins and post-processed. Three factors (i.e., artifact orientation, platen orientation, and finish type) are considered for the investigation. The case study results show that the platen orientation, finish type, and their interaction have a significant influence on the surface roughness (Ra). The best Ra roughness results were obtained for the glossy finish type in the range of 0.5–4 μm.

Achieving performance is the premise of an organization’s existence on the market. Performance may be achieved by optimal administration of resources, in order to ensure not only short-term but also long-term efficacy. In this sense, performance and sustainability have common support. A sustainable enterprise is, implicitly, a performing enterprise. To be performing or to be able to support sustainable development implies the concern for simultaneous achievement of three categories of objectives: Economic-financial, social and environmental. Therefore, performance measurement requires a global vision of what the entity’s performance means. Thus, the present paper has the major objective of determining the global performance within industrial systems, by indicators that are mainly used to assess the sustainability aspects of the manufacturing systems. Indicators, such as manufacturing costs, quality of manufacturing, energy consumption, personal motivation, and safety, were correlated by an advanced multicriterial analysis. The created model presents the novelty that it provides a total score for performance, allowing to highlight risk areas and to set up improvement measures. The model is an important tool for optimizing the planning processes in order to reduce the consumption of energy, materials or water.

As one of the most widely used acidic condiments in the world, vinegars have demonstrated physiological functions. Due to their polyphenol content, blueberries (Vaccinium myrtillus L.) are a valuable source of natural flavours and antioxidants for vinegar production. Ultrasound treatment is recognized as an effective technique for improving the extraction yield of bioactive compounds from various plant materials. The aim of this research was to investigate the antioxidant properties of experimental vinegar variants obtained by an innovative manufacturing recipe using an alcoholic substrate containing blueberry juice for a rapid initiation of acetic fermentation. The substrate was subjected to ultrasound treatment at a frequency of 20 kHz and an amplitude (A) of 40%, 60%, and 80% for 3, 4, and 5 min. Under these conditions, total polyphenol content (TPC), total anthocyanins content (TAC), antioxidant activity based on ABTS and DPPH assays, as well as the sensory attributes in blueberry-vinegar formulations, were evaluated. The level of TPC and TAC and the antioxidant activity of the developed vinegar variants were optimized using response surface methodology (RSM). The obtained results revealed that ultrasound treatment resulted in increased TPC and TAC and improved antioxidant properties and sensory characteristics of blueberry vinegar. Our data revealed that the optimum values of the ultrasound treatment parameters were amplitude A: 78.50% and time t: 3.96 min. The following predicted values were determined for the main parameters: TPC: 628.01 mg GAE/L, TAC: 22.79 mg C3G/L, ABTS: 391.7 μmol/100 mL, and DPPH: 229.17 μmol/100 mL. The results of this study recommend the integration of both the use of an alcoholic substrate containing blueberry juice and the application of ultrasound treatment in vinegar production as innovative technological interventions with practical applicability for a rapid initiation of acetic fermentation and for improving the antioxidant properties of blueberry vinegar. In addition, RSM can be considered a valuable tool to optimize the ultrasound treatment’s effect on the antioxidant properties of the vinegar formulations.

Sustainable development can be defined in terms of three main components: ecologically sustainable, economically sustainable, and socially sustainable. Conflicting objectives are involved in sustainable development issues, of which the economic, environmental, and social objectives are the most important. Multi-criteria decision-making provides a systematic methodology that combines these conflicting objectives and can provide a rational compromise solution to complex decision-making issues. Using multi-objective optimization, this paper integrated three major objectives of organizational performance, maximizing sales, minimizing expenses, and maximizing productivity, all of which are combined for the sustainable development of the organization. All these conflicting problems were included in an objective function that allowed for solving the three objectives simultaneously and, thus, achieving economic, environmental, and social performance, in order to sustainably develop the organization. The input data included the objectives planned by the company regarding the turnover to be achieved, the production and delivery costs, and the productivity achieved by employees. Customer orders, production capacity, delivery costs, and resource consumption were also taken into account for optimization. With the help of multi-objective linear programming, the optimal number of parts produced and delivered by the company was determined so as to reach its economic, environmental, and social performance.

Plastic parts used in automotive interior are difficult to coat, due to their low surface energies as well as their sensitivity to temperature and solvents, rendering the development of coating systems for such substrates challenging. Automotive customer requirements are explicit and clear, mainly focused on functional and surface defects. A new failure modes detection methodology of UV clear coated polymers for automotive interior, obtained by a multi-step manufacturing process, is proposed. The polymer complex parts analyzed in this paper are manufactured in various steps as follows: two components plastic injection molding, primer coating, laser engraving, and UV-cured clear coating. The failure modes detection methodology of the parts within each process step is investigated using different tests and analyses as follows: surface tension test, painting adhesion test, optical 3D measuring, energy dispersive X-ray analysis (EDX), and microscopy. A design of the experiments (DoE) based on the Taguchi technique with the aim to detect the influence of the main factors that lead to surface defects was performed. The proposed methodology is validated by a case study. The results showed that the mold temperature and the laser engraving current have a significant influence on the surface defect occurrence. Additionally, a possible contamination of the molding tool can generate the defects. A solution to reduce the occurrence of the failures was proposed, reducing the defect rate from 50% to 0.9%.

The tourism industry is currently experiencing a substantial digital transformation, with Online Travel Agencies (OTAs) committed to integrating innovations such as artificial intelligence (AI) in order to enhance service delivery and personalize user experiences. This study investigates the relationship between the utilization of AI and the technical quality scores of tourism websites, aiming to identify significant associations and variances in the critical conversion phase. An exploratory research design is employed to evaluate the technical quality of three prominent international tourism websites, i.e., Booking.com, Airbnb.com, and Tripadvisor.com. The investigation uses Google Lighthouse, with Performance, Accessibility, Best Practices, and Search Engine Optimization (SEO) as variables analyzed across both desktop and mobile versions, as well as on pages with and without AI functionality. Data analysis is performed using JASP (version 0.19.3), including linear regression analysis to quantify the predictive relationship. The analysis confirms that the Performance variable is the most sensitive to the influence of AI. AI integration demonstrates a significant positive influence on the Performance score of travel websites. The regression model indicates that AI usage explains 78.9% of the variation in the Performance score (${\mathrm{R}}^2$ = 0.789, p < 0.001), indicating a substantial correlation with technical sustainability. Nevertheless, there remains an ongoing necessity for optimization, particularly with regard to the enhancement of overall performance and improvement of scores for mobile devices. The study acknowledges certain limitations related to the sample size of AI applications and the accessibility of specific AI versions in particular geographic regions.