The batch synthesis of inorganic clusters can be both time consuming and limited by a lack of reproducibility. Flow-system approaches, now common in organic synthesis, have not been utilized widely for the synthesis of clusters. Herein we combine an automated flow process with multiple batch crystallizations for the screening and scale up of syntheses of polyoxometalates and manganese-based single-molecule magnets. Scale up of the synthesis of these architectures was achieved by programming a multiple-pump reactor system to vary reaction conditions sequentially, and thus explore a larger parameter space in a shorter time than conventionally possible. Also, the potential for using the array as a discovery tool is demonstrated. Successful conditions for product isolation were identified easily from the array of reactions, and a direct route to 'scale up' was then immediately available simply by continuous application of these flow conditions. In all cases, large quantities of phase-pure material were obtained and the time taken for the discovery, repetition and scale up decreased.

The concept of smart manufacturing has become an important issue in the manufacturing industry since the start of the twenty-first century in terms of time and production cost. In addition to high production quality, a quick response could determine the success or failure of many companies and factories. One the most effective concepts for achieving a smart manufacturing industry is the use of computer-aided process planning (CAPP) techniques. Computer-aided process planning refers to key technology that connects the computer-aided design (CAD) and the computer-aided manufacturing (CAM) processes. Researchers have used many approaches as an interface between CAD and CAPP systems. In this field of research, a lot of effort has been spent to take CAPP systems to the next level in the form of automatic computer-aided process planning (ACAPP). This is to provide complete information about the product, in a way that is automated, fast, and accurate. Moreover, automatic feature recognition (AFR) techniques are considered one of the most important tasks to create an ACAPP system. This article presents a comprehensive survey about two main aspects: the degree of automation in each required input and expected output of computer-aided process planning systems as well as the benefits and the limitations of the different automatic feature recognition techniques. The aim is to demonstrate the missing aspects in smart ACAPP generation, the limitations of current systems in recognising new features, and justifying the process of selection.

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