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Hybrid manufacturing technologies combining individual processes can be recognized as one of the most cogent developments in recent times. As a result of integrating additive, subtractive and inspection processes within a single system, the relative benefits of each process can be exploited. This collaboration uses the strength of the individual processes, while decreasing the shortcomings and broadening the application areas. Notwithstanding its numerous advantages, the implementation of hybrid technology is typically affected by the limited process planning methods. The process planning methods proficient at effectively using manufacturing sources for hybridization are notably restrictive. Hence, this paper aims to propose a computer-aided process planning system for hybrid additive, subtractive and inspection processes. A dynamic process plan has been developed, wherein an online process control with intelligent and autonomous characteristics, as well as the feedback from the inspection, is utilized.

In this research, a computer-aided process planning system for hybrid additive, subtractive and inspection process has been proposed. A framework based on the integration of three phases has been designed and implemented. The first phase has been developed for the generation of alternative plans or different scenarios depending on machining parameters, the amount of material to be added and removed in additive and subtractive manufacturing, etc. The primary objective in this phase has been to conduct set-up planning, process selection, process sequencing, selection of machine parameters, etc. The second phase is aimed at the identification of the optimum scenario or plan.

To accomplish this goal, economic models for additive and subtractive manufacturing were used. The objective of the third phase was to generate a dynamic process plan depending on the inspection feedback. For this purpose, a multi-agent system has been used. The multi-agent system has been used to achieve intelligence and autonomy of different phases.

A case study has been developed to test and validate the proposed algorithm and establish the performance of the proposed system.

The major contribution of this work is the novel dynamic computer-aided process planning system for the hybrid process. This hybrid process is not limited by the shortcomings of the constituent processes in terms of tool accessibility and support volume. It has been established that the hybrid process together with an appropriate computer-aided process plan provides an effective solution to accurately fabricate a variety of complex parts.

This study aims to develop and demonstrate a deposition framework for the implementation of a region-based adaptive slicing strategy for the Tungsten Inert Gas (TIG) welding-based additive manufacturing system. The present study demonstrates a deposition framework for implementing a novel region-based adaptive slicing strategy termed as Fast Interior and Accurate Exterior with Constant Layer Height (FIAECLH).

The mentioned framework has been developed by performing experiments using the design of experiments and analyzing the experimental data. Analysis results have been used to obtain the mathematical function to integrate customization in the process. The paper, in the end, demonstrates the FIAECLH framework for implementing region-based adaptive slicing strategy on the hardware level.

The study showcase a new way of implementing the region-based adaptive slicing strategy to arc-based metal additive manufacturing. The study articulating a new strategy for its implementation in all types of wire and arc additive manufacturing processes.

Wire-arc-based technology has the potential to deliver cost-effective solutions for metal additive manufacturing. The research on arc welding-based processes is being carried out in different dimensions. To deposit parts with complex geometry and better dimensional accuracy implementation of a novel region-based adaptive slicing strategy for the arc-based additive manufacturing process is an essential task. The successful implementation of an adaptive slicing strategy would ease the fabrication of complex geometry in less time. This paper accomplishes this need of implementing a region-based adaptive slicing strategy as no experimental investigation has been reported for the TIG-based additive manufacturing process.

The purpose of this paper is to examine the suitability of additive manufacturing technologies in the reconstruction of archaeological discoveries as illustrative models. The processes of reverse engineering and part fabrication are discussed in detail, with particular emphasis placed on the difficulties of managing scaling and material characteristics for the manufacturing process.

Through a case‐based approach, this paper examines the reconstruction of a fifteenth‐century ship recovered from the River Usk in South Wales, UK. Using interviews and process data, the paper identifies challenges for both archaeologists and manufacturers in the application of additive manufacturing technologies for archaeological reconstruction applications.

This paper illustrates both the suitability of additive manufacturing in archaeological restoration, but also the challenges which result from this approach. It demonstrates the practical considerations of scaling process and materials, whilst also highlighting the techniques to improve accuracy and mechanical properties of the model.

Whilst the technologies of additive manufacturing have previously been applied to model making, little scholarly research has considered the practical techniques of design elicitation and manufacturing for archaeological applications. Using an in‐depth case study, this paper highlights the principal considerations for these applications, and provides guidance in the mitigation of manufacturing issues.

This paper aims to explore the influences of different process parameters, including laser power, scanning speed, defocusing distance and scanning mode, on the shape features of molten pool and, based on the obtained relationship, realize the diagnosis of forming defects during the process.

Molten pool was captured on-line based on a coaxial CCD camera mounted on the welding head, then image processing algorithms were developed to obtain melt pool features that could reflect the forming status, and it suggested that the molten pool area was the most sensitive characteristic. The influence of the processing parameters such as laser power, traverse speed, powder feed rate, defocusing distance and the melt pool area was studied, and then the melt pool area was used as the characteristic to detect the forming defects during the cladding and additive manufacturing process.

The influences of different process parameters on molten pool area were explored. Based on the relationship, different types of defects were accurately detected through analyzing the relationship between the molten pool area and time.

The findings would be helpful for the quality control of laser additive manufacturing.

This paper aims to investigate the extent to which traditional manufacturers are equipped and interested in participating in a hybrid manufacturing system which integrates traditional processes such as machining and grinding with additive manufacturing (AM) processes.

A survey was conducted among traditional metal manufacturers to collect data and evaluate the ability of these manufacturers to provide hybrid – AM post-processing services in addition to their standard product offering (e.g. mass production).

The original equipment manufacturers (OEMs) surveyed have machine availability and an interest in adopting hybrid manufacturing to additionally offer post-processing services. Low volume parts which would be suitable for hybrid manufacturing are generally more profitable. Access to metal AM, process engineering time, tooling requirements and the need for quality control tools were equally identified as the major challenges for OEM participation in this evolving supply chain.

OEMs can use this research to determine if hybrid manufacturing is a possible fit for their industry using existing machine tools.

Survey data offer an unique insight into the readiness of metal manufacturers who play an integral role in the evolving hybrid supply chain ecosystem required for post-processing of AM metal parts. This study also suggests that establishing metal AM centers around OEMs as a shared resource to produce near-net AM parts would be beneficial.

Because of the anisotropy of the process and the variability in the quality of printed parts, finite element analysis is not directly applicable to recycled materials manufactured using fused filament fabrication. The purpose of this study is to investigate the numerical-experimental mechanical behavior modeling of the recycled polymer, that is, recyclable polyethylene terephthalate (rPET), manufactured by a deposition FFF process under compressive stresses for new sustainable designs.

In all, 42 test specimens were manufactured and analyzed according to the ASTM D695-15 standards. Eight numerical analyzes were performed on a real design manufactured with rPET using Young's compression modulus from the experimental tests. Finally, eight additional experimental tests under uniaxial compression loads were performed on the real sustainable design for validating its mechanical behavior versus computational numerical tests.

As a result of the experimental tests, rPET behaves linearly until it reaches the elastic limit, along each manufacturing axis. The results of this study confirmed the design's structural safety by the load scenario and operating boundary conditions. Experimental and numerical results show a difference of 0.001–0.024 mm, allowing for the rPET to be configured as isotropic in numerical simulation software without having to modify its material modeling equations.

The results obtained are of great help to industry, designers and researchers because they validate the use of recycled rPET for the ecological production of real-sustainable products using MEX technology under compressive stress and its configuration for numerical simulations. Major design companies are now using recycled plastic materials in their high-end designs.

Validation results have been presented on test specimens and real items, comparing experimental material configuration values with numerical results. Specifically, to the best of the authors’ knowledge, no industrial or scientific work has been conducted with rPET subjected to uniaxial compression loads for characterizing experimentally and numerically the material using these results for validating a real case of a sustainable industrial product.

The purpose of this paper is to predict and control the composition during laser additive manufacturing, since composition control is important for parts manufactured by laser additive manufacturing. Aluminum and steel functionally graded material (FGM) were manufactured by laser metal deposition, and the composition was analyzed by means of spectral analysis simultaneously.

The laser metal deposition process was carried out on a 5 mm thick 316L plate. Spectral line intensity ratio and plasma temperature were chosen as two main spectroscopic diagnosis parameters to predict the compositional variation. Single-trace single-layer experiments and single-trace multi-layer experiments were done, respectively, to test the feasibility of the spectral diagnosis method.

Experiment results showed that with the composition of metal powder changing from steel to aluminum, the spectral intensity ratio of the characteristic spectral line is proportional to the elemental content in the plasma. When the composition of deposition layers changed, the characteristic spectrum line intensity ratio changed obviously. And the linear chemical composition analysis results confirmed the gradient composition variation of the additive manufacturing parts. The results verified the feasibility of composition analysis based on spectral information in the laser additive manufacturing process.

The composition content of aluminum and steel FGM was diagnosed by spectral information during laser metal deposition, and the relationship between spectral intensity and composition was established.

The purpose of this paper is to improve the productivity and quality of the wire arc additive manufacturing process by benchmarking the strategies from the selected six strategies, namely, heat treatment process, inter pass cooling process, inter pass cold rolling process, peening process, friction stir processing and oscillation process.

To overcome the lack of certainty associated with correlations and relationships in quality functional deployment, fuzzy numbers have been integrated with the quality functional deployment framework. Twenty performance measures have been identified from the literature under five groups, namely, mechanical properties, physical properties, geometrical properties, cost and material properties. Using house of quality weights are allocated to performance measures and groups, relationships are established between performance measures and strategies, and correlations are assigned between strategies. Finally, for each strategy, relative importance, score and crisp values are calculated.

Inter pass cold rolling process strategy is computed with the highest crisp value of 15.80 which is followed by peening process, heat treatment process, friction stir processing, inter pass cooling process,] and oscillation process strategy.

To the best of the authors’ knowledge, there has been no research in the literature that analyzes the strategies to improve the quality and productivity of the wire arc additive manufacturing process.

This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process.

Four components, namely, big data application, big data sensing and acquisition, big data processing and storage, model establishing, data mining and process optimization were presented to comprise the framework. Key technologies including the big data acquisition and integration, big data mining and knowledge sharing mechanism were developed for the big data analytics for AM.

The presented framework was demonstrated by an application scenario from a company of three-dimensional printing solutions. The results show that the proposed framework benefited customers, manufacturers, environment and even all aspects of manufacturing phase.

This study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering.

The proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process.

This paper introduces the concept of big data in the field of AM. The proposed framework can be used to make better decisions based on the big data during manufacturing process.

Among the different methodologies used for performance control in precision manufacturing, the measurement of metrological test artefacts becomes very important for the characterization, optimization and performance evaluation of additive manufacturing (AM) systems. The purpose of this study is to design and manufacture several benchmark artefacts to evaluate the accuracy of the selective laser melting (SLM) manufacturing process.

Artefacts consist of different primitive features (planes, cylinders and hemispheres) on sloped planes (0°, 15°, 30°, 45°) and stair-shaped and sloped planes (from 0° to 90°, at 5° intervals), manufactured in 17-4PH stainless steel. The artefacts were measured optically by a structured light scanner to verify the geometric dimensioning and tolerancing of SLM manufacturing.

The results provide design recommendations for precision SLM manufacturing of 17-4PH parts. Regarding geometrical accuracy, it is recommended to avoid surfaces with 45° negative slopes or higher. On the other hand, the material shrinkage effect can be compensated by resizing features according to X and Y direction.

No previous work has been found that evaluates accuracy when printing inwards (pockets) and outwards (pads) geometries at different manufacturing angles using SLM. The proposed artefacts can be used to determine the manufacturing accuracy of different AM systems by resizing to fit the build envelope of the system to evaluate. Analysis of manufactured benchmark artefacts allows to determine rules for the most suitable design of the desired parts.