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Inconsistencies in build quality part-to-part and build-to-build continue to be a problem in additive manufacturing (AM). The flexibility of AM often enables low-volume and custom production, making conventional methods of machine qualification and health monitoring challenging to implement. Machine health has been difficult to separate from the effects of design and process decisions, and therefore inferring machine health through part quality has been similarly complicated.

This conceptual paper proposes a framework for monitoring machine health by monitoring two types of witness parts, in the form of witness builds and witness artifacts, to provide sources of data for potential indicators of machine health.

The proposed conceptual framework with witness builds and witness artifacts permits the implementation into AM techniques to monitor machine health according to part quality. Subsequently, probabilistic models can be used to optimize machine costs and repairs, as opposed to statistical approaches that are less ideal for AM. Bayesian networks, hidden Markov models and Markov decision processes may be well-suited to accomplishing this task.

Though variations of witness builds have been created for use in AM to measure build quality and machine capabilities, the literature contains no previously proposed framework that permits the evaluation of machine health and its influence on quality through a combination of witness builds and witness artifacts, both of which can be easily added into AM production.

The purpose of this paper is to review existing smart manufacturing (SM) maturity models' dimensions and maturity levels to assess their applicability and drawbacks. There are many maturity models available but many of them have not been validated or do not provide a useful guide or tool for applications. This gap creates the need for a review of the existing maturity model's applicability.

Nineteen peer-reviewed maturity models related to “Digital Transformation,” “Industry 4.0” or “Smart Manufacturing” were selected based on a systematic literature review and five consulting firm models were selected based on the author's industry knowledge. The chosen models were analyzed to determine 10 categories of dimensions. Then they are assessed on a 1–5 scale for how applicable they are in the 10 categories of dimensions.

The five “consulting firm” models have a first-mover advantage, are more widely used in industry and are more applicable, but some require payment, and they lack published details and validation. The 19 “peer reviewed” models are not as widely used, lack awareness in the industry and are not as easy to apply because of no web tool for self-assessment, but they are improving. The categories defined to characterize the models and facilitate comparisons for users include “Information Technology (IT) and Cyber-Physical System (CPS) and Data,” “Strategy and Organization,” “Supply Chain and Logistics,” “Products and Services,” “Culture and Employees,” “Technology and Capabilities,” “Customer and Market,” “Cybersecurity and Risk,” “Leadership and Management” and “Governance and Compliance.” The analyzed maturity models were particularly weak in the areas of cybersecurity, leadership and governance.

Researchers and practitioners can use this review with consideration of their specific needs to determine if a maturity model is applicable or if a new model needs to be developed. The review can also aid in the development of maturity models through the discussion of each of the dimension categories.

Compared to existing reviews of SM maturity models, this research determines comprehensive dimension categories and focuses on applicability and drawbacks.

The optimal control on reassembly (remanufacturing assembly) error is one of the key technologies to guarantee the assembly precision of remanufactured product. However, because of the uncertainty existing in remanufactured parts, it is difficult to control assembly error during reassembly process. Based on the state space model, this paper aims to propose the optimal control method on reassembly precision to solve this problem.

Initially, to ensure the assembly precision of a remanufactured car engine, this paper puts forward an optimal control method on assembly precision for a remanufactured car engine based on the state space model. This method takes assembly workstation operation and remanufactured part attribute as the input vector reassembly status as the state vector and assembly precision as the output vector. Then, the compensation function of reassembly workstation operation input vector is calculated to direct the optimization of the reassembly process. Finally, a case study of a certain remanufactured car engine crankshaft is constructed to verify the feasibility and effectiveness of the method proposed.

The optimal control method on reassembly precision is an effective technology in improving the quality of the remanufactured crankshaft. The average qualified rate of the remanufactured crankshaft increased from 83.05 to 90.97 per cent as shown in the case study.

The optimal control method on the reassembly precision based on the state space model is available to control the assembly precision, thus enhancing the core competitiveness of the remanufacturing enterprises.

As the last link of product remanufacturing, reassembly process is of great importance in increasing the utilization of remanufactured parts as well as decreasing the production cost for remanufacturing enterprises. It is a common problem that a large amount of remanufactured part/reused part which past the dimension standard have been scrapped, which have increased the production cost of remanufacturing enterprises to a large extent. With the aim to improve the utilization of remanufacturing parts with qualified quality attributes but exceed dimension, the purpose of this paper is to put forward a reassembly classification selection method based on the Markov Chain.

To begin with, a classification standard of reassembly parts is proposed. With the thinking of traditional ABC analysis, a classification management method of reassembly parts for remanufactured engine is proposed. Then, a homogeneous Markov Chain of reassembly process is built after grading the matching dimension of reassembly parts with different variety. And the reassembly parts selection model is constructed based on the Markov Chain. Besides, the reassembly classification selection model and its flow chart are proposed by combining the researches above. Finally, the assembly process of remanufactured crankshaft is adopted as a representative example for illustrating the feasibility and the effectiveness of the method proposed.

The reassembly classification selection method based on the Markov Chain is an effective method in improving the utilization of remanufacturing parts/reused parts. The average utilization of remanufactured crankcase has increased from 35.7 to 80.1 per cent and the average utilization of reused crankcase has increased from 4.2 to 14 per cent as shown in the representative example.

The reassembly classification selection method based on the Markov Chain is of great importance in enhancing the economic benefit for remanufacturing enterprises by improving the utilization of remanufactured parts/reused parts.

The purpose of this study was to research how the reassembly (remanufacturing assembly) achieves a quality that is not lower than original production with different precision remanufactured parts based on the integration of mechanics, mathematics (measurement uncertainty) and management (optional classification). Remanufactured product quality is the soul of the remanufacturing project.

First, this paper studies the recycled parts features and reassembly features. Then, we build the mathematical sub-model with remanufactured parts and dimensional precision, which is proven that optional classification can effectively improve the reassembly accuracy mathematically. The optimization model of optional classification for reassembly is proposed under the constraint of a dimensional chain, and the solutions are studied based on particle swarm optimization. Finally, this method is applied in a remanufacturing enterprise and achieves good results.

The method can reduce the cost of quality loss and improve the quality of remanufactured products.

It provides a new solution and idea for reassembly with different precision remanufactured parts and promotes the healthy development of reverse logistics with a high level of customer satisfaction. This method can maximize the use of different levels of quality remanufactured parts and improve reassembly accuracy by mathematical proofs and examples.