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This study aims to extend the known design guidelines for the polymer-based fused filament fabrication (FFF) 3D printing process with the focus on function-integrated components, specifically optomechanical parts. The potential of this approach is demonstrated by manufacturing function-integrated optomechanics for a low-power solid-state laser system.

For the production of function-integrated additively manufactured optomechanics using the FFF process, essential components and subsystems have been identified for which no design guidelines are available. This includes guidelines for integrating elements, particularly optics, into a polymer structure as well as guidelines for printing functional threads and ball joints. Based on these results, combined with prior research, a function-integrated low-power solid-state laser optomechanic was fabricated via the FFF process, using a commercial 3D printer of the type Ultimaker 3. The laser system's performance was assessed and compared to a reference system that employed commercial optomechanics, additionally confirming the design guidelines derived from the study.

Based on the design goal of function integration, the existing design guidelines for the FFF process are systematically extended. This success is demonstrated by the fabrication of an integrated optomechanic for a solid-state laser system.

Based on these results, scientists and engineers will be able to use the FFF process more extensively and benefit from the possibilities of function-integrated manufacturing.

Extensive research has been published on additive manufacturing of optomechanics. However, this research often emphasizes only cost reduction and short-term availability of components by reprinting existing parts. This paper aims to explore the capabilities of additive manufacturing in the production of function-integrated components to reduce the number of individual parts required, thereby decreasing the workload for system assembly and leading to an innovative production process for optical systems. Consequently, where needed, it provides new design guidelines or extends existing ones and verifies them by means of test series.

High-performance wrought aluminum alloys, particularly AA6061, are pivotal in industries like automotive and aerospace due to their exceptional strength and good response to heat treatments. Investment casting offers precision manufacturing for these alloys, because casting AA6061 poses challenges like hot cracking and severe shrinkage during solidification. This study aims to address these issues, enabling crack-free investment casting of AA6061, thereby unlocking the full potential of investment casting for high-performance aluminum alloy components.

Nanotechnology is used to enhance the investment casting process, incorporating a small volume fraction of nanoparticles into the alloy melt. The focus is on widely used aluminum alloy 6061, utilizing rapid investment casting (RIC) for both pure AA6061 and nanotechnology-enhanced AA6061. Microstructural characterization involved X-ray diffraction, optical microscopy, scanning electron microscopy, differential scanning calorimetry and energy dispersive X-ray spectroscopy. Mechanical properties were evaluated through microhardness and tensile testing.

The study reveals the success of nanotechnology-enabled investment casting in traditionally challenging wrought aluminum alloys like AA6061. Achieving crack-free casting, enhanced grain morphology and superior mechanical properties, because the nanoparticles control grain sizes and phase growth, overcoming traditional challenges associated with low cooling rates. This breakthrough underscores nanotechnology's transformative impact on the mechanical integrity and casting quality of high-performance aluminum alloys.

This research contributes originality and value by successfully addressing the struggles in investment casting AA6061. The novel nano-treating approach overcomes solidification defects, showcasing the potential of integrating nanotechnology into rapid investment casting. By mitigating challenges in casting high-performance aluminum alloys, this study paves the way for advancements in manufacturing crack-free, high-quality aluminum alloy components, emphasizing nanotechnology's transformative role in precision casting.

This study aims to deepen the knowledge concerning the metal fused filament fabrication technology through an analysis of the printing parameters of a commercial 316L stainless steel filament and their influence on the porosity and mechanical properties of the printed parts. It also investigates the feasibility of manufacturing complex geometries, including strut-and-node and triply periodic minimal surface lattices.

A three-step experimental campaign was carried out. Firstly, the printing parameters were evaluated by analysing the green parts: porosity and density measurements were used to define the best printing profile. Then, the microstructure and porosity of the sintered parts were investigated using light optical and scanning electron microscopy, while their mechanical properties were obtained through tensile tests. Finally, manufacturability limits were explored with reference samples and cellular structures having different topologies.

The choice of printing parameters drastically influences the porosity of green parts. A printing profile which enables reaching a relative density above 99% has been identified. However, voids characterise the sintered components in parallel planes at the interfaces between layers, which inevitably affect their mechanical properties. Lattice structures and complex geometries can be effectively printed, debinded, and sintered if properly dimensioned to fulfil printing constraints.

This study provides an extensive analysis of the printing parameters for the 316L filament used and an in-depth investigation of the potential of the metal fused filament fabrication technology in printing lightweight structures.

The study aims to develop an inexpensive metal oxide semiconductor gas sensor with high sensitivity, excellent selectivity for a specific gas and rapid response time.

This study synthesized Zn2SnO4 nanostructures using a hydrothermal method with a 1 M concentration of zinc chloride (ZnCl2) as the zinc source and a 0.7 M concentration of tin chloride (SnCl4) as the tin source. Thick films of nanostructured Zn2SnO4 were then produced using screen printing. The structural properties of Zn2SnO4 were confirmed using X-ray diffraction, and the formation of Zn2SnO4 nanoparticles was verified by transmission electron microscopy. Scanning electron microscopy was used to analyse the surface morphology of the fabricated material, while energy dispersive spectroscopy provided insight into the chemical composition of the thick film. These fabricated thick films underwent testing for various hazardous gases, including nitrogen dioxide, ammonia, hydrogen sulphide (H2S), ethanol and methanol.

The nanostructured Zn2SnO4 thick film sensor demonstrates a notable sensitivity to H2S gas at a concentration of 500 ppm when operated at 160°C. Its selectivity, response time and recovery time were assessed and documented.

The primary limitations of this research on metal oxide semiconductor gas sensors include poor selectivity to specific gases, limited durability and challenges in achieving detection at room temperature.

The nanostructured Zn2SnO4 thick film sensor demonstrates a strong response to H2S gas, making it a promising candidate for commercial production. The detection of H2S is crucial in various sectors, including industries and sewage plants, where monitoring this gas is essential.

Currently, heightened global apprehension about atmospheric pollution stems from the existence of perilous toxic and flammable gases. This underscores the imperative need for monitoring such gases. Toxic and flammable gases are frequently encountered in both residential and industrial environments, posing substantial hazards to human health. Noteworthy accidents involving flammable gases have occurred in recent years. It is crucial to comprehend the presence and composition of these gases in the surroundings for precise detection, measurement and control. Thus, there has been a significant push for extensive research and development in diverse sensor technologies using various materials and methodologies to monitor and regulate these gases effectively.

In this research, Zn2SnO4 nanostructures were synthesized using a hydrothermal method with ZnCl2 at a concentration of 1 M for zinc and SnCl4 at a concentration of 0.7 M for tin. Thick films of nanostructured Zn2SnO4 were then fabricated via screen printing technique. Following fabrication, all thick films were subjected to testing with various toxic gases, and the results were compared to previously published data. The analysis indicated that the nanostructured Zn2SnO4 thick film sensor demonstrated outstanding performance concerning gas response, gas concentration, selectivity and response time, particularly towards H2S gas.

The spatial concentration of the manufacturing employment and production is the general feature in all the economy. Indian economy is not an exception from this scenario. The spatial pattern of Indian manufacturing sector reports a significant regional disparity. Since manufacturing is becoming more vital in the Indian economy, it is essential to understand the spatial distribution of the manufacturing sector. This chapter provides a detailed picture of India’s spatial distribution pattern in formal and informal manufacturing sectors. Using the well-known Ellison–Glaeser index, we measure the industrial agglomeration for all National Industry Classification (NIC) three-digit industries and further incorporate statewise manufacturing activity distribution. From the analysis, it is evident that there is a substantial regional concentration of manufacturing activity; however, the recent regional distribution pattern also depicts some signs of industrial dispersion.

The investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.

The study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.

The work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.

The novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

The aim was not to perform a systematic review but firstly to search in PubMed, Science Direct, Scopus and Web of Science databases on the papers published in the last five years using tools for reviewing the statement of preferred information item for systematic reviews without focusing on a randomized analysis and secondly to perform a bibliometric analysis on the properties of films and coatings added of tocopherol for food packaging.

On January 24, 2022, information was sought on the properties of films and coatings added of tocopherol for use as food packaging published in PubMed, Science Direct, Scopus and Web of Science databases. Further analysis was performed using bibliometric indicators with the VOSviewer tool.

The searches returned 33 studies concerning the properties of films and coatings added of tocopherol for food packaging, which were analyzed together for a better understanding of the results. Data analysis using the VOSviewer tool allowed a better visualization and exploration of these words and the development of maps that showed the main links between the publications.

In the area of food science and technology, the development of polymers capable of promoting the extension of the shelf life of food products is sought, so the knowledge of the properties is vital for this research area since combining a biodegradable polymeric material with a natural antioxidant active is of great interest for modern society since they associate environmental preservation with food preservation.

Conventional powder bed fusion systems, with their high costs, proprietary nature and restrictive fees, limit research opportunities. This study aims to unveil an affordable, open-source hardware, open-source software laser-based metal powder bed fusion system. Recognizing the distinction between DIY and open-source hardware is crucial for widespread acceptance.

The authors present a comprehensive system architecture using object process methodology for functions and architecture, a design structure matrix to model system dependencies and classical technical drawing exploded views for select subsystems. Modularization enables high adaptability, fostering potential adoption.

The fully open system enables unrestricted research, mirroring common industrial metal laser-based powder bed fusion (L-PBF) systems. While “open” systems are available for purchase, they remain closed-source, lacking source code and technical drawings sharing, hindering contribution and co-development. The authors’ is the pioneering and sole open-source metal L-PBF system, boasting 1,500+ print hours. A series of industrial and academic adopters are currently implementing the system.

The open system, slicer software and controller offer unique process control, supporting multimaterial printing. The authors shared the design on the OpenAM GitHub page under the CERN-OHL-P v2 Open Source Hardware license. While it is functional for additive manufacturing (e.g. aluminum, tool steel, titanium and stainless steel), the entire process chain is actively evolving, ideal for co-development with the additive manufacturing community.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.