Additive manufacturing is a recent trend in production processes owing to its many benefits. It can be defined as the process of producing parts through the deposition of material in a layer-by-layer fashion. It has been a topic of intense study and review by many researchers. In this work, a comprehensive review pertaining to additive manufacturing has been accomplished. The evolution of additive manufacturing as a prominent technology and its various phases are discussed. The importance of part orientation, build time estimation, and cost computation has also been reviewed. The remarkable aspect of this work is the identification of problems associated with different additive manufacturing methods. Because of the imperfections in additive manufacturing, its hybridization with other methods, such as subtractive manufacturing, has been emphasized. This review will help readers understand the different aspects of additive manufacturing and explore new avenues for future research.

Additive manufacturing (AM) is a technique that progressively deposits material in layer-by-layer manner (or in additive fashion) for producing a three-dimensional (3D) object, starting from the computer-aided design (CAD) model. This approach allows for the printing of complicated shaped objects and is quickly gaining traction in the aerospace, medical implant, jewelry, footwear, automotive, and fashion industries. AM, which was formerly used for single part customization, is currently being considered for mass customization of parts because of its positive impacts. However, part quality and build time are two main impediments to the deployment of AM for mass production. The optimal part orientation is fundamental for maximizing the part’s quality as well as being critical for reducing the fabrication time. This research provides a new method for multi-part AM production that improves quality while reducing overall build time. The automatic setup planning or orientation approach described in this paper employs two objective functions: the quality of the build component and the build time. To tackle the given problem, it introduces a three-step genetic algorithm (GA)-based solution. A feature-based technique is utilized to generate a collection of finite alternative orientations for each component within a specific part group to ensure each part’s individual build quality. Then, a GA was utilized to find the best combination of part build orientations at a global optimal level to reduce material consumption and build time. A case study of orienting nine components concurrently inside a given building chamber was provided for illustration. The findings suggest that the developed technique can increase quality, reduce support waste, and shorten overall production time. When components are positioned optimally rather than in random orientations, build time and support volume are reduced by approximately 7% and 16%, respectively.

Support structures are required for overhanging surfaces in additive manufacturing to prevent part deformation. However, these support structures are undesirable because they are removed during postprocessing after the part is built. It is critical to remove metal support structures because they can occasionally leave residue on the part. Among the various support-removal techniques, manual removal and wire electrical discharge machining (EDM) are the most frequently used. While most manufacturers recommend the manual method due to its economic benefits, it may be challenging for thin walls, small-scale components, and brittle materials. The purpose of this study is to evaluate and compare these two methods, taking into account both their advantages and disadvantages. The results indicate that while manual support-removal is quick, it results in a degraded surface topography and increased surface roughness due to support tooth remnants and residue. The wire EDM method of removing support resulted in a more uniform surface topography and a superior surface finish. However, as a result of thermal damage, a heat-affected zone developed on the machined surface.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Practical implications: A case study has been developed to test and validate the proposed algorithm and establish the performance of the proposed system. Originality/value: 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.

Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity. A reasonable inspection plan, which can reduce inspection costs and achieve high customer satisfaction, is therefore very important in the production industry. Considerations such as preparations for part inspection, measuring machines, and their setups as well as the measurement path are described in an inspection plan which is subsequently translated into part inspection machine language. Therefore, the measurement of any component using a coordinate measuring machine (CMM) is the final step preceded by several other procedures, such as the preparation of the part setup and the generation of the probe path. Effective measurement of components using CMM can only be done if the preceding steps are properly optimized to automate the whole inspection process. This paper has proposed a method based on artificial intelligence techniques, namely, artificial neural network (ANN) and genetic algorithm (GA), for fine-tuning output from the different steps to achieve an efficient inspection plan. A case study to check and validate the suggested approach for producing effective inspection plans for CMMs is presented. A decrease of nearly 50% was observed in the travel path of the probe, whereas the CMM measurement time was reduced by almost 25% during the actual component measurement. The proposed method yielded the optimum part setup and the most appropriate measuring sequence for the part considered.