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New sustainable approaches to fashion products are needed due to the demand for customization, better quality and cost reduction. Therefore, the decoration of fashion products using 3D printing technology can create a new direction for manufacturing science.

This study aims to optimize the 3D printing of soft TPU material on textiles. In the past decade, trials of using 3D printing in tailored fashion products have been done due to the 3D printing simplicity, low cost of materials and time reduction. Therefore, soft polymers can be multi-layer stepped-deposited smoothly with the fused filament fabrication process.

Even though there have been many attempts in the literature to 3D print multilayer polymer filaments directly onto textile fabrics by special-purpose 3D printers, only a few reports of decorative or personalized artefact 3D printing using open-platform filament material extrusion 3D printers. Printing speed, nozzle Z distance, textile fabric thickness and deposited strand height significantly affect 3D printing on textile fabric.

This study investigates the potential of 3D printing on textiles by changing the printing speed, nozzle hot end, Z distance and layer thickness. It presents two critical case studies of 3D printing soft thermoplastic polyurethane material on a cotton T-shirt and on a tulle textile to reveal the 3D printing on textile fabrics manufacturing challenges.

A method for estimating the build‐time required by the laminated object manufacturing (LOM) process is presented in this paper. The proposed algorithm – taking into account the real process parameters and the information included in the part's STL‐file – performs a minimum manipulation of the file, and calculates total volume, total surface area and flat areas involved in fine cross‐hatching. A number of experiments performed verify the applicability of the algorithm in process build‐time estimation. The time prediction estimates are within 7.6 per cent of the real build‐times for the LOM process. It is believed that, through specific minor adjustments, the algorithm could well be employed in process build‐time estimation for similar rapid prototyping processes.

To investigate laminated object manufacturing (LOM) process quality, using a design of experiments approach.

The quality characteristics measured were in‐plane dimensional accuracy, actual layer thickness (ALT), and mean time per layer. The process parameters tested were nominal layer thickness (LT), heater temperature (HT), platform retract (PR), heater speed (HS), laser speed (LS), feeder speed (FS) and platform speed (PS). A typical test part has been used, and matrix experiments were carried out based on Taguchi design. Optimal process parameter values were identified and finally, additive and regression models were applied to the experimental results and tested using evaluation experiments.

The statistical analysis of the experimental results shows that error in X direction was higher than error in Y direction. Dimensional accuracy in X direction depends mainly on the HS (89 percent) and HT (5 percent), and in Y direction on HS (50 percent), LT (31 percent), LS (9 percent), PS (6 percent), and HT (3 percent). On the other hand, ALT depends mainly on the nominal ALT (96 percent), HS (2 percent), HT (1 percent), and PR (1 percent). Finally, mean time per layer depends mainly on HS (59 percent), LS (17 percent), FS (17 percent), and PS (4 percent).

Future work should involve extensive matrix experiments using parameters such as dimensions of test part (X_max, Y_max, Z_max), hatch spacing in X and Y directions, and delay time between sequential layers.

Using the extracted models, the quality of LOM parts can be predicted and appropriate process parameter values selected. This means minimization of post processing time, easier disengagement between supporting frame and part, easier decubing, process optimization, less finishing and satisfactory final LOM parts or tools. Also, ALT prediction and mean time per layer analysis could be used to improve LOM build time predictions.

The above analysis is useful for LOM users when predictions of part quality, paper consumption, and build time are needed. This methodology could be easily applied to different materials and initial conditions for optimisation of other LOM‐type processes.

To investigate the influence of different process parameters of the laminated object manufacturing (LOM) process on the roughness of vertical surfaces along Z‐axis on ZX‐plane of parts produced by LOM.

The process parameters tested were layer thickness, heater temperature, platform retract, heater speed, laser speed, feeder speed and platform speed. A typical test part has been used, and matrix experiments were carried out based on Taguchi design. Optimal process parameter values were identified and finally, a regression model was applied onto the experimental results, and compared with bibliography models, using arbitrary experiments.

The statistical analysis of the experimental results showed that the surface roughness depends mainly on the heater temperature, layer thickness, and laser speed. Moreover, the regression model gave good predictions when heater temperature values were within the initial experimental area and inaccurate predictions when heater temperature takes the value 200°C.

Future work should involve extensive matrix experiments using parameters such as dimensions of test part (X_max, Y_max, Z_max), hatch spacing in X and Y directions, and delay time between sequential layers.

Using the extracted regression model, vertical surface roughness can be predicted and selected proprietary process parameter values. This means minimization of post processing time, easier disengagement between supporting frame and part, easier decubing, process optimization, and less finishing.

This methodology could be easily applied on different materials and initial conditions for optimisation of LOM processes.

Fuzzy numbers are often used to represent non-probabilistic uncertainty in engineering, decision-making and control system applications. In these applications, fuzzy arithmetic operations are frequently used for solving mathematical equations that contain fuzzy numbers. There are two approaches proposed in the literature for implementing fuzzy arithmetic operations: the α-cut approach and the extension principle approach using different t-norms. Computational methods for the implementation of fuzzy arithmetic operations in different applications are also proposed in the literature; these methods are usually developed for specific types of fuzzy numbers. This chapter discusses existing methods for implementing fuzzy arithmetic on triangular fuzzy numbers using both the α-cut approach and the extension principle approach using the min and drastic product t-norms. This chapter also presents novel computational methods for the implementation of fuzzy arithmetic on triangular fuzzy numbers using algebraic product and bounded difference t-norms. The applicability of the α-cut approach is limited because it tends to overestimate uncertainty, and the extension principle approach using the drastic product t-norm produces fuzzy numbers that are highly sensitive to changes in the input fuzzy numbers. The novel computational methods proposed in this chapter for implementing fuzzy arithmetic using algebraic product and bounded difference t-norms contribute to a more effective use of fuzzy arithmetic in construction applications. This chapter also presents an example of the application of fuzzy arithmetic operations to a construction problem. In addition, it discusses the effects of using different approaches for implementing fuzzy arithmetic operations in solving practical construction problems.

This study presents a framework to estimate throughput and cost of additive manufacturing (AM) as related to process parameters, material thermodynamic properties and machine specifications. Taking a 3D model of the part design as input, the model uses a parametrization of the rate-limiting physics of the AM build process – herein focusing on laser powder bed fusion (LPBF) and scaling of LPBF melt pool geometry – to estimate part- and material-specific build time. From this estimate, per-part cost is calculated using a quantity-dependent activity-based production model.

Analysis tools that assess how design variables and process parameters influence production cost increase our understanding of the economics of AM, thereby supporting its practical adoption. To this aim, our framework produces a representative scaling among process parameters, build rate and production cost.

For exemplary alloys and LPBF system specifications, predictions reveal the underlying tradeoff between production cost and machine capability, and look beyond the capability of currently commercially available equipment. As a proxy for build quality, the number of times each point in the build is re-melted is derived analytically as a function of process parameters, showcasing the tradeoff between print quality due to increased melting cycles, and throughput.

Typical cost models for AM only assess single operating points and are not coupled to models of the representative rate-limiting process physics. The present analysis of LPBF elucidates this important coupling, revealing tradeoffs between equipment capability and production cost, and looking beyond the limits of current commercially available equipment.

The purpose of this study is to demonstrate the variation between the set torque and the actual torque at which the actuator trips can be minimized using Taguchi's robust engineering methodology. The paper also aims to demonstrate the application of feature selection approach for the identification of insignificant effects in unreplicated fractional factorial experiments.

The methodology used was design of experiments with the set torque as the signal factor and the tripping torque as response variable. The compounded noise factor was identified based on the type of operations and load variation, which are not under the manufacturer's control. The effect of five control factors (with two levels each) and two interactions were studied. The experiments were designed using L8 orthogonal array.

The result showed that the factors spring height, spring thickness, star washer position and the interaction between drive shaft length and spring height play a significant role in actuator performance. The implementation of the optimum combination of factors resulted in improving the overall capability indices, Cp from 0.52 to 2.12 and Cpk from 0.4 to 1.67.

This study provides valuable information to actuator manufacturers on optimizing actuator performance.

To the best of the author's knowledge, no study has been conducted using Taguchi's robust engineering methodology to optimize actuator performance. In addition, no attempt has been made in the past to identify the insignificant factors and interactions using feature selection approach for unreplicated fractional factorial experiments.

Yoghurt is most popular and more acceptable throughout the world because of its general positive image among consumers because of its diverse nutritional and therapeutic properties and can be the most suitable probiotic carrier. Key factors for consumer’s inclination towards functional foods are increased awareness for healthy foods because of health deterioration resulting from busy lifestyles, growing healthcare cost and the aspiration for an improved quality life in later years. Yoghurt is still not consumed in certain parts of the world because of a lack of a cultural tradition of consuming yogurt and further people are not aware of the health benefits associated with yogurt consumption. In this study an attempt has been to project probiotic yoghurt as a functional food in the current era of self-care and complementary medicine.

Attempt has been made to review the literature on the biochemical activities of yoghurt cultures and their behavior in association with diverse probiotic cultures. Both review and research papers related to biochemical activities and functional properties of yoghurt cultures in association with probiotics and their health benefits published in diverse journals under Pub Med and Science Direct have been considered. Keywords used for data search included functional foods, yoghurt, probiotic, health benefits, etc.

Functional properties of yoghurt can be further enhanced with fortification of minerals and vitamins or inclusion of probiotic cultures. Diversity in biochemical behavior yoghurt cultures in association with different probiotic cultures has been reported. Conjugated application of probiotics with yoghurt cultures would result in a product with enhanced functional properties to extend health benefits.

Inclusion of probiotic cultures in yoghurt is suggested to extend the functional properties of normal yoghurt, thus providing necessary nutrients, improving health and preventing or reducing nutrition-related diseases. Regular intake of probiotic yoghurt is suggested for healthy lifestyles, as it will help in retaining their health and reduce the potentially long-term risk of disease. Food industries can have profit-driven business by projecting the probiotic yoghurt as a functional food.

The rapid growth of 3D printing has transformed the cost-effective production of prototypes and functional items, primarily using extrusion technology with thermoplastics. This study aims to focus on optimizing mechanical properties, precisely highlighting the crucial role of mechanical compressive strength in ensuring the functionality and durability of 3D-printed components, especially in industrial and engineering applications.

Using the Box−Behnken experimental design, the research investigated the influence of layer thickness, wall perimeter and infill level on mechanical resistance through compression. Parameters such as maximum force, printing time and mass utilization are considered for assessing and enhancing mechanical properties.

The layer thickness was identified as the most influential parameter over the compression time, followed by the degree of infill. The number of surface layers significantly influences both maximum strength and total mass. Optimization strategies suggest reducing infill percentage while maintaining moderate to high values for surface layers and layer thickness, enabling the production of lightweight components with adequate mechanical strength and reduced printing time. Experimental validation confirms the effectiveness of these strategies, with generated regression equations serving as a valuable predictive tool for similar parameters.

This research offers valuable insights for industries using 3D printing in creating prototypes and functional parts. By identifying optimal parameters such as layer thickness, surface layers and infill levels, the study helps manufacturers achieve stronger, lighter and more cost-efficient components. For industrial and engineering applications, adopting the outlined optimization strategies can result in components with enhanced mechanical strength and durability, while also reducing material costs and printing times. Practitioners can use the developed regression equations as predictive tools to fine-tune their production processes and achieve desired mechanical properties more effectively.

This research contributes to the ongoing evolution of additive manufacturing, providing insights into optimizing structural rigidity through polylactic acid (PLA) selection, Box−Behnken design and overall process optimization. These findings advance the understanding of fused deposition modeling (FDM) technology and offer practical implications for more efficient and economical 3D printing processes in industrial and engineering applications.

The purpose of this study is to measure the R&D performance of 81 cities in Turkey by using a scientific approach.

A four-step solution approach is developed for this problem. In the first step, a hierarchical structure of 14 indicators (including number of patents, publications, R&D expense, etc.) in three dimensions is constructed. In the second step, explicitly and implicitly spatial indicators such as university location and R&D manpower are mapped by using geographic information system (GIS). In the third step, a hybrid multi-criteria decision making model, namely, DANP that combines decision-making trial and evaluation laboratory (DEMATEL) and analytic hierarchy process (ANP) techniques is applied to assign different level of importance to the indicators. In the last step, Višekriterijumska Optimizacijai kompromisno Rešenje (VIKOR) method is used to rank the performance of 81 cities. Obtained results are visualized using GIS to show the pros and cons of each city in terms of R&D performance.

Results of the paper show that Istanbul, Ankara and Konya are ordered as contenders of best R&D performances and on the contrary, Igdir, Sirnak and Tunceli are ordered as the worst R&D performances among 81 cities.

One limitation of the study can be the considered criteria. However, all the criteria are obtained from literature and experts; thus, the paper covers as much criteria as possible.

The proposed study may allow Ministry of Science, Industry and Technology of Turkey to formulate more effective strategies to improve cities’ R&D performance. In addition, any country can apply the same methodology for measuring the R&D performance of their cities by using their related data. As the worst R&D city performances belong to the eastern part of Turkey, it can be deducted that the socio-cultural structure of the eastern part of the country needs improvement.

To the best of the author’s knowledge, this is the first study which applies a GIS-based MCDM approach for R&D performance measurement. Thus, the value of this paper belongs to both literature and real life.