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The purpose of this paper is to evaluate the tool experiences using torque during welding as a means of in-process sensing for tool wear. Metal matrix composites (MMCs) are materials with immense potential for aerospace structural applications. The major barrier to implementation of these materials is manufacturability, specifically joining MMCs to themselves or other materials using fusion welding. Friction stir welding (FSW) is an excellent candidate process for joining MMCs, as it occurs below the melting point of the material, thus precluding the formation of degradative intermetallics’ phases present in fusion welded joints. The limiting factor for use of FSW in this application is wear of the tool. The abrasive particles which give MMCs their enhanced properties progressively erode the tool features that facilitate vertical mixing and consolidation of material during welding, resulting in joints with porosity. While wear can be mitigated by careful selection of process parameters and/or the use of harder tool materials, these approaches have significant complexities and limitations.

This study evaluates using the torque the tool experiences during welding as a means of in-process sensing for tool wear. Process signals were collected during linear FSW of Al 359/SiC/20p and correlated with wear of the tool probe. The results of these experiments demonstrate that there is a correlation between torque and wear, and the torque process signal can potentially be exploited to monitor and control tool wear during welding.

Radial deterioration of the probe during joining of MMCs by FSW corresponds to a decrease in the torque experienced by the tool. Experimentally observed relationship between torque and wear opens the door to the development of in-process sensing, as the decay in the torque signal can be correlated to the amount of volume lost by the probe. The decay function for tool wear in FSW of a particular MMC can be determined experimentally using the methodology presented here. The decay of the torque signal as the tool loses volume presents a potential method for control of the wear process.

This work has near-term commercial applications, as a means of monitoring and controlling wear in process could serve to grow commercial use of MMCs and expand the design space for these materials beyond net or near-net-shape parts.

This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built.

Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects.

Through aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts.

Although the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers.

Effective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts.

While most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

The purpose of this paper is to surveys classic and recently developed strategies for quality monitoring and real-time control of laser-based, directed-energy deposition.Additive manufacturing of metal parts is a complex undertaking. During deposition, many of the process variables that contribute to overall build quality – such as travel speed, feedstock flow pattern, energy distribution, gas pressure, etc. – are subject to perturbations from systematic fluctuations and random external disturbances.

Sensing and control of laser-based, directed-energy metal deposition is presented as an evolution of methods developed for welding and cladding processes. Methods are categorized as sensing and control of machine variables and sensing and control of build attributes. Within both categories, classic methods are presented and followed by a survey of novel developments.

Additive manufacturing would not be possible without highly automated, computer-based controllers for processing and motion. Its widespread adoption for metal components in critical applications will not occur without additional developments and integration of machine- and process-based sensing systems to enable documentation, and control of build characteristics and quality. Ongoing work in sensing and control brings us closer to this goal.

This work serves to introduce researchers new to the field of additive manufacturing to common sources of process defects during metal powder-based, directed-energy deposition processing, and surveys sensing and control methods being investigated to improve the process. The work also serves to highlight, and stress the significance of novel developments in the field.

The wide application of metal material extrusion (MEX) has been hampered by the practicalities associated with the resulting shrinkage of the final parts when commercial three-dimensional (3D) printing equipment is used. The shrinkage behaviour of MEX metal parts is a very important aspect of the MEX metal production process, as the parts must be accurately oversized to compensate for shrinkage. This paper aims to investigate the influence of primary 3D printing parameters, namely, print speed, layer height and print angle, on the shrinkage behaviour of MEX Steel 316L parts.

Two groups of dog-bone and rectangular-shape specimens were produced with the BASF Ultrafuse Steel 316L metal filament. The length, width and thickness of the specimens were measured pre- and post-debinding and sintering to calculate the percentile shrinkage rates. Analysis of variance (ANOVA) was used to evaluate and rank the significance of each manufacturing parameter on shrinkage. Typical main print quality issues experienced in this analysis are also reported.

The shrinkage rates of the tested specimens ranged from 15.5 to 20.4% along the length and width axis and 18.5% to 23.1% along the thickness axis of the specimens. Layer height and raster angle were the most statistically significant parameters influencing shrinkage, while print speed had very little influence. Three types of defects were observed, including surface roughness, surface deformation (warping and distortion) and balling defects.

This paper bridges an existing gap in MEX Steel 316L literature, with a focus on the relationship between MEX manufacturing parameters and subsequent shrinkage behaviour. This study provides an in-depth analysis of the relationship between manufacturing parameters – layer height, raster angle and print speed and subsequent shrinkage behaviour, thereby providing further information on the relationship between the former and the latter.

Breast cancer has become the largest cancer in the world today. Health problems for women with breast cancer need to be addressed urgently. This study aims to select the best method for preparing temperature-sensitive sports underwear, and to verify the feasibility of using K-type thermocouple threads in underwear fabrics.

In the experiments, two samples were designed for temperature-sensitive performance tests and the effects produced by different outer layer structures were investigated. In the second step, K-type thermocouple wires were integrated into sports underwear. The comfort and feasibility of the temperature-sensitive underwear were investigated.

It was finally verified to obtain the best comfort and temperature-sensing performance of K-type thermocouple filaments integrated into sports underwear with plain stitching.

The underwear has a certain prospect for the application of smart apparel based on breast cancer health monitoring, which is of some significance for monitoring smart apparel.

Interkama, the international fair for industrial communication, automation and measurement, is undergoing major structural changes, but new developments in sensing and measurement were well represented at the October 1999 exhibition in Düsseldorf.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

This report describes the progress that has been made to date on burn detection by acoustic emission with two‐inch camshafts produced for V6 and V8 engines on a Norton ‘camomatic’ grinding machine used for finish grinding valve cams.

The purpose of this paper is to propose a service development process that is adapted to manufacturing companies and to discuss its implications for companies with a traditional focus on product development and product sales.

The paper looks at new service development (NSD) literature and argues for a rationale to study NSD processes in a manufacturing context. Next, a generic NSD framework for manufacturing companies is presented. Examples are given based on an explorative multiple case study (ten companies) with in‐depth interviews and focus groups. The analysis reveals organizational requirements and other critical factors related to each stage of the NSD process.

A four‐stage service offering development framework is presented. Critical aspects of NSD in a manufacturing context are highlighted. The importance of considering both NSD and new product development (NPD) together is also emphasized.

The limitations are based primarily on methodology; the case studies focused only on the service organizations of the manufacturing companies studied.

Managers need to be aware of the inter‐relationship that exists between NSD and NPD and on the specificities of service development in companies where an industrial logic dominates. A number of managerial implications are proposed and discussed.

The paper emphasizes the importance of latter stages in NSD, something that has not previously been extensively studied or addressed. In addition, to explicitly discuss NSD in a manufacturing context is novel.

The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).

IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.

The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.

DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.

The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.