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ACCOUNTS of some comments made at the Annual National Committee meeting of the Amalgamated Engineering Union make discouraging reading. This is the only suitable adjective when speakers at that meeting state that time standards determined by time study are set on the performances of the best workers in the shop. We should have thought that with all the training that representatives have received in time study and allied subjects both from managements and unions that they would have known better than this.

Quantitative understanding of the temperatures, gradients and heating/cooling rates in and around the melt pool in laser powder bed fusion (L-PBF) is essential for simulation, monitoring and controls development. The research presented here aims to detail experiment design and preliminary results of high speed, high magnification, in-situ thermographic monitoring setup on a commercial L-PBF system designed to capture temperatures and dynamic process phenomena.

A custom door with angled viewport was designed for a commercial L-PBF system which allows close access of an infrared camera. Preliminary finite element simulations provided size, speed and scale requirements to design camera and optics setup to capture melt pool region temperatures at high magnification and frame rate speed. A custom thermal calibration allowed maximum measurable temperature range of 500°C to 1,025°C. Raw thermographic image data were converted to temperature assuming an emissivity of 0.5. Quantitative temperature results are provided with qualitative observations with discussion regarding the inherent challenges to future thermographic measurements and process monitoring.

Isotherms around the melt pool change in size depending on the relative location of the laser spot with respect to the stripe edges. Locations near the edges of a stripe are cooled to lower temperatures than the center of a stripe. Temperature gradients are highly localized because of rough or powdery surface. At a specific location, temperatures rise from below the measurable temperature range to above (<550°C to >1100°C) within two frames (<1.11 m/s). Particle ejection is a notable phenomenon with measured ejection speeds >11.7 m/s.

Several works are detailed in the Introduction of this paper that detail high-speed visible imaging (not thermal imaging) of custom or commercial LBPF processes, and lower-speed thermographic measurements for defect detection. However, no work could be found that provides calibrated, high-speed temperature data from a melt-pool monitoring configuration on a commercial L-PBF system. In addition, the paper elucidates several sources of measurement uncertainty (e.g. calibration, emissivity and time and spatial resolution), describes inherent measurement challenges based on observations of the thermal images and discusses on the implications to model validation and process monitoring and control.

This paper aims to mainly report the impact of torch angle on the dynamic behavior of the weld pool which is recorded and monitored in real time with the aid of a high-speed camera system. The influence of depositing torch angle on the fluctuation behavior of weld pool and the quality of weld formation are compared and analyzed.

The FANUC controlled robotic manufacturing system comprised a Fronius cold metal transfer (CMT) Advanced 4000R power source, FANUC robot, water cooling system, wire feeding system and a gas shielding system. An infrared laser was used to illuminate the weld pool for high-speed imaging at 1,000 frames per second with CR600X2 high-speed camera. The high-speed camera was set up a 35 ° angle with the deposition direction to investigate the weld pool flow patterns derived from high-speed video and the effect of torch angles on the first layer of wire additive manufacture-CMT.

The experimental results demonstrated that different torch angles significantly influence on the deposited morphology, porosity formation rate and weld pool flow.

With regard to the first layer of wire arc additive manufacture of aluminum alloys, the change of torch angle is critical. It is clear that different torch angles significantly influence on the weld morphology, porosity formation and weld pool flow. Furthermore, under different torch angles, the deposited beads will produce different defects. To get well deposited beads, 0-10° torch could be made away from the vertical position of the deposition direction, in which the formation of deposited beads were well and less porosity and other defects.

Comments on recent developments in the machine vision industry and describes some of the more significant exhibits at the IPOT 2001 exhibition, including camera networks, addressable pixel cameras and low resolution thermal imagers.

This paper aims to reveal developments in sensors applied to packaging lines.

Machine vision systems including special‐purpose smart cameras and a high‐speed camera are examined. The technology of radio frequency identification (RFID) is explained, and some products relevant to packaging are highlighted. Advances in X‐ray, metal detection and gas‐leak detection equipment are discussed.

Manufacturers are making smart cameras and high‐speed cameras easier to use. There is a trend for manufacturers to provide portable as well as in‐line instrumentation, for example, in code readers and gas leak detectors. RFID is an emerging technique for improving traceability in the supply chain, and some labelling machines additionally program an embedded chip.

Tracks the latest developments in sensors for engineers in the food and pharmaceutical packaging industries.

In recent years, additive manufacturing (AM) technology has been acknowledged as an efficient method for producing geometrical complex objects with a wide range of applications. However, dimensional inaccuracies and presence of defects hinder the broad adaption of AM procedures. These factors arouse concerns regarding the quality of the products produced with AM and the utilization of quality control (QC) techniques constitutes a must to further support this emerging technology. This paper aims to assist researchers to obtain a clear sight of what are the trends and what has been inspected so far concerning non-destructive testing (NDT) QC methods in AM.

In this paper, a survey on research advances on non-destructive QC procedures used in AM technology has been conducted. The paper is organized as follows: Section 2 discusses the existing NDT methods applied for the examination of the feedstock material, i.e. incoming quality control (IQC). Section 3 outlines the inspection methods for in situ QC, while Section 4 presents the methods of NDT applied after the manufacturing process i.e. outgoing QC methods. In Section 5, statistical QC methods used in AM technologies are documented. Future trends and challenges are included in Section 6 and conclusions are drawn in Section 7.

The primary scope of the study is to present the available and reliable NDT methods applied in every AM technology and all stages of the process. Most of the developed techniques so far are concentrated mainly in the inspection of the manufactured part during and post the AM process, compared to prior to the procedure. Moreover, material extrusion, direct energy deposition and powder bed processes are the focal points of the research in NDT methods applied in AM.

This literature review paper is the first to collect the latest and the most compatible techniques to evaluate the quality of parts produced by the main AM processes prior, during and after the manufacturing procedure.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.

LAST month we commented on a recent report, “Measurement of Productivity—Work Study Application and Training”, issued by the Joint Committee of the Institute of Cost and Works Accountants and the Institute of Production Engineers. Since then we have had the opportunity of making a closer study of the Report and feel that some of the recommendations are of such importance that they are worthy of further comment.

Developments in communications are now of prime interest to all cyberneticians. In particular, the need to provide instant and direct communications between computers, work stations and other equipment regardless of location and type, for applications such as computer‐aided design, software engineering and office automation is immediate.

The purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.

This study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.

This study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.

Once the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.

To the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.