Abstract:  In terms of the contemporary intelligent manufacturing (IM) and industrial Internet, three-dimensional printing (3DP) / additive manufacturing (AM) is definitely one of the most important key technologies because 3DP/AM can bring the disruptive innovation from high-level design to low-level manufacturing. Therefore, the academic topics about 3DP/AM attract more and more attention from many different kinds of scholars and institutes. While we retrospect the relevant publications on 3DP/AM during the past decades, we can find that most of the current research on 3DP/AM focuses on the huge innovation and market potential of 3DP/AM, and very few studies ever mentioned or touched the possible or potential risk of 3DP/AM. Actually, the main streams of studies on 3DP/AM just inform the very positive impact on economic systems, and very little is known about the possible/potential risk or threat to health, environment and ecology, security and social ethic and so on. Once 3DP/AM technologies would be popularized and industrialized, besides the tremendous pleasure for the emerging opportunities of economic development, the corresponding public-policies are also very important for the social-economic systems; otherwise, the emerging technologies could cause a new negative case of technology commercialization. However, the related studies and publications to the risk of 3DP/AM are still very few, and this exploration onthe risk of 3DP/AM are scattered in many different categories or academic areas, for example, chemistry, materials science, physics, biology and environment science and so forth. Obviously, because the risk signals on 3DP/AM are so weak and difficult to be identified and captured by those non-proficient policy-makers, a certain degree of information blockage and decision-making obstacles unintentionally derived, and the formulation of relevant policies on anticipatory governance has to face the difficulty of knowledge-acquisition. Further, the theoretical explorations and empirical analyses on the potential risk of emerging technologies are rare in the past decades, in particular the relevant quantitative studies on risk of a specific emerging technology almost keep blank; even could be a blind spot in the relevant areas on emerging technologies. To mitigate the gap between contemporary study on risk of emerging technology and concrete requirements of anticipatory governance and public policy making, a quantitative analytical method to identify the risk signal of a specific emerging technology is proposed, which can be attributed to a mining algorithm of cross-topic terms, and depends on the philosophy of nave machine learning. In the proposed framework of identifying the risk signals from the relevant publications on the observed emerging technology, the quantitative method can be divided into such different steps as: (a) the preparation of simple machine learning; (b) algorithm training based on a small sample; (c) the optimization of the relevant parameters. In each step, some concepts and computing equations are subsequently proposed. To verify the proposed identifying method for risk signals of emerging technology, the case study of 3DP/AM is conducted in empirical analysis. Relatively, the proposed method for identifying the risk of a specific emerging technology can provide moderate accuracy and recall ration, and significantly improve the efficiency of search the risk studies on a specific emerging to some degree. In terms of the empirical case on 3DP/AM, some important information is revealed and visualized that several significant risk or threat to human health and social security should be paid more attention in the contemporary stage of 3DP/AM development. First, several commercial polymer materials for 3DP in market present the biological toxicity that could make harmful impact on human health. Second, the ultrafine particles produced by 3DP/AM technologies not only bring the negative emission to the environment, but also bring the detrimental effect on human’s lung, in particular in the office space, and the harmful effect of ultrafine mental or polymer particles could cause unpredictable diseases. Third, the highly concentrated usage of power/electricity in 3DP/AM technologies could bring the huge impact on the stability of grid because the sintering steps in 3DP/AM rely on laser technology. In addition, 3DP/AM could aggravate the potential infringement of intellectual property because the technology of 3DP can make much easier copycats, and then could bring a big blow to those creative corporations and industries. Meanwhile, very few studies or researchers in social science can timely knew or responded to those risk signals of 3DP/AM, which could mean that the public concerns and public perceptions on 3DP/AM could be incomplete, i.e. the most of us just knew the huge potential for economic development about 3DP/AM, and very lack of the knowledge about the possible threats from 3DP/AM. In consequence, the relevant public-policies are highly possible time-lag, and even invalid while the health or security risk of 3DP/AM would become true events. China has the huge population, and very high density of population in some big cities; therefore, the potential or possible risk/threat of 3DP/AM or the other emerging technologies to human health, security and ethic should cause more attention from Chinese scholars, institutes and policy-makers because the loss in China or India could be much larger than the other small countries while those risk or threats of emerging technologies had really happened.However, the theoretical exploration and the empirical case on 3DP/AM also have limitations, for example, the mining method of trans-disciplinary topic terms has the room to enhance the accuracy, and whether the case study of 3DP/AM can be utilized by the other emerging technologies and so on. In addition, this paper just provides a new perspective to retrospect the prior studies on 3DP/AM, and does not imply the possible direction for the relevant researchers, i.e. how to encourage more scholars or institutes join the explorations on identifying risk signals for a specific emerging technology is not revealed by far.In view of the current scientific problems related to the identification and assessment of potential risks of emerging technologies, this paper proposes an interdisciplinary/trans-disciplinary topic word mining algorithm based on naive machine learning, and makes an empirical analysis with the current hot three-dimensional printing technology. Theoretical and empirical analysis show that the proposed method has a certain content recognition capability on the mining the potential risk signal for a specific emerging technology, which may have a certain value and positive significance for public governance decision-making and science and technology policy formulation for emerging technology.

Key words:  emerging technology, 3D printing, additive manufacturing, risk recognition, topic terms mining, machine learning